Note: Descriptions are shown in the official language in which they were submitted.
CA 2964243 2017-04-12
STRESS ENGINEERING ASSESSMENT OF RISERS and
RISER STRINGS
TECHNICAL FIELD
[0001] The invention is an autonomous system approach to risk management
through
continuous riser stress-engineering-assessment. The system/method verifies the
integrity of a ris-
er joint and the in-deployment-integrity of a riser string by knowing the
status, details and loca-
tion of each riser joint and by monitoring the deployment parameters. When the
failure risk ex-
ceeds an acceptable level, riser stress-engineering-assessment equipment
activates at least one
alarm using voice, sound and lights.
[0002] BACKGROUND OF THE INVENTION
[0003] Components are made from materials and are typically assembled to
sub-
systems which in turn are assembled to complex systems. Complex systems are
assembled using
processes and often they function within the envelop of a process. As is known
in the art, materi-
als are selected for use based on criteria including minimum strength
requirements, useable life
and anticipated normal wear. The list of typical materials and systems
includes, but is not limited
to, aircraft, beam, bridge, blowout preventer, BOP, boiler, cable, casing,
chain, chiller, coiled
tubing (herein after referred to as "CT"), chemical plant, column, composite,
compressor, cou-
pling, crane, drill pipe (herein after referred to as "DP"), drilling rig,
enclosure, engine, fastener,
flywheel, frame, gear, gear box, generator, girder, helicopter, hose, marine
drilling and produc-
tion Riser (herein after referred to as "Riser"), metal goods, oil country
tubular goods (herein
after referred to as "OCTG"), pipeline, piston, power plant, propeller, pump,
rail, refinery, rod,
rolling stoke, sea going vessel, service rig, storage tank, structure, sucker
rod (herein after re-
ferred to as "SR"), tensioner, train, transmission, trusses, tubing, turbine,
vehicle, vessel, wheel,
workover rig, components of the above, combinations of the above, and similar
items, (herein
after referred to as "Material-Under-Assessment" or "MUA"). "MUA of interest"
is also referred
to as "MUA".
[0004] During its useful life, MUA deteriorates and/or is weakened
and/or is deformed
by external events such as mechanical and/or chemical actions arising from the
type of applica-
1
CA 2964243 2017-04-12
tion, environment, repeated usage, handling, hurricanes, earthquakes, ocean
currents, pressure,
waves, storage, temperature, transportation, and the like; thus, raising
safety, operational, func-
tionality, and serviceability issues. A non-limiting list of the loads the MUA
may endure during
its life involves one or more of bending, buckling, compression, cyclic
loading, deflection, de-
formation, dynamic linking, dynamic loading, eccentricity, eccentric loading,
elastic defor-
mation, energy absorption, feature growth, feature morphology migration,
feature propagation,
flexing, heave, impulse, loading, misalignment, moments, offset, oscillation,
plastic deformation,
propagation, pulsation, pulsating load, shear, static loading, strain, stress,
tension, theimal load-
ing, torsion, twisting, vibration, analytical components of the above,
relative components of the
above, linear combinations thereof, non-linear combinations thereof and
similar items, (herein
after referred to as "Loads").
[0005] Marine drilling risers, catenary risers, flexible risers and
production risers are
hereinafter referred to as "Riser". Risers provide a conduit for the transfer
of materials, such as
drilling and production fluids and gases, to and from the seafloor equipment,
such as a Blowout
Preventer, hereinafter referred to as "BOP", to the surface floating platform.
[0006] Multi-tubulars comprise tubular arrangement of multiple tubes
running in parallel.
Risers are multi-tubulars along with umbelicals. However, umbelicals may be
analyzed as one
tube whereas the main tube of the riser is the main load bearing structure.
[0007] A Riser joint may comprise of a single or more typically multiple
pipes in paral-
lel that are selected for use based on minimum material strength requirements.
Each Riser joint is
designed to withstand a range of operation loads, hereinafter after referred
to as "Loads". A fail-
ure occurs when the stresses due to the deployment Loads exceed the actual
Riser strength. It is
reasonable therefore to expect that the applicable Standards and Recommended
Practices would
discuss and set allowable stresses limits and/or maximum allowable Loads.
[0008] REFERENCES
[0009] American Petroleum Institute (API) RP 16Q: Recommended Practice
for Design,
Selection, Operation and Maintenance of Marine Drilling Riser Systems
[0010] API Specification 16F: Specification for Marine Drilling Riser
Equipment
2
CA 2964243 2017-04-12
[0011] American Society of Mechanical Engineers (ASME) B31.4
[0012] API 579-1/ASME FFS-1: Fitness-for-Service
[0013] Det Norske Veritas (DNV): DNV-0S-F201 Offshore Standards
[0014] DNV- F206: Riser Integrity Management
[0015] DNV-OSS-302: Offshore Riser Systems
[0016] DNV-RP-G103: Non-Intrusive Inspection
[0017] American Bureau of Shipping (ABS): Guide for the Certification of
Drilling Sys-
tems
[0018] ABS: Guide for Building and Classing Subsea Riser Systems
[0019] Atlantic Margin Joint Industry Group (AMJIG): Deep Water Drilling
Riser Integ-
rity Management Guidelines.
[0020] Theory of Elasticity S. P. Timoshenko, J. N. Goodier
[0021] ROARK'S Formulas for Stress and Strain
[0022] Pertersen Stress Concentration Factors
[0023]
[0024] REVIEW OF STANDARDS AND RECOMMENDED PRACTICES
[0025] API RP 16Q Section 3: RISER RESPONSE ANALYSIS "This section
applies
equally to the design of a new riser system or the site specific evaluation of
an existing riser sys-
tem. Riser analysis should be performed for a range of environmental and
operational parame-
ters."
[0026] API RP 16Q Table 3.1: Lists maximum operating and design stresses
factors
and "[3] All stresses are calculated according to von Mises stress failure
criterion".
[0027] API 16F Section 5.4: "The analysis shall provide peak stresses and
shall in-
clude effects of wear, corrosion, friction and manufacturing tolerances" 3.74
Stress Amplifica-
tion Factor (SCF): "The factor is used to account for the increase in the
stresses caused by geo-
metric stress amplifiers that occur in riser components".
3
CA 2964243 2017-04-12
[0028] ASME B31.4 402 Calculation of stresses: "Circumferential,
longitudinal, shear,
and equivalent stresses shall be considered..." "Calculations shall take into
account stress inten-
sification factors..." Table 402.1-1 lists "stress intensification factors".
[0029] ABS 9.1: "The riser is to be so designed that the maximum stress
intensity for
the operating modes, as described in API RP 16Q, is not exceeded"
[0030] AMJIG A.1.2: "Assessment of pipe strength is based on the von
Mises com-
bined stress criterion" A1.2.1 Riser Stresses: "API-RP-16Q recommends a
maximum allowable
stress factor for drilling operations of 0.67".
[0031] DNV-RP-F204: Riser Fatigue Appendix A.
[0032] DNV-F206 10.2.2: Condition Based Maintenance "This maintenance
strategy
can be used when it is possible to observe some kind of equipment
degradation".
[0033] DNV-OSS-302: API RP 16Q is applicable. 108: "Establishment of
compo-
nents strength in terms of maximum applicable external loads / deformations"
[0034] API 579-1 / ASME FFS-1 G.1.2. "When conducting a FFS assessment it
is very
important to determine the cause(s) of the damage or deterioration".
[0035] REVIEW OF NON-DESTRUCTIVE-INSPECTION
[0036] The concepts of modern Non-Destructive-Inspection (hereinafter
referred to as
"NDI") were established in the 1920s. Modern day NDI units often use a similar
design concept
as the US Patent 1,823,810 and the exact same sensors and configuration as
found in US Patent
2,685,672 Figs. 5 and 6. The vacuum tube amplifier of US Patent 1,823,810 is
replaced with a
solid-state amplifier and the readout meter is replaced by a computer with a
colorful display. A
few have replaced the coil sensors of US Patent 2,685,672 Figs. 5 and 6 with
Hall probes. None
of this repackaging has improved the overall capabilities of modern NDI as the
US Patent
2,685,672 single sensor per area comingles all imperfection signals into one
signal resulting in
what may be called a one-dimensional NDI, herein referred to as "1D-NDI".
Notice that the 1D-
NDI classification also applies to eddy-current, radiation, ultrasonic,
similar systems and combi-
nations thereof. Some combine different 1D-NDI techniques in-line resulting in
a system with
4
CA 2964243 2017-04-12
two or more 1D inspection signals that are not related in form, kind, space
and time and thus,
they cannot be used to solve a system of equations.
[0037] The 1D-NDI signal is insufficient to solve the system of equations
to "determine
the cause(s) of the damage or deterioration" per API 579-1/ASME FFS-1 and to
identify the
"geometric stress amplifiers that occur in riser components" per API 16F.
Therefore, and as op-
posed to RiserSEA as discussed hereinafter, 1D-NDI data is unrelated to the as-
is Riser strength,
fitness-for-service (herein referred to as "FFS") and remaining-useful-life
(herein referred to as
"RUL") other than an occasional end-of-life statement.
[0038] It should be expected that the Lack-of-Knowledge about the MUA
Features re-
sults in "false indications" or "false calls" whereby the 1D-NDI signal (1D-
NDI flag) is not asso-
ciated with any Feature, resulting in wasted verification crew man-hours and
reduced productivi-
ty. In order to improve productivity, 1D-NDI employs threshold(s) to eliminate
the material sig-
nature, the low amplitude signals that are commonly referred to as "grass".
Fatigue gives rise to
low amplitude signals and therefore, fatigue signals are eliminated from the
1D-NDI traces as a
standard procedure. For example, 1D-NDI equipment that is configured to comply
with T.H. Hill
DS-1, will never detect drill pipe fatigue build-up regardless of how often
drill pipe undergoes
DS-1 type of inspection.
[0039] The "false calls" in US Patent 6,594,591 is the result of 1D-
NDI "not
knowing by any detail" the MUA Feature, not even knowing if the signal
corresponds to a Fea-
ture much less been capable of "connecting or associating the feature with
known definitions"
that allow the calculation of an FFS and/or RULE. US Patent Application
2004/0225474 de-
scribes the same problem in [0004] "A significant impediment to NDE
inspections in the field
(as opposed to depot) and to onboard diagnostics and prognostics is the
potential for excessive
false indications that directly impact readiness". In other words, 1D-NDI
cannot be deployed in
the field or onboard an aircraft because of the excessive number of 1D-NDI
"false indications"
requiring the human intervention of at least one verification crew.
[0040] It should be understood that all the means and methods
improvised to re-
duce the 1D-NDI "false indications" or "false calls" are simply band aids to
the underline prob-
lem of insufficient number of sensors and signal processing to solve the
multidimensional MUA
CA 2964243 2017-04-12
problem (the system of equations) of detecting, identifying and recognizing
MUA Features and
calculating an FFS and/or a RULE as the present invention does.
[0041] Furthermore, today's NDI standards, like the drill pipe DS-1,
discuss Fatigue ex-
tensively and then specify an 1D-NDI unit setup that eliminates any Fatigue
signals through
thresholds to improve the "signal to noise ratio", just like in the 1920s US
Patent 1,823,810 vari-
able grid bias. However, the "noise" also contains metallurgy and Fatigue
signals in addition to
the sensor ride chatter. Therefore, modern day repackaging of the 1920s 1D-NDI
means and
methods did not improve the overall 1D-NDI performance. Because of the signal
comingling and
the limited dynamic range, 1D-NDI cannot detect many of the dangerous
imperfections early on,
such as fatigue, and has a limited operational range for pipe size,
configuration, wall thickness,
types of imperfections, inspection speed, sampling rate and similar items
while it still relies on
the manual intervention of a verification-crew to locate and identify the
source of the 1D-NDI
signal. As opposed to the RiserSEA affirmative verification of the as-is Riser
status, 1D-NDI
verifies that it did not detect the few late-life defects within its
capabilities.
[0042] As opposed to inspection, Assessment is an affirmative process
that relies on a
sufficient number of good quality specific data to judge and confuni. FFS and
RULE are the re-
sults of an Assessment.
[0043] It would then be the responsibility of whoever performs the
Assessment to
define the good quality inspection(s), scope and techniques including the
number and type of
specific data to facilitate the Assessment. Inspection therefore is a very
small part of an Assess-
ment process and it is well defined only when it is part of an Assessment
process. Inspection is
not a substitute for an Assessment. Many disasters root-cause can be traced to
this misunder-
standing alone; where inspection, such as 1D-NDI, is used as a substitute for
Assessment.
[0044] It should further be understood that Assessment preferably
examines and
evaluates, as close as possible, 100% of the MUA for 100% of Features and
declare the MUA fit
for service only after the Features impact upon the MUA have been evaluated
under specific
knowledge and rules that include, but not limited, to the definition of the
deployment "service"
or "purpose". Inspection, such as 1D-NDI, inherently cannot fulfill that role.
Marine Drilling
Risers are an example of the difference between Assessment and inspection.
6
CA 2964243 2017-04-12
[0045] Risers connect the drillship to the seafloor BOP and
therefore are a very
critical component of the offshore drilling operation. Based on the API RP-579
Fitness-For-
Service recommendations, the Riser Assessment of the main tube alone should be
based on about
30,000 Wall-Thickness readings. From the commercial literature, the Riser
inspection of US Pa-
tent No. 6,904,818 acquires about 180 Wall-Thickness readings and yet, it does
fulfill the "annu-
al inspection" letter of the Law although more than 99% of the Riser condition
is still unknown
after this inspection.
[0046] Although API RP-579 lists some of the MUA specific data
required to fa-
cilitate an Assessment it fails to provide means to obtaining the MUA specific
data that lead to
an Assessment as it only focuses on how difficult it is obtain such data
(sufficient number of
good quality data) with 1D-NDI. Attaining detailed MUA condition knowledge and
the associat-
ed specific data through manual means is prohibitive both financially and time
wise as it in-
volves the employment of a number of multidiscipline experts, laboratories and
equipment.
[0047] It is desirable therefore to provide to the industry
automatic means and
methods to facilitate an MUA condition based maintenance program through an
Assessment and
preferably, through frequent Assessments to facilitate a constant-vigilance
maintenance program,
especially for high-reliability safety-critical equipment, systems and
processes with minimum
amount of human intervention.
[0048]
[0049] RISER 1D-NDI ANALYSIS
100501 Riser pipes fall well outside the inspection capabilities of 1D-
NDI. Furthermore,
the primary concern of the Riser manufacturers (herein referred to as "Riser-
OEM") is to verify
the compliance of the new pipes from the pipe mill with the purchase order
prior to assembling
them into a new Riser. A limited manual 1D-NDI sampling (herein referred to as
"Spot-Checks")
is sufficient to verify compliance. The Riser-OEM Spot-Checks comprises of a
number of manu-
al spot readings that typically cover less than 1% of the pipe, again, due to
the limitations of the
available 1D-NDI technology. However, this Riser-OEM Spot-Checks is inadequate
and inap-
propriate for the inspection of used Riser where 100% inspection coverage is
essential for the
calculation of the maximum (peak) Riser stresses. It should also be noted that
Riser-OEM Spot-
7
CA 2964243 2017-04-12
Checks is inadequate and inappropriate for the inspection of all other new or
used Oil-Country-
Tubular-Goods, hereinafter after referred to as "OCTG", like drill pipe.
[0051] The Riser-OEM Spot-Checks comprise of one or more of: a) a few
ultrasonic
(UT) readings around the pipe circumference, typically 4 readings spaced 2 to
5 feet apart, prov-
ing less than 0.1% inspection coverage for wall thickness only; b) a limited
eddy-current inspec-
tion (EC) of the ID surface that also provides less than 0.1% inspection
coverage for near-surface
imperfections only; c) TOFD of welds that may only detect mid-wall
imperfections with two dif-
fracting ends. The mid-wall imperfections must be away from the TOFD two
inspection dead-
zones (the near-surface dead-zone due to lateral waves and the far-surface
dead-zone due to ech-
oes); d) mag-particle inspection (MPI) of the welds that is limited to surface
and near-surface
imperfections on the OD only, after the buoyancy and the paint or coating are
removed; e) visual
inspection and 0 a few dimensional readings. Again, this Riser-OEM Spot-Checks
may be ade-
quate to verify the compliance of new pipe with the purchase order; however,
it is inadequate for
the inspection of used Risers as it leaves over 99% of the Riser condition
unknown, a serious
safety hazard.
[0052] Due to the limitations of 1D-NDI to provide 100% inspection
coverage on Riser
pipes, certified and monitored inspection companies that specialize in the
inspection of new and
used OCTG, such as the inspection of drill pipe, production tubing etc., are
not involved with the
inspection of Risers. This leaves the Riser-OEMs as the only vendors of used
Riser inspection.
Lacking any other means and used OCTG inspection expertise, Riser-OEMs utilize
the same
Spot-Checks to inspect used Risers leaving 99% of the Riser condition unknown
after the inspec-
tion. The simplicity of the spot checks, the modest investment in tools and
the lack of required
certification and monitoring has encouraged many to enter the used Riser
inspection market.
[0053] Furthermore, and in order to perform the spot-checks, Riser-OEMs
and others re-
quire the used Riser to be shipped to one of their facilities onshore. In
summary, this involves: a)
loading the Riser to a workboat; b) unloading the Riser from the workboat onto
a flatbed truck;
c) transporting and unloading the Riser at the inspection facility; d)
disassembling, removing
paint/coating and cleaning the Riser; e) performing the spot-check 1D-NDI; 0
recoat-
ing/repainting and reassembling the Riser with 99% of its condition still
unknown and g) ship-
ping the Riser back to the rig. Although the Riser is exposed to a high
probability of transporta-
8
CA 2964243 2017-04-12
tion and handling damage including but not limited to disassembly and
reassembly errors and
omissions, this entire process does not produce sufficient data to verify the
used Riser integrity
or for the calculation of the maximum (peak) Riser stresses. A careful study
may conclude that
this process is more harmful than helpful because, among many more, it also a)
produces a sig-
nificant amount of air and water contaminant from the transportation, sand-
blasting and pressure-
washing of the Riser pipes and b) gives the false sense of security to the rig
crew that otherwise
may be more vigilant during the deployment or retrieval of the Riser.
[0054] It should be noted that for decades drill pipe and other used OCTG
inspection
mandates 100% inspection coverage by certified and monitored inspection
companies using cali-
brated equipment. Again, Riser-OEM spot-checks do not meet the new or used
drill pipe and
other OCTG minimum inspection requirements. In offshore drilling, drill pipe
is deployed inside
the Riser Main Tube along with the drilling and well fluids. The irony of it
all is that if the drill
pipe breaks it would result in an inconvenience as the Riser will protect the
environment and lim-
it any harmful consequences. If the Riser breaks, drilling and well fluids and
gases would be re-
leased immediately to the environment with limited means to control the damage
and the pollu-
tion. It should also be noted that gases may reach the surface underneath or
very near the floating
platform and may ignite, a familiar Gulf-of-Mexico scenario. In other words,
100% inspection
coverage by a certified and monitored company is specified to prevent an
inconvenience while
1% or less inspection coverage by anybody is deemed adequate to prevent a
disaster.
[0055] RISER ANALYSIS
[0056] Due to lack of 1D-NDI useful data, Riser analysis is still carried
out using ideal
Riser material assumptions such as: a) the material is assumed to be Linearly
Elastic; b) the ma-
terial is assumed to be Homogeneous (having the same material properties at
all points); c) the
material is assumed to be Isotropic (having the same properties at all
directions); d) the cross-
sectional-area (herein referred to as "CSA") of the material is Circular
throughout its Length; e)
the CSA is constant throughout its Length and f) the Riser is straight. These
assumptions simpli-
fy the Riser analysis while it is further assumed that any unknowns, errors
and omissions are
covered when the calculated Riser maximum stresses do not exceed, for example,
0.67 of the
material specified minimum yield strength. This assumption may be allowable
for normal operat-
ing conditions. However, under abnormal, contingency, extreme, emergency and
survival condi-
9
CA 2964243 2017-04-12
tions the knowledge of the actual strength of the weakest riser joint in the
string becomes the key
to survival, not an assumed value of an ideal material that is never present
in a string.
[0057] Furthermore, the greater water depths are now overshadowing the
ideal Riser ma-
terial assumptions. This is equivalent to high altitude mountain climbing
whereby the lack of ox-
ygen at or above the death-zone overshadows the skills, endurance and
determination of the
climber. However, as opposed to the mountain climbing fixed death-zone
altitude, the Riser
death-zone depends on the condition of each Riser joint. For example, quoting
from API 16F
"3.74 Stress Amplification Factor (SAF): The factor is used to account for the
increase in the
stresses caused by geometric stress amplifiers that occur in riser
components". Geometric stress
amplifiers: a) are never present in ideal material; b) they are not the same
from Riser joint to Ris-
er joint; c) can only be determined from NDI data that cover 100% of the
volume of the Riser
joint and d) is capable of "determining the cause(s) of the damage or
deterioration" per API 579-
1/ASME FFS-1.
[0058] Therefore, there is an offshore drilling industry need for an
automated system to
calculate maximum Riser stresses during deployment using deployment data along
with Riser
material and geometry data, including the effects of geometric stress
amplifiers, and to compare
said stresses to failure-criteria to determine if the Riser string is still
fit-for-deployment per API
16Q, API 16F, DNV, ABS and all other specifications and requirements.
[0059] SUMMARY OF THE INVENTION
[0060] It is reasonable to conclude from the aforementioned that the
purpose of the Riser
inspection is to acquire a sufficient number of good quality specific data to
facilitate a Riser re-
sponse Analysis that includes, but is not limited, to a calculation of maximum
Riser stresses to
verify that they do not exceed the allowable stresses under Loading,
preferably using the von
Mises stress failure criterion. The Analysis should include, but is not
limited to, the effects of
corrosion, crack-like-flaws, fatigue, geometric-distortion, groove-like-flaws,
hardness, local wall
thickness misalignment, pit-like-flaws, wall thickness, wear, and other stress-
concentrators (ge-
ometric stress amplifiers), herein referred to as "Imperfections".
Imperfections that exceed an
alert threshold are herein referred to as "Flaws". Imperfections that exceed
an alarm threshold
are herein referred to as "Defects".
CA 2964243 2017-04-12
[0061] As opposed to Riser codes, standards and 1D-NDI, computers and
finite element
analysis software, herein referred to as "FEA", have made great strides
widening the gap be-
tween Riser Analysis and Riser Inspection.
[0062] Furthermore, a condition based maintenance is preferable when the
Riser inspec-
tion can detect a spectrum of degradation (DNV-F206) and determine the causes
of degradation
(API 579-1 / ASME FFS-1). Therefore, RiserSEA should detect and recognize a
spectrum of
Imperfections and analyze their combined effects on the Riser under loading.
It should then be
understood that RiserSEA analysis results in an affirmative verification that
the as-is Riser ex-
ceeds a minimum strength requirement or should be rerated or should be
repaired or should be
removed from service.
[0063] In one possible embodiment, RiserSEA comprises an Autonomous
Constant-
Vigilance (herein after referred to as "AutoCV") system or elements thereof
may be provided to
ascertain and/or to mitigate hazards arising from the failure of an MUA
resulting from misappli-
cation and/or deterioration of the MUA. The AutoCV system may comprise
elements such as, for
instance, a computer and an MUA Features acquisition system. The MUA Features
acquisition
system may be used to scan the MUA and identify the nature and/or
characteristics of MUA Fea-
tures. A computer program may evaluate the impact of the MUA Features upon the
MUA by op-
erating on the MUA Features, said operation guided by a database constraints
selected at least in
part from knowledge and/or rules and/or equations and/or MUA historical data.
The AutoCV
system may acquire Loads and Deployment Parameters by further comprising of a
data acquisi-
tion system. A computer program may evaluate the impact of the Loads and
Deployment Pa-
rameters upon the MUA by operating on the MUA Features, said operation guided
by a database
constraints selected at least in part from knowledge and/or equations and/or
rules. A computer
program may convert the MUA data to a data format for use by a Finite Element
Analysis pro-
p-am (herein after referred to as "FEA"), also known as an FEA engine, or a
Computer Aided
Design program (herein after referred to as "CAD"),
[0064] The computer program may further combine the as-is MUA components
into a
functional (operational) MUA model, such as a structure, an engine, a pump or
a BOP. The com-
puter may further recalculate the physical shape of each as-is MUA component
using Features,
Loads, Deployment Parameters, constraints, equations, rules and knowledge and
may then oper-
11
CA 2964243 2017-04-12
ate the MUA model to verify that the MUA is still functional as intended
within a safe opera-
tional-envelop and in an emergency, guide the crew on the limits of exceeding
the safe opera-
tional-envelop.
[0065] The computer program may further combine as-is MUA models to
assess the
functionality of a complex system, such as the as-is drill pipe inside the as-
is Riser and the as-is
subsea BOP. Such a simulation will also take into account the as-is drill
pipe, Riser and BOP
including, but not limited to, as-is shape, wall thickness, hardness,
hydraulic pressure and tem-
perature and other pertinent Features, Loads and Deployment Parameters.
[0066] These and other embodiments, objectives, features, and advantages
of the present
invention will become apparent from the drawings, the descriptions given
herein, and the ap-
pended claims. However, it will be understood that above-listed embodiments
and/ or objectives
and/or advantages of the invention are intended only as an aid in quickly
understanding certain
possible aspects of the invention, are not intended to limit the invention in
any way, and there-
fore do not form a comprehensive or restrictive list of embodiments,
objectives, features, and/or
advantages.
[0067] BRIEF DESCRIPTION OF DRAWINGS
[0068] FIG. 1 illustrates a block diagram of an example of an AutoCV
system, of which
RiserSea may be a component, deployed with an offshore drilling rig in accord
with one possible
embodiment of the present invention;
[0069] FIG. 2 illustrates a block diagram of an example a surface AutoCV
system de-
ployed at the rig floor of an offshore drilling rig in accord with one
possible embodiment of the
present invention;
[0070] FIG. 3A illustrates an example of a Two-Dimensional (2D)
Extraction Matrix in
accord with one possible embodiment of the present invention;
[0071] FIG. 3B illustrates an example of a Identifier Equations in accord
with one possi-
ble embodiment of the present invention;
12
CA 2964243 2017-04-12
[0072] FIG. 3C illustrates an example of a Three-Dimensional (3D) Stress
Concentration
graph for use in a stress concentration factors calculation in accord with one
possible embodi-
ment of the present invention;
[0073] FIG. 4 illustrates an example of Critically-Flawed-Path on a tube
showing related
measurements and related critically flawed areas in accord with one possible
embodiment of the
present invention.
[0074] Fig. 5A is an elevational view of a floating drilling rig with a
deployed riser con-
necting to a subsea BOP;
[0075] Fig. 5B is an elevational view of a floating drilling rig of risers
such as those as
indicated in FIG. lA that do not include buoyancy jackets;
[0076] Fig. 5C is an elevational view of a floating drilling rig of risers
such as those as
indicated in FIG. lA that do include buoyancy jackets;
[0077] Fig. 6A is an end view of a possible marine drilling riser
coupling;
[0078] Fig. 6B is a view of risers in a shipyard prior to deployment;
[0079] Fig. 7 is a RiserSEA and/or component of AutoCV block diagram in
accord with
one embodiment of the present invention;
[0080] Fig. 8 is an illustration of an addressable sensor array in accord
with one embod-
iment of the present invention;
[0081] Fig. 9A is an example of a Riser Fitness Certificate;
[0082] Fig. 9B is an example of signals produced in accordance with
RiserSEA in accord
with one possible embodiment of the present invention;
[0083] Fig. 10 is an example of an export to FEA analysis of pipes,
risers, umbelicals,
and the like in accord with one possible embodiment of the present invention.
[0084]
[0085] DESCRIPTION OF EMBODIMENTS OF THE PRESENT INVENTION
[0086]
13
CA 2964243 2017-04-12
[0087] To understand the tern's associated with the present invention,
the following de-
scriptions are set out herein below. It should be appreciated that mere
changes in terminology
cannot render such terms as being outside the scope of the present invention.
Details of the
terms and systems for providing these functions are also discussed in
respective of our previous
patents which are referenced herein.
[0088] Autonomous: able to perform a function without external control or
intervention,
which however may be initiated and/or switched off and/or verbally interacted
with and/or visu-
ally interacted with and/or auditorily interacted with and/or revised and/or
modified as desired by
external control or intervention.
[0089] AutoNDI: Autonomous Non-Destructive Inspection
[0090] AutoFFS: Autonomous Fitness-For-Service
[0091] AutoFFSE: Autonomous Fitness-For-Service-Estimation
[0092] AutoRULE: Autonomous Remaining-Useful-Life-Estimation
[0093] AutoCV: Autonomous Constant-Vigilance Assessment method and
equipment
carried-out, at least in part, by the exemplary STYLWAN Rig Data Integration
System (RDIS-
10) and incorporating herein by reference in their entirety the following: US
Patent Application
Serial Number 13/304,061, US Patent Application Serial Number 13/304,136, US
Patent No
8,086,425, US Patent No 8,050,874, US Patent No 7,403,871, US Patent No
7,231,320, US Pa-
tent No 7,155,369, US Patent No 7,240,010, and any other patents/applications
. In the prior art,
FFS and RULE was typically performed by an expert or a group of experts using
as-designed
data and assumptions while the AutoCV assessment is based primarily on as-
built or as-is data.
When design data is available, AutoCV also monitors compliance with the design
data. When
less than optimal data is available, AutoCV may perform a Fitness-For-Service-
Screening (Here-
in after referred to as "FFSS"). RiserSea may be a
[0094] Degradation Mechanism: the phenomenon that is harmful to the
material. Degra-
dation is typically cumulative and irreversible such as fatigue built-up.
[0095] Essential: important, absolutely necessary.
[0096] Expert: someone who is skillful and well informed in a particular
field.
14
CA 2964243 2017-04-12
[0097] Feature: a property, attribute or characteristic that sets
something apart.
[0098] Finite Element Analysis (Herein after referred to as "FEA"): a
method to solve
the partial or ordinary differential equations that guide physical systems.
[0099] FEA Engine: is an FEA computer program, a number of which are
commercially
available such as Algor and Nastran. In practice, FEA engines are used to
analyze structures un-
der different loads and/or conditions, such as a Riser under tension and
enduring vortex induced
vibration (Herein after referred to as "VIV"). An FEA engine may analyze a
structure with a fea-
ture under static and/or dynamic loading, but not a feature on its own.
[00100] Fitness For Service: typically an engineering Assessment to
establish the integrity
of in service material, which may or may not contain an imperfection, to
ensure the continuous
economic use of the material, to optimize maintenance intervals and to provide
meaningful re-
maining useful life predictions.
[00101] Imperfection: one of the material features - a discontinuity,
irregularity, anomaly,
inhomogeneity, or a rupture in the material under Assessment. Imperfections
are undesirable and
often arise due to fabrication non-compliance with the design, transportation
mishaps and MUA
degradation. A Flaw is an Imperfection that exceeds an alert-threshold when
monitored in accord
with an embodiment of the present invention and typically places the MUA in
the category of
requiring in-service monitoring. A Defect is an Imperfection that exceeds an
alarm-threshold for
reliable use when monitored in accord with an embodiment of the present
invention and may re-
quire removal from service, repair, remediation, different use and/or the
like.
[00102] Knowledge: a collection of facts and rules capturing the knowledge
of one or
more specialist and/or experts.
[00103] Operational Envelop: the context of the conditions under which it
is safe to use.
[00104] Remaining Useful Life: a measure that combines the material
condition and the
failure risk the material owner is willing to accept. The time period or the
number of cycles ma-
terial (a structure) is expected to be available for reliable use.
[00105] Remaining Useful Life Estimation: establishes in one possible
embodiment the
next monitoring interval or the need for remediation but it is not intended to
establish the exact
CA 2964243 2017-04-12
time of a failure. When Remaining Useful Life can be established with
reasonable certainty, the
next monitoring interval may also be established with reasonable certainty.
When Remaining
Useful Life cannot be established with reasonable certainty, then RULE may
establish the reme-
diation method and upon completion of the remediation, the next monitoring
interval may be es-
tablished. When end of useful life is established with reasonable certainty,
alteration and/or re-
pair and/or replacement may be delayed under continuous monitoring.
[00106] Rules: how something should be done or not be done concerning MUA
based up-
on know and/or detected facts.
[00107] Assessment of equipment, systems and processes
[00108] Referring now to the drawings, Fig. 1 illustrates an offshore
drilling rig 1. The
offshore drilling rig 1 was selected as an example for a Constant-Vigilance
application because it
encompasses a large variety of materials, some safety-critical, deployed under
extreme condi-
tions. In this example, Constant-Vigilance monitors the drilling process
through a number of dis-
tributed AutoCV systems in continuous communication with each other and each
specifically
configured for its assignment. However, the present invention is not limited
to this particular ap-
plication and may also be implemented in previously discussed and/or alluded
to applications
and/or other applications.
[00109] It should be understood that complex equipment, systems and
processes, safety-
critical or otherwise, are coupled closely and their interaction(s) is very
complex. Even small
changes may form a chain that may propagate through the system, amplify and
may trigger a
failure that cannot be predicted readily by a cursory look. Furthermore,
equipment, systems and
processes, especially safety-critical, preferably must exhibit high-
reliability and fault-tolerance,
whereby some operational capacity is still available after a failure.
[00110] Assessment of equipment, systems and processes, especially safety-
critical, ac-
cording to the present invention, preferably starts from the top and defines
and prioritizes the key
requirements of the operational-envelop and the risks associated with the
failure-paths. It is a
unique feature of one possible embodiment of the present invention that
whoever performs the
Assessment must examine and include in the MUA historical data a list of
Loads, Deployment
Parameters, Environment, Risk and Failure-chains to specifically exclude from
list parts that do
16
CA 2964243 2017-04-12
not belong in the Operational-Envelop of the MUA deployment. Then, the
characteristics and
values of the remaining Loads, Deployment Parameters, Environment, Risk and
Failure-chains
should be defined like chemistry, cyclic, magnitude, maximum, minimum, peak,
phase, probabil-
ity, pulsating, range, span, steady, units of measurement, combinations of the
above and similar
items. This list guides/reminds/helps whoever performs the Assessment or a
follow-up Assess-
ment to judge and confirm and to seek knowledge, search, ask for help or
obtain an expert opin-
ion(s) from the start of the Assessment process.
1001111 For example, such a list would have guided/reminded the HMAN
Westralia crew
that the fuel hoses do not only endure static pressure, but they also endure
vibration (attached to
a diesel engine), pulsating pressure (attached to a pump) and the other Loads,
Deployment Pa-
rameters and Environment a sea going vessel encounters. A cursory search of
the engine manuals
and the manufacturer's bulletins could have averted this disaster as the
pulsating pressure peak
value was extensively discussed and is considered general knowledge among
marine engineers
and others.
[00112] Assessment then progresses downwards and splits the system into
sub-systems
and eventually components. For each sub-system and component, Assessment
defines and priori-
tizes the key requirements of its operational-envelop and the risks associated
with its failure-
paths as aforementioned. It should be understood that the failure-paths of sub-
systems and com-
ponents may define additional requirements and/or may reformulate the risk
associated with the
overall system whereby restarting the Assessment from the top again
(Assessment feedback).
Assessment therefore knows by some detail the risks associated with each sub-
system and com-
ponent and then specifies the good quality inspection(s), scope and techniques
including the
number and type of specific data to facilitate the Assessment and to
preferably disrupt the acci-
dent-chain(s).
[00113] The most effective way to manage complex equipment, systems and
processes is
to translate them, when possible, to a mathematical description that
simplifies the detection and
assesses subtle changes that people and organizations would miss with a
cursory look thus warns
about errors and contains failures by actively disrupting the failure-chains
with knowledge. Oc-
casionally, humans tend to misinterpret, misunderstand, simplify and dismiss
subtle readings and
changes, such as the pressure readings on the Deepwater Horizon. On the other
hand, AutoCV
17
CA 2964243 2017-04-12
mathematical description allows for higher-resolution Assessment, allows for
overall system As-
sessment and it will not simplify or dismiss subtle changes.
[00114] Autonomous Constant-Vigilance System
[00115] The exploration, production, transportation and processing of
hydrocarbons, on-
shore or offshore, utilizes substantially similar equipment and configuration
of equipment. For
example, a metallic or composite cylinder (with or without end connectors
and/or welds) may be
referred to as casing, coiled tubing, drill pipe 7, Riser 6, (see FIG. 2)
pipe, pipeline, tubing etc.,
collectively referred to herein as OCTG and designated as MUA 9 (shown Riser 6
main tube and
auxiliary lines with the drill pipe 7 inside the main tube). Similarly, a
valve or a configuration of
valves is referred to as control valve, diverter valve, relief valve, safety
valve, BOP 8 etc. A
structure is referred to as an aircraft wing, bridge, derrick 3, crane 4,
frame, tower, helicopter
landing pad 2 etc. and of course, the rig I itself is a sea going vessel
comprising of most MUA
varieties. Regardless of the MUA name, which may comprise any of the above
mentioned ele-
ments, AutoCV: a) scans the MUA to detect a plurality of Features; b)
recognizes the MUA de-
tected Features and therefore "knows by some detail" the MUA Features; c)
associates and con-
nects the recognized MUA Features with known definitions, formulas, risks and
MUA historical
data, preferably stored in a database; d) creates an MUA mathematical and/or
geometrical and/or
numerical description compiled through the mathematical, geometrical and
numerical description
of the MUA recognized Features (herein after referred to as "Mathematical
Description"); e)
converts the MUA recognized Features into a data format for use by an FEA
and/or a CAD pro-
gram; f) calculates Feature change-chain and compares with stored failure-
chains for a match; g)
calculates a remediation to disrupt the Feature change-chain (disrupt the
failure-chain early on)
and h) updates the MUA historical data database.
[00116] The MUA Mathematical Description is then acted upon by the Loads
and De-
ployment Parameters, sufficient for calculating an MUA FFS and RULE to predict
an MUA be-
havior under deployment in accord with an embodiment of AutoCV operation.
Furthermore, the
MUA Mathematical Description may be converted to an MUA functional model or
prototype
which may be operated to verify MUA functionality directly and/or through a
CAD program
and/or through an FEA program.
18
CA 2964243 2017-04-12
[00117] Fig. 1 illustrates some components of the drilling process that
are critical. The
Riser joints 6 connect the rig 1 to the subsea BOP 8. Risers 6 comprise at
least a main tube, typi-
cally 21 inches OD, and a number of auxiliary lines. The drill pipe 7 reaches
the strata through
the Risers 6 main tube and through the BOP 8. Riser 6 main tube also acts as
the primary conduit
of the drilling fluids to the rig 1. The BOP 8 main function is to shear the
drill pipe 7 and to seal
the well in the event of an accident.
[00118] The Riser string, which could conceivably be less than or greater
than 10,000'
long, is not only exposed to the hydrostatic pressure, it is also exposed to
the ocean currents that
change direction with depth. Therefore, the riser string is a flexible
structure that also experienc-
es varying side loads, some of which lead to vortex induced vibration (VIV).
Anyone can place
vibration monitors along the Riser string, collect VIV data, write a paper and
contribute to the
general knowledge. However, as was discussed above, general knowledge does not
prevent an
accident.
[00119] AutoCV on the other hand, recognizes that it is not a generic
riser joint that en-
dures VIV but a very specific riser joint that endures a very specific VIV
loading (frequency,
magnitude etc.) that changes minute by minute. VIV adds to the cyclic fatigue
and acts upon the
Features of the specific riser joint. Therefore, knowing in detail the fatigue
status and the other
features (wall-thickness, corrosion, hardness etc.) of each riser joint in the
riser string (the subtle
readings and changes), AutoCV assesses accurately the risk factors associated
with the specific
riser joint under the specific deployment loads and thus, it disrupts a
failure-chain with exact
knowledge that is continually updated. On the other hand, Riser inspection
that acquires very few
readings only adds an insignificant amount of information beyond what is known
about a generic
riser joint.
[00120] AutoCV also recognizes that it is not a generic drill pipe joint
across the generic
shear rams of a generic BOP. Instead, AutoCV recognizes that, at any given
moment, there is a
very specific length of a very specific drill pipe joint (specific wall-
thickness, corrosion, hard-
ness, tool joint etc.) across the very specific shear rams of a very specific
BOP and thus, it dis-
rupts another failure-chain with exact knowledge that is continually updated.
19
CA 2964243 2017-04-12
[00121] Constant-Vigilance uses this specific knowledge to select
inspection and monitor-
ing instruments, such as the exemplary AutoCV system, and then strategically
locate them
around the rig. It should be understood that this selection is based on safety
and business values
and therefore, not all equipment that are discussed in the examples below
would be deployed in
all similar applications.
[00122] Subsea AutoCV
[00123] The subsea AutoCV 10C comprises of at least one console 11, an
Assessment
head 12, a number of sensors 15, a power and communication link 17 and/or a
wireless and/or
sonic and/or underwater modem and/or other types of communicators and/or chain
or relay sta-
tions that provide communication link 18 and a power and control link 19. The
console 11 com-
prises of at least one computer with software connected to a Features
detection interface and a
data acquisition system. The data acquisition system is connected to sensors
15 comprising of
numerous Loads and/or Deployment Parameters sensors that may include one or
more subsea
cameras. Console 11 further comprises of a power backup with sufficient
storage to safely oper-
ate AutoCV 10C and maintain communication with the rig floor AutoCV 10A
through the com-
munication links 17, 18 and control link 19.
[00124] Assessment head 12 comprise of at least one Features detection
sensor which in
one embodiment may produce data which when utilized in the software or
equations of the pre-
sent invention can distinguish and/or measure one, two, or three physical
dimensions of and/or
classify one, two, or three physical dimensions, and/or one, two or three
physical dimensions of
different Features and/or measure changes in Feature-morphology, fatigue, or
the like (See for
example US 7,155,369 Autonomous Non-Destructive Inspection, incorporated
herein by refer-
ence in its entirety). The features detection system is preferably not limited
to "one-dimensional"
information in the sense that "one-dimensional" data simply provides, for
example, an electrical
signal that may change due to numerous reasons and therefore it is often
unable to distinguish
much less measure or describe significant and non-significant one dimensional
physical varia-
tions of one, two or three dimensions of different features, and cannot
realistically distinguish,
much less measure or classify one, two or three physical dimensional aspects
of different fea-
tures. However, AutoCV may utilize multiple "one-dimensional" sensors that
when combined
may be utilized with equations to detect, measure and/or distinguish one, two
or three dimen-
CA 2964243 2017-04-12
sional different features. (See, for example, US 7,231,320 Extraction of
Imperfection Features
through Spectral Analysis, referenced hereinbefore and incorporated herein by
reference).
[00125] The subsea AutoCV 10C communicates with and monitors the BOP 8
controls
through the control link 19. For example, in one possible embodiment, control
link 19 may du-
plicate the function of the power and communication link 17 whereby AutoCV 10C
is powered
by and communicates with the rig floor AutoCV 10A through the BOP 8 controls.
In addition to
performing a continuous FFS, RULE and operating a model of the BOP 8, the
subsea AutoCV
10C may prevent BOP 8 actions that may damage the BOP 8 or at least notify and
ask for con-
firmation from the surface before the BOP 8 action is permitted. It should be
understood that, as
an Assessment of the system and the drilling process, the rig floor AutoCV 10A
and the subsea
AutoCV 10C are in continuous communication and act as one whereby, for
example, the rig
floor AutoCV 10A may prohibit pipe movement when the BOP 8 pipe rams are
closed until such
time that the action is confirmed. It is envisioned that such notification
will be carried out
through the rig floor AutoCV 10A visual, speech and sound interface (see FIG.
2 items 21, 31R,
50 and 55) whereby, in case of an emergency, the rig floor AutoCV 10A would
automatically
connect to additional speakers around the rig and increase the volume to an
appropriate level to
announce the emergency.
[00126] It should further be understood that the subsea AutoCV 10C would
then monitor
and confirm that the BOP 8 action was performed as intended and report back or
calculate and/or
estimate the degree by which the action was performed using data obtained
through the Assess-
ment head 12 and/or Loads and Deployment Parameters sensors 15, such as
battery status, posi-
tion of BOP 8 rams, activation of valves and controls, control's pressure,
differential pressure
across the rams and similar items. Monitoring the sound and the flow inside
the BOP 8 or the
Risers 6 would be a measure of success in closing the rams to seal the well.
[00127] Referring to Deepwater Horizon, the BOP monitor of US Patent No
7,155,369,
FIG. 3, incorporated herein by reference in its entirety, would have detected
the conditions
around the BOP 8 shear rams and would have alerted the driller instantly if
the sheared drill pipe
fell into the well away from the rams; while there was still thousands of feet
of fluid inside the
Riser. It would also have alerted the driller that the drill pipe did not fall
away, in other words it
did not shear completely, or if the drill pipe is bend or additional material
is jamming the rams.
21
CA 2964243 2017-04-12
This knowledge alone would have saved countless days of futile attempts to
close the Deepwater
Horizon BOP shear rams. Almost a year later and at enormous cost, the DNV
report reflects
what could have been known onsite instantly, knowledge that may have given the
rig crew a
fighting chance; a prime example of the high cost of lack-of-knowledge.
[00128] AutoCV standalone operation
[00129] The subsea AutoCV 10C is also capable of standalone operation in
the event of a
mishap. The subsea AutoCV 10C may be notified of a mishap or recognize a
mishap through the
Assessment head 12 and/or Loads and Deployment Parameters sensors 15 and/or
sound recogni-
tion 55 and/or through data loss or even loss of external power. The subsea
AutoCV 10 would
then enter the automatic standalone operation mode after a certain amount of
time without com-
munication with the rig floor AutoCV 10A and/or after a number of failed
communication at-
tempts or by receiving a command to enter the standalone operation mode.
[00130] The actions of the subsea AutoCV 10C may be controlled by the
material inside
the BOP 8 and/or information derived from Loads and Deployment Parameters
sensors 15 and/or
sound recognition 55 (See FIG. 2) and may be limited by the amount of stored
backup power.
The subsea AutoCV 10C may be programmed with an active and/or a passive
standalone mode.
In the active standalone mode, the subsea AutoCV 10C may analyze the
information from the
sensors using onboard stored expert knowledge and may attempt to power and/or
operate at least
part of the BOP 8 if the expert analysis suggests, for example, a well
blowout. In the passive
standalone mode, the subsea AutoCV 10C may monitor and relay to the surface
data obtained
through the Assessment head 12 and/or Loads and Deployment Parameters sensors
15, such op-
eration optimized to extend the power backup life. It is envisioned that the
subsea AutoCV 10C
may integrate a complete BOP 8 control system.
[00131] Mid-level AutoCV
[00132] A number of AutoCV 10B may be deployed along the length of the
Riser string to
perform functions substantially similar to the subsea AutoCV 10C. For example,
AutoCV 10B
may be located at a certain depth where known currents initiate VIV. AutoCV
10B system(s)
may be in communication by various means as discussed hereinbefore with AutoCV
10A and
10C systems. In addition, as part of a fault-tolerant system, the AutoCV 10B
may be equipped
22
CA 2964243 2017-04-12
with a flow restrictor to be deployed in case of a mishap. The flow restrictor
may be as simple as
an inflatable bladder with a fluid or compressed air reservoir or a ram and
support equipment.
[00133] Rig Floor (surface) AutoCV
[00134] Fig. 2 illustrates one possible embodiment of AutoCV 10A deployed
on the rig
floor 5 where it may be used to: a) assess the status of the OCTG; b) assess
the status of other rig
equipment, such as mooring, lifting and tensioner cables, tensioner cylinders
and pistons, BOP 8,
etc., c) assess the status of the rig structure and d) assess the status of
complete systems and pro-
cesses. It should be understood that AutoCV 10A may utilize different types
and/or shapes
and/or configurations of assessment heads 12 to fulfil the Assessment needs of
the different
MUAs which are referenced hereinbefore or after.
[00135] In this embodiment, AutoCV 10A comprise of at least one computer
20, with a
display 21 and a remote display 21R, storage 23, an Assessment head 12 (shown
while scanning
drill pipe 7 as it is tripped from the well), a position and speed encoder 13,
a features detection
interface 30 and a data acquisition system 35 connected to numerous Load and
Deployment Pa-
rameter sensors 15 distributed around the rig. The rig floor AutoCV 10
communicates with other
AutoCV system, which may selectively be deployed around the rig, through wired
and wireless
communication links 26 that also allows for access to remote experts,
computers and stored
knowledge. The AutoCV 10A communicates with an operator or the rig crew
through displays
21 and 21R, keyboard 22, Natural Speech and Sound interface 50 connected to a
speaker or ear-
phone 27 (helmet mount is shown) and a Speech and Sound recognition interface
55 connected
to a microphone 28. It should be understood that not all AutoCV components
would be deployed
in all applications.
[00136] Material Identification
[00137] Material identification is critical for the Assessment process.
The present inven-
tion provides means of correcting some misidentifications but not necessarily
all. In addition to
identification through camera 29 and/or operator identifying the material
through keyboard 22,
microphone 28, speech 55 and/or other inputs or stored information, at least
one communication
link 26 may facilitate communication with an identification system or a tag,
such as RFID, af-
fixed to MUA. Such identification tags are described in US patents No.
4,698,631, No. 5,202,680
23
CA 2964243 2017-04-12
and No. 6,480,811 and are commercially available from multiple sources such as
Texas Instru-
ments, Motorola and others. Embedded tags specifically designed for harsh
environments, are
available with user read-write memory onboard (writable tag). It is
anticipated that the memory
onboard identification tags would increase as well as the operational
conditions, such as tempera-
ture, while the dimensions and cost of such tags would decrease.
[00138] Computer 20 preferably provides for data exchange with the
material identifica-
tion system, including but not limited to, material ID, material geometry,
material database, pre-
ferred FEA model, preferred evaluation system setup, constraints, constants,
tables, charts, for-
mulas, historical data or any combination thereof. It should be understood
that identification sys-
tems may further comprise of a data acquisition system and storage to monitor
and record Load
and Deployment Parameters of MUA 9 (See FIG. 1). It should be further
understood that the ma-
terial identification system would preferably operate in a stand-alone mode or
in conjunction
with AutoCV. For example, while tripping out of a well, computer 20 may read
such data from
the drill pipe 7 or tubing identification tag and while tripping into a well,
computer 20 may up-
date the identification tag memory. Another example would be an identification
computer with a
data acquisition system affixed onto a Riser joint 6 or a crane 4. During
deployment, such an
identification system would preferably monitor and record Load and Deployment
Parameters.
[001391 Speech and Voice control
[00140] Speech is a tool which allows communication while keeping one's
hands free and
one's attention focused on an elaborate task, thus, adding a natural speech
interface to the Au-
toCV would preferably enable the operator to focus on the MUA and other
related activities
while maintaining full control of the AutoCV. Furthermore, the AutoCV natural
speech interac-
tion preferably allows the operator to operate the AutoCV while wearing gloves
or with dirty
hands as he/she will not need to physically manipulate the system.
[001411 Language selection
[001421 Different AutoCV may be programmed in different languages and/or
with differ-
ent commands but substantially performing the same overall function. The
language capability of
the AutoCV may be configured to meet a wide variety of needs. Some examples of
language ca-
pability, not to be viewed as limiting, may comprise recognizing speech in one
language and re-
24
CA 2964243 2017-04-12
sponding in a different language; recognizing a change of language and
responding in the
changed language; providing manual language selection, which may include
different input and
response languages; providing automatic language selection based on pre-
programmed instruc-
tions; simultaneously recognizing more than one language or simultaneously
responding in more
than one language; or any other desired combination therein. In the event of
an emergency, Au-
toCV preferably will announce the emergency and the corrective action in
multiple languages
preferably to match the native languages of all the crew members. It should be
understood that
the multi-language capability of the AutoCV voice interaction is feasible
because it is limited to
a few dozen utterances as compared to commercial voice recognition systems
with vocabularies
in excess of 300,000 words per language.
[00143] AutoCV speech
[00144] Text to speech is highly advanced and may be implemented without
great difficul-
ty. Preferably, when utilizing text to speech, the AutoCV can readily recite
its status utilizing,
but not limited to, such phrases as: "magnetizer on"; "chart out of paper",
and "low battery". It
can recite the progress of the AutoCV utilizing, but not limited to, such
phrases as: "MUA
stopped" and "four thousand feet down, six thousand to go". It can recite
readings utilizing, but
not limited to, such phrases as "wall loss", "ninety six", "loss of echo",
"unfit material", "ouch",
or other possible code words to indicate a rejectable defect. The operator
would not even have to
look at a watch as simple voice commands like "time" and "date" would
preferably recite the
AutoCV clock and/or calendar utilizing, but not limited to, such phrases as
"ten thirty two am",
or "Monday April eleven".
[00145] However, it should be understood that the primary purpose of the
AutoCV is to
relay MUA (as-designed, as-is etc.) Load and Deployment information to the
operator. There-
fore, AutoCV would first have to decide what information to relay to the
operator and the related
utterance structure. It should be understood that in this example AutoCV 10A
may further be uti-
lized to coordinate communications for other AutoCV systems.
[00146] Assessment trace to sound conversion
[00147] The prior art does not present any solution for the conversion of
the Assessment
to speech or sound. The present invention utilizes psychoacoustic principles
and modeling to
CA 2964243 2017-04-12
achieve this conversion and to drive a speech and sound synthesizer 50 with
the resulting sound
being broadcast through a speaker or an earphone 27. Thus, the assessment
signals may be lis-
tened to alone or in conjunction with the AutoCV comments and are of
sufficient amount and
quality as to enable the operator to monitor and carry out the entire
assessment process from a
remote location, away from the AutoCV console and the typical readout
instruments. Further-
more, the audible feedback is selected to maximize the amount of information
without overload
or fatigue. This assessment-to-sound conversion also addresses the dilemma of
silence, which
may occur when the AutoCV has nothing to report. Typically, in such a case,
the operator is not
sure if the AutoCV is silent due to the lack of features or if it is silent
because it stopped operat-
ing. Furthermore, certain MUI 9 features such as, but not limited to, collars
or welds can be ob-
served visually and the synchronized audio response of the AutoCV adds a
degree of security to
anyone listening. A wearable graphics display 21R could further enhance the
process away from
the AutoCV console.
[00148] AutoCV sound recognition
[00149] AutoCV would preferably be deployed in the MUA use site and would
be ex-
posed to the site familiar and unfamiliar sounds. For example, a familiar
sound may originate
from the rig engine revving-up to trip an OCTG string out of a well. An
indication of the MUA
speed of travel may be derived from the rig engine sound. An unfamiliar sound,
for example,
would originate from a bearing about to fail. It should be noted that not all
site sounds fall within
the human hearing range but may certainly fall within the AutoCV analysis
range when the Au-
toCV is equipped with appropriate sensors and microphone(s) 28. It should also
be noted that an
equipment unexpected failure may affect adversely the MUA RUL, thus training
the AutoCV to
the site familiar, and when possible unfamiliar sounds, such as a well blowout
or a high pressure
hose leak, would be advantageous.
[00150] AutoCV speech recognition
[00151] Speech recognition is also highly advanced and may be implemented
without
great difficulty or may be purchased commercially. A typical speech and sound
recognition en-
gine 55 may comprise an analog-to-digital (herein after referred to as "A/D")
converter, a spec-
tral analyzer, and the voice and sound templates table. The description of the
sequence of soft-
26
CA 2964243 2017-04-12
ware steps (math, processing, etc.) is well known in the art, such as can be
found in Texas In-
struments applications, and will not be described in detail herein.
[00152] Operator identification and security
[00153] Preferably, at least some degree of security and an assurance of
safe operation, for
the AutoCV, is achieved by verifying the voiceprint of the operator and/or
through facial or iris
scan or fingerprint identification through camera 29 or any other biometric
device. It should be
understood that camera 29 may comprise multiple cameras distributed
throughout. With voice-
print identification, the likelihood of a false command being carried out is
minimized or substan-
tially eliminated. It should be appreciated that similar to a fingerprint, an
iris scan, or any other
biometric, which can also be used for equipment security, a voiceprint
identifies the unique char-
acteristics of the operator's voice. Thus, the voiceprint coupled with
passwords will preferably
create a substantially secure and false command immune operating environment.
[00154] Voiceprint speaker verification is preferably carried out using a
small template, of
a few critical commands, and would preferably be a separate section of the
templates table. Dif-
ferent speakers may implement different commands, all performing the same
overall function.
For example "start now" and "let's go" may be commands that carry out the same
function, but
are assigned to different speakers in order to enhance the speaker recognition
success and im-
prove security. As discussed herein above, code words can be used as commands.
The com-
mands would preferably be chosen to be multi-syllabic to reduce the likelihood
of false triggers.
Commands with 3 to 5 syllables are preferred but are not required.
[00155] It should be further understood that the authorize operator may
also be identified
by plugging-in AutoCV a memory storage device with identification information
or even by a
sequence of sounds and or melodies stored in a small playback device, such as
a recorder or any
combination of the above.
[00156] AutoCV operation through speech
[00157] Preferably, the structure and length of AutoCV utterance would be
such as to con-
form with the latest findings of speech research and in particular in the area
of speech, meaning
and retention. It is anticipated that during the AutoCV deployment, the
operator would be dis-
tracted by other tasks and may not access and process the short term auditory
memory in time to
27
CA 2964243 2017-04-12
extract a meaning. Humans tend to better retain info' illation at the
beginning of an utterance
(primacy) and at the end of the utterance (recency) and therefore the AutoCV
speech will be
structured as such. Often, the operator may need to focus and listen to
another crew member, an
alarm, a broadcasted message or even an unfamiliar sound and therefore the
operator may mute
any AutoCV speech output immediately with a button or with the command "mute"
and enable
the speech output with the command "speak".
[00158] The "repeat" command may be invoked at any time to repeat an
AutoCV utter-
ance, even when speech is in progress. Occasionally, the "repeat" command may
be invoked be-
cause the operator failed to understand a message and therefore, "repeat"
actually means "clari-
fy" or "explain". Merely repeating the exact same message again would probably
not result in
better understanding, occasionally due to the brick-wall effect. Preferably,
AutoCV, after the first
repeat, would change slightly the structure of the last utterance although the
new utterance may
not contain any new information, a strategy to work around communication
obstacles. Further-
more, subsequent "repeat" commands may invoke the help menu to explain the
meaning of the
particular utterance in greater detail.
[00159] It should be appreciated that the present invention incorporates a
small scale
speech recognition system specifically designed to verify the identity of the
authorized operator,
to recognize commands under adverse conditions, to aid the operator in this
interaction, to act
according to the commands in a substantially safe fashion, and to keep the
operator informed of
the actions, the progress, and the status of the AutoCV process, especially in
the event an emer-
gency.
[00160] AutoCV Assessment
[00161] New material may or may not be fabricated as-designed and the
design is often
based on certain assumptions which may or may not be correct, such as the
gusset plates of the I-
35W bridge in Minneapolis. Furthermore, the in-service (used) material
deterioration is cumula-
tive over time. AutoCV 10 (which may comprise AutoCV 10A, AutoCV 10B, AutoCV
10C
and/or other AutoCV systems) provides a quantitative Assessment of a new or an
in-service ma-
terial to ascertain its suitability for a service. AutoCV Assessment is based
on the as-is material
28
CA 2964243 2017-04-12
Mathematical Description coupled with the historical data, the measured Loads
and Deployment
Parameters.
[00162] The MUA historical data should relay sufficient knowledge about
the MUA, the
deployment conditions and the boundaries (Accept/In-service monitoring/Reject-
Redeploy) to
adequately define the automatic Assessment Fitness categories and/or the safe-
operating zone(s)
and to create and operate an MUA FEA model. Typically, historical data define
or permit for the
calculation of the MUA safe-operating zone(s). Initial historical data is
typically provided by the
MUA owner/user/manufacturer and consists of:
[00163] a) Design data such as drawings, material specifications, design
parameters and
assumptions, loads, limits, constraints and calculations to adequately define
the as-designed
MUA;
[00164] b) Fabrication data such as drawings, material specifications,
weld and heat-
treatment reports, measurements and manufacturing inspection records to
adequately define the
as-built MUA;
[00165] c) Maintenance data such as alterations, adaptations, repairs and
inspection rec-
ords to adequately define the as-last-known MUA and
[00166] d) Loads, Deployment Parameters, Environment, Risks and Failure-
chains as dis-
cussed above. The location (longitude and latitude) may be sufficient to
define some of the loads
and boundaries like the formation, prevailing ocean currents, seismic activity
and similar items.
[00167] The function of the features detection interface 30 is to induce
controlled excita-
tion into the MUA through the Assessment head 12 and to detect the response of
the MUA
through the sensors of the Assessment head 12. It should be appreciated that
the Assessment
head 12, whole or in part, may be applied to the outside or to the inside of
the MUA or any com-
bination thereof to cover the Assessment needs of MUA. It should also be
understood that not all
Assessment head 12 functions and components would be deployed simultaneously
or in all ap-
plications. It should further be understood that the assessment heads 12 may
operate in an active
mode (induce full excitation) or in a bias mode (induce modified excitation)
or in a passive mode
(monitor the sensors only).
29
CA 2964243 2017-04-12
[00168] The Assessment head 12 sensor signals are preferably band limited
and are con-
verted to, lengthwise or timewise, time-varying discrete digital signals which
are further pro-
cessed by at least one computer 20 utilizing an extraction matrix (illustrated
in Fig. 3A) to de-
compose the time-varying discrete digital signals into the flaw spectrum (flaw
spectrum is a
trademark of STYLWAN). The extraction matrix concept was published in 1994 and
it is beyond
the scope of this patent but it applies equally to any MUA some of which are
referenced herein-
before or after.
[00169] Mathematical Description of the MUA
[00170] The flaw spectrum is then processed by a system of identifier
equations, as illus-
trated in Fig. 3B, resulting in a Mathematical Description of the MUA compiled
through the
Mathematical Description of its Features. At least one computer 20 utilizes
stored constraints
and/or knowledge and/or rules and/or equations and/or MUA historical data to
identify the nature
and/or characteristics of MUA Features so that at least one computer 20 knows
by some detail
the MUA Features and connects and associates the MUA Features with known
definitions, for-
mulas, Mathematical Description, FEA, CAD and similar items resulting in
Identification Coef-
ficient(s) Ki. It should be understood that Ki may be a number and/or an
equation, an array of
numbers and/or equations, a matrix, a table or a combination thereof.
[00171] Under certain geometrical conditions, Features in proximity may
form a Critical-
ly-Flawed-Area (CFA) (Critically-Flawed-Area and CFA are trademarks of
STYLWAN), even
Features that are mundane on their own. A root-cause of a failure would be a
1D-NDI inspector
dismissing mundane Features without taking into account their interaction in
the overall system.
STYLWAN defines a CFA (illustrated in Fig. 4) as "an MUA area that fosters
crack initiation
due to high stress concentration and promotes rapid crack propagation through
bridging". There-
fore, the Feature's Neighborhood is another critical Assessment parameter that
1D-NDI over-
looks. At least one computer 20 examines the lengthwise flaw spectrum for
other Neighborhood
Features resulting in Neighborhood Coefficient(s) Kn. It should be understood
that Kn may be a
number and/or an equation, an array of numbers and/or equations, a matrix, a
table or a combina-
tion thereof.
CA 2964243 2017-04-12
[00172] At least one computer 20 may further measure and acquire MUA Loads
and/or
Deployment Parameters by operating a data acquisition system 35 connected to
numerous Load
and Deployment Parameter sensors 15 resulting in Loading Coefficient(s) Kf. It
should be under-
stood that Kf may be a number and/or an equation, an array of numbers and/or
equations, a ma-
trix, a table or a combination thereof. At least one computer 20 further
calculates and verifies
that the MUA is operating within the safe-operating zone(s) of the operational-
envelop. When
the MUA is operated outside the safe-operating zone(s), at least one computer
20 alerts the oper-
ator and logs the conditions, time and event duration. AutoCV may further be
programmed to
permit such operation for a limited duration, to permit the operation under
instructions from the
operator or to inhibit the operation of MUA. Fig. 1 numerous AutoCVs may also
be pro-
grammed to determine the root-cause(s) of the operating anomaly, for example,
a well blowout
may be determined by the upward traveling wellbore flow and associated
pressure and sound.
[00173] A computer program may further evaluate the impact of the MUA
Features, and
Deployment Parameters upon the MUA by selecting and applying Load specific
Stress-
Concentration and/or Deterioration Coefficients from equations, look-up tables
or 3D charts as
illustrated in Fig. 3C. Load specific Stress Concentration factor values may
be obtained from the
literature, from equations, from FEA or a combination thereof. Some
Deterioration Coefficients
may also be obtained from the literature, however, more accurate location
specific Deterioration
Coefficients may be obtained from previously acquired flaw spectrums in
proximity to the de-
ployment location. Therefore, coupling lengthwise flaw spectrums with
longitude and latitude
also results in a 3D history of the location/formation.
[00174] Numerical description of the MUA
[00175] The simplest form of a MUA Mathematical Description is a string of
numbers.
Strings of lengthwise numbers may represent wall thickness, hardness,
corrosion, cracks, fatigue,
FFS, RULE, number of cycles, other MUA information or combinations thereof.
For example,
the string {0.888, 0.879, ..., 0.876, 0.880} may represent the lengthwise Wall
Thickness of a
Riser joint in inches. The string {101, 100, ... 99, 100} may represent the
lengthwise Wall
Thickness of a Riser joint as percentage of nominal Wall Thickness. The string
{155, 161, ...
157, 160} may represent the lengthwise Brinell hardness of a Riser joint. The
string {19.24,
19.28, ... 19.20, 19.21} may represent the lengthwise internal diameter (ID)
of a Riser joint. The
31
CA 2964243 2017-04-12
string {55.01, 54.87, ... 54.62, 54.98} may represent the lengthwise cross-
sectional area of a Ris-
er joint in square inches, combinations thereof and similar items.
[00176] It should be understood that multiple such strings would cover, as
close as possi-
ble to 100%, the MUA resulting in a string array of a specific type which may
comprise multiple
pipes that create a multi-conductor riser or a multi-conductor umbilical. A
unique feature of the
present invention is that calculations using string arrays may reveal
additional MUA details and
subtle changes that humans and 1D-NDI ignore. For example, the lengthwise
minimum and
maximum diameter of a tube would permit a full length calculation of ovality a
pipe or each
conductor of a multi-conductor riser or umbilical used for subsea operations.
The internal (ID)
and external (OD) diameter string arrays of tubes are also used in the
calculation of axial stress,
burst yield, collapse yield, fluid volume, hoop stress, overpull, radial
stress, stretch, ultimate load
capacity, ultimate torque, yield load capacity, yield torque, similar items
and combination thereof
using formulas and charts found in the literature. In another example,
Assessment would exam-
ine the temperature readings encountered during a sea-going vessel passage to
determine if the
ductile-brittle transition temperature was ever reached or preferably
Assessment would assign a
passage to avoid low temperature areas.
[00177] It should further be understood that coupling string arrays with
other measured
values would result in a detailed geometrical description of the as-is MUA,
such as combining
the lengthwise internal diameter (ID) string arrays of a tube with the
corresponding wall thick-
ness arrays. The geometrical description of the MUA may further be compared
with the histori-
cal Data such as Design, Fabrication and Alteration records and may be
exported as a drawing
file for use by CAD programs, simulation programs and FEA engines. MUA non-
compliance
may be reported to the operator.
[00178] Furthermore, comparison of historical data similar strings and
Failure-chains may
reveal a Feature change, a Feature morphology migration, a Feature propagation
and the calcula-
tion and identification of a subtle change-chain that matches an early stage
of at least one of
stored Failure-chains that may be disrupted through remediation before it
progresses to a Failure-
chain and eventually to an Accident-chain. For example, in Coiled Tubing a
crack may initiate at
the bottom of a corrosion pit that acts as a stress concentrator under loading
(a CFA). The fre-
quent scans of AutoCV would detect the coexisting crack and thus AutoCV will
detect the subtle
32
CA 2964243 2017-04-12
Feature morphology migration from pit to crack, recommend a remediation and
disrupt the acci-
dent-chain. It should be understood that the transition from Feature change to
a Failure (Imper-
fection to Flaw to Defect) is subtle and lengthy while the transition from
Failure to Accident is
rapid and sudden. For example, the morphology change-chain may take 98% of the
material
RUL while the progress to Accident only 2%. This is also the reason sporadic
inspections of crit-
ical materials are often inadequate.
[00179] Critically-Flawed-Path
[00180] Computer 20 may further calculate a simpler flaw spectrum by
combining all Fea-
tures of a section, such as a circumference, into an equivalent flaw spectrum
using Ki, Kn, Kf
coefficients, stored formulas, charts, tables and historical data.
[00181] Fig. 4 illustrates the MUA resulting simpler flaw spectrum, a
Critically-Flawed-
Path (Herein after referred to as "CFP") (Critically-Flawed-Path and CFP are
trademarks of
STYLWAN). It should be understood that there is no physical correspondence
between the CFP
and the MUA Features as CFP is a mathematical construct that only preserves
the MUA perfor-
mance. A conservative Assessment of MUA will place the CFP on the Major/Minor
axis of
MUA where Features endure the maximum effects of loading. Under bending, for
example, the
major axis experiences the maximum tension and the minor axis the maximum
compression. It is
not uncommon for the major and minor axis to alternate during deployment.
Again, it should be
understood that the CFP Assessment is very conservative representing the worst
case scenario.
However, such Assessment is appropriate for safety-critical equipment that
must exhibit high
operation reliability, such as the BOP 8.
[00182] Optimizing system operation
[00183] Typically MUA is part of a system which can be viewed as a complex
MUA as
discussed earlier. Again, it should be understood that following the analysis,
the Assessment of
complex MUA closes the loop by starting from the simplest MUA components
progressing up-
wards in complexity. For example, a tool joint is a component of a drill pipe
7, which in turn is a
component of the drilling process along with casing, derrick, BOP 8, Risers 6
etc. It is a unique
feature of the present invention that the Mathematical Description of the MUA
may be further
manipulated to address system specific requirements and to optimize the system
operation.
33
CA 2964243 2017-04-12
[00184] For example, the Mathematical Description of each drill pipe 7
joint coupled with
their specific location would result in the Mathematical Description of the as-
is drill string, a
unique feature of the present invention. While drilling, the drill string
endures high tensile loads
at the surface and high compressive loads at the bottom and therefore, AutoCV
knows by some
detail the type of loading and the duration each drill pipe 7 joint endured,
assess the drill pipe 7
Features under the measured loading and estimates an FFS and RULE. While
tripping out of the
well, AutoCV 10A would then scan the drill pipe 7 and compare the actual
Features, FFS and
RULE to the predicted Features by the Assessment while drilling and fine tune
the Assessment
through these continuous measurements. The Mathematical Description of the as-
is drill string
may be further manipulated to a CFP to address specific drilling process and
equipment needs,
such as the specific needs of the BOP 8 rams or other well features or
equipment.
[00185] For example, in order to address the specific needs of the BOP 8
rams, at least one
computer 20 may reprocess the drill string to a special string array of
numbers such as {10, 8,8,
... 1, 1, 3, 1, 1, ... 1, 4, 8, 8, 10, 10, 8 , 8 ... 1, 1, 1, ... 8,8, 10}
where 10 may be assigned to a
tool joint or a drilling collar (red ¨ do not close BOP 8 rams), 8 may be
assigned to safety select-
ed lengths on either side of a tool joint (orange ¨ safety length), 4 may be
assigned to lengths
with higher hardness(yellow), 3 may be assigned to lengths with thicker than
nominal wall (yel-
low), and 1 to lengths with nominal material (green ¨ preferred length to
close the rams). Fur-
thermore, at least one computer 20 may monitor the string weight through data
acquisition sys-
tem 35 to determine if the drill pipe 7 is under tension or compression. The
optimal condition to
shear the drill pipe 7 is when body wall it is centered in the shear rams,
under tension and with
nominal or less hardness and wall thickness. The driller's display may then
combine all such data
in a simplified color scheme appropriate for an emergency. Preferably, the
emergency driller's
monitor would be separate from the other monitors and will not use overlapping
windows, as a
critical but rarely used window may be hidden behind a more often used window.
In addition to
the display, at least one computer 20 may utilize stored expert knowledge,
sound, voice and
speech recognition to aid or even guide the driller in case of an emergency.
[00186] It should be understood that if part or the whole drill string is
replaced by a higher
strength drill string, AutoCV will detect the change and assess automatically
the drilling system
using the new drill string data.
34
CA 2964243 2017-04-12
[00187] It should also be understood that the lengthwise drill pipe
lengths are in reference
to the surface AutoCV 10A assessment head 12. At least one computer 20 through
data acquisi-
tion system 35 may measure Deployment Parameters such as, but not limited to,
angle, direction,
distance, heave, position, location, speed and similar items to calculate
instantly the location of
the surface assessment head 12 in reference to other locations such as the BOP
8 rams or a dog-
leg and therefore reference said flagged lengths to said other locations. This
calculation may be
utilized alone and/or may provide a backup for the subsea AutoCV 10C when one
is deployed. In
addition, AutoCV may calculate the drill pipe stretch using measured
Deployment Parameters
and Historical data.
[00188] The above is an example of how AutoCV may use data from one system
compo-
nent, the as-is drill string for example, to examine its impact on the overall
system. Another
unique and novel feature of the present invention is that it may also assess
the impact of the
overall process upon a component. For example, computer 20 may monitor, log
and evaluate the
overall drilling performance and its impact on the MUA by measuring the power
consumption of
the drilling process, the string weight, weight on bit, applied torque,
penetration rate and other
related parameters. Such information, an indication of the strata and the
efficiency of the drilling
process, may be processed and used as a measure to further evaluate and
understand the impact
of the process upon the MUA, the as-is drill string imperfections, FFS and
RULE.
[00189] Optimizing a process
[00190] In addition, MUA is part of a system which, most likely, is part
of a process. For
example, a pitot tube is after all part of the flight from Rio to Paris. This
failure-chain is fairly
easy to establish.
[00191] The components involve the Pitot Tube working, who is flying the
plane, wheth-
er the AircraftAutopilot Pilot is used and has a recovery procedure built into
software, training
for RecoveryOverspeed, and similar factors.
[00192] The worst Failure-chain then is: {No (Pitot Tube not working),
Unknown (no
other type of air speed indicator), Off (disconnect auto pilot), Passenger
flying the airplane, No
training for recovery/overspeed, and no software built into the auto pilot for
overspeed/recovery
or to provide help to the flight crew) while the particular Failure-chain was
{No, Unknown, Off,
CA 2964243 2017-04-12
Trainee, No, Yes}. This Failure-chain could have been disrupted with adequate
airspeed backup
indicator of different type, with a Senior Captain in the controls, with
training of the flight crew
to recover from the pitottube failure, with a recovery procedure programmed in
the Autopilot or
even the computer advising the flight crew on probable causes and suggesting
recovery tech-
niques. It should also be noted that AutoCV could utilize an accelerometer
and/or other sensors
to measure the sharpness of the storm jolts and bumps and convert them to an
estimated aircraft
(or watercraft) speed. After all, the Autopilot did detect the failure and
disconnected instead of
advising the crew of a recovery procedure(s) while monitoring critical flight
data. Furthermore,
review of historical data revealed that these particular pitot tubes freeze
with increased frequency
during a storm in the Intertropical Convergence Zone where the disaster
occurred. An Assess-
ment would then have concluded that the pitot tube heaters were not
sufficient, also disrupting
the failure-chain. Flying around the storm would also have disrupted the
Failure-chain but it
would have delayed the flight and consumed more fuel.
[00193] Again, this failure-chain can easily be translated to a numerical
string, such as
{10, 10, 10, 6, 10, 10} where 10 represents the worst possible scenario, 6
represents a trainee and
1 represents the best possible scenario. One may add 8 for flight through the
Intertropical Con-
vergence Zone resulting in {8, 10, 10, 10, 6, 10, 10}. It is clear from this
numerical string that
this was a disaster waiting to happen. A backup speed sensor adept to harsh
conditions or a more
powerful heater would change the numerical string to {8, 10, 1, 1, 6, 10, 1}
disrupting the fail-
ure-chain. This is also an example of using identical systems as a backup
resulting in a double or
triple failure, not increased reliability and safety. Another example is
stacking two or three BOPs
d on top of each other that will fail simultaneously when dealing with high
strength pipe result-
ing again in a double or triple failure, not increased reliability and safety.
[00194] AutoCV operable model
[00195] Another unique and novel feature of the present invention is the
functional model
of the as-is MUA that may be operated by the software. For example, the
software may close and
open a BOP 8 ram (will operate the software model of BOP 8) and verify that
the as-is BOP 8,
under the measured Loads and Deployment Parameters, is still operable. This
involves at mini-
mum, assembling a system using preferably the as-is components; calculating
the effects of the
36
CA 2964243 2017-04-12
Loads and Deployment Parameters on each component and verifying that there is
no undue dete-
rioration or interference between the components during the operation.
[00196] For example, when two concentric tubes slide in reference to each
other, the mod-
el operation may be limited to examining the ODs of the inner and the IDs of
the outer tube us-
ing the corresponding string arrays, all referenced to a common centerline.
For simplicity, the
model operation may be carried-out using a 2D cutout comprising of the minimum
outer ID and
the maximum inner OD as shown below.
[00197] {5.007, 5.009, ... 5.006, 5.004} ID of outer tube (minimum values)
[00198] {4.999, 5.003, ... 5.001, 4.998} OD of inner tube (maximum values)
[00199] However, the inner tube may be subjected to a fixed or, most
likely, varying
bending moment when it slides out. This action alone would fatigue and deform
the inner tube
over time. In addition, the inner tube may endure thermal-cycling along with
the cyclic bending.
A measure of the inner tube fatigue may be as simple as keeping track of the
number of cycles,
Loads and Deployment Parameters sufficient for the RULE calculation of the
inner tube. It
should further be understood that fatigue is not equally distributed
throughout the material, so a
conservative RULE value should be utilized until additional data is obtained
following subse-
quent Assessment scans.
[00200] Furthermore, the extended inner tube (or rod) may be subjected to
a corrosive en-
vironment resulting in additional deterioration. For example, during drilling,
repeated scans of
the drill pipe 7 may establish a measure for the corrosive environment. It
would be safe to as-
sume that the wellbore side of BOP 8 and the Risers 6 are subjected to the
same environment
leading to deterioration calculation for the exposed BOP 8 components and the
ID of the Risers
6. These estimates may be further fine-tuned with subsequent Assessment scans
and the findings
may further be stored in a Longitude and Latitude reference for use in future
drilling operations.
This is another example of AutoCV assessing the impact of the overall process
upon a compo-
nent.
[00201] It is well known that material deterioration due to loading is
magnified when the
loads are applied in a corrosive environment. Particularly, the problem of
fatigue cracks rapidly
magnifies when the material is subjected to cyclic loading in corrosive
environments. The envi-
37
CA 2964243 2017-04-12
ronment the BOP 8, the drill pipe 7, the Risers 6 and the welds are exposed
may change as the
drilling progresses. Exposed rods of the BOP 8 or tensioner pistons may
corrode slightly under-
mining the seals resulting in a hydraulic leak. This is an example of a subtle
change that may im-
pact the drilling equipment but it will go unnoticed until a failure occurs or
an oil sheen is ob-
served.
[00202] Preferably, AutoCV knows by some detail the components
deterioration mecha-
nism(s) and its effects over time or number of cycles etc. This knowledge may
also be applied
on the as-is model to calculate, for example, a BOP 8 shear-efficiency
constant Kse and to create
an as-predicted model, thus calculating FFS and RULE through a different path.
[00203] Preferably, the Deployment Parameters of MUA, along with the
operable as-
designed and as-built model will be stored onboard the AutoCV to facilitate an
operational com-
parison of the as-is and/or as-predicted to the as-designed and/or as-built
MUA model. It should
be understood that on a subsequent Assessment, the new as-is model would be
compared to the
as-predicted model which would be appropriately updated.
[00204] BOP Assessment
[00205] The BOP 8 pressure rating only applies to the pressure containment
vessel, not the
valve closure mechanisms or the overall BOP 8 operation. Therefore, minimal 1D-
NDI is per-
formed on the pressure containment vessel, none of which takes into account
the actual static and
dynamic conditions the BOP 8 endures during deployment and especially during a
blowout
where the BOP 8 is the last line of defense. For example, subsea BOP 8
inspection does not ac-
count, among many others, for simple issues like the pressure and temperature
difference be-
tween the outside of the BOP 8 (seafloor) and the inside of the BOP 8
(wellbore). Yet, this De-
ployment Parameters difference alone could even render the BOP 8 inoperable
during deploy-
ment.
[00206] As a result, subsea BOPs fail to pass a "good test" 50% of the
time, as document-
ed by SINTEF, MMS and other organizations and studies. Following a SINTEF
study of the
Norwegian sector of the North Sea, MMS began a review of the BOP testing
around 1993. MMS
study determined that BOP failure rates were substantially greater than those
recorded by SIN-
TEF. Despite two decades of studies, MMS, API, SINTEF, DNV and other
participants are not
38
CA 2964243 2017-04-12
reporting any BOP performance improvement. The failure of the Deepwater
Horizon BOP was
consistent both with the industry observations/tests and the findings and
reports of the regulatory
agencies (like MMS, now renamed BOEMRE).
[00207] Where safety-critical high-reliability equipment is concerned,
such as the BOP 8,
the risk is increased significantly when sporadic 1D-NDI is used as a
substitute for Assessment.
Another faulty approach is the use ill-defined backup equipment as a
substitute for a high-
reliability Assessment. For example, stacking two BOPs, one on top of the
other, may give a
false sense of security and increased safety. However, both BOPs are typically
made by the same
manufacturer, both BOPs suffer from the exact same idiosyncrasies and
shortcomings and both
BOPs will fail exactly the same way when dealing with high-strength drill pipe
or a drilling col-
lar, a reliability problem that will never be solved by stacking BOPs.
Therefore, backup systems
do not necessarily result in a high-reliability fault-tolerant system because
backup systems come
with their own idiosyncrasies and shortcomings and they are more difficult to
test. Failures of
backup systems resulted in the Three Mile Island, Chernobyl and Fukusima
disasters, all three of
which could have been avoided with high-reliability Assessment methods and
controls.
[00208] Therefore, meticulous Assessment of safety-critical high-
reliability equipment,
systems and processes should pave the way for the selection of backup.
Selection of backup, fol-
lowing a meticulous Assessment, would most likely result in fine-tuned backup
system(s) capa-
ble of recovering whole or partial functionality after a failure, such as the
mid-level AutoCV
10B. Typically, a fine tuned backup system is less expensive to implement and
does increase re-
liability and safety. On the other hand, ordering two of the same would most
likely result in a
double failure, not increased reliability and safety. For example, the A330
uses more than 2 pitot
tubes that are also heated to avoid freezing and yet, it should be expected
that all will fail the
same way when the temperature drops below a certain level.
[00209] The "Fog-Of-Emergency"
[00210] Lack-of-knowledge controls an emergency, particularly at the
onset. Preferably,
AutoCV would foresee a failure that may lead to an emergency through the
Mathematical De-
scription of the system and alert the operator before the failure occurs.
However, AutoCV does
not scan all of the system components continually and for some components
AutoCV relies on
39
CA 2964243 2017-04-12
predicting their deterioration through indirect means. Furthermore, an
emergency may be the re-
sult of circumstances beyond the realm of AutoCV, such as another vessel
colliding with a float-
ing drilling rig. Even under those circumstances, AutoCV preferably would be
programmed to
aid the operator by lifting the Fog-of-Emergency within its realm ("Fog-Of-
Emergency" or
"FOE" are trademarks of STYLWAN). For example, if the mishap did not damage
the drilling
equipment, systems and process, the operator or other crew members could
instantly access their
status through the AutoCV with a simple "status" verbal command where the
AutoCV will dis-
play and recite the status of critical parameters. This will enable the
operator and crew to focus
on other emergency issues, even away from the control room, with the AutoCV
monitoring the
drilling equipment, system and process and keeping in touch with operator and
crew through the
multiple remote communication links.
[00211] Preferably AutoCV will also be programmed to interpret the data
and recognize
the root-cause of an emergency or identify some most-likely causes. AutoCV
would then be pro-
grammed to recite the findings to the operator and the crew and suggest
corrective actions to dis-
rupt the failure-chain. It should be understood that the operator may move to
a safe(r) location
and still stay in touch with AutoCV through speech, sound and the remote
communication links.
Furthermore, AutoCV access to remote experts may be utilized during an
Emergency with the
experts having access to all AutoCV data.
[00212] It should further be understood that AutoCV systems may be
distributed through-
out the rig as communication backups. For example, a failure or a fire may
disable the rig floor
AutoCV 10A, however, AutoCVs 10B and 10C would still be fully functional and
capable of
duplicating multiple AutoCV 10A functions therefore, the distributed
communication capability
may recover whole or partial AutoCV functionality. Subsea power is limited and
expensive and
therefore AutoCV may configure assessment heads 12 of AutoCVs 10B and/or 10C
to function
in a passive detection mode without inducing power consuming excitation or
inducing reduced
excitation during normal operation. After the failure though, AutoCV may
instruct AutoCVs 10B
and/or 10C to enter the active mode to safely perform an Emergency Disconnect
Sequence (here-
in after referred to as "EDS") for example.
[00213] In offshore drilling there may be a need for an emergency
disconnect between a
drilling rig and the sea-floor wellhead. In addition to an equipment failure,
a dynamically posi-
CA 2964243 2017-04-12
tioned rig may no longer be able to maintain its position above the sea-floor
wellhead due to in-
clement weather. A properly executed EDS allows the rig to move off location
without damaging
the subsea equipment and still maintaining control of the well. A typical EDS
mandates that the
drill string is picked up and hung off in the BOP 8 pipe rams. Thus, it
becomes necessary to
know the exact drill pipe length in the BOP 8 rams.
[00214] The present invention provides four different means to monitor the
material inside
the BOP 8 rams: a) Scanning the drill pipe with the rig floor AutoCV 10A and
/or the mid-level
AutoCV 10B and calculating the instantaneous drill pipe length in the BOP 8
rams using other
Deployment parameters such as, but not limited to, angle, direction, distance,
heave, position,
location, speed and similar items; b) Monitoring the BOP 8 rams with the
subsea AutoCV 10C;
c) preparing the drill pipe on the surface for a BOP 8 rams passive tool joint
monitor and d) uti-
lizing a mid-level AutoCV 10B passive or active mode or a combination thereof.
On the other
hand, providing two surface AutoCV 10As would most likely result in a double
failure, not in-
creased safety and reliability. In this particular example, a simple and less
expensive communi-
cator(s) increased the safety and reliability.
[00215] It may be seen from the preceding description that a novel
Autonomous Constant
Vigilance system and control has been provided that is simple and
straightforward to implement.
Although specific examples may have been described and disclosed, the
invention of the instant
application is considered to comprise and is intended to comprise any
equivalent structure and
may be constructed in many different ways to function and operate in the
general manner as ex-
plained hereinbefore. Accordingly, it is noted that the embodiments described
herein in detail for
exemplary purposes are of course subject to many different variations in
structure, design, appli-
cation and methodology. Because many varying and different embodiments may be
made within
the scope of the inventive concept(s) herein taught, and because many
modifications may be
made in the embodiment herein detailed in accordance with the descriptive
requirements of the
law, it is to be understood that the details herein are to be interpreted as
illustrative and not in a
limiting sense.
[00216] A computer program may evaluate the impact of the MUA Features
upon the
MUA by operating on the MUA Features, said operation guided by a database
constraints select-
ed at least in part from knowledge and/or rules and/or equations and/or MUA
historical data. The
41
CA 2964243 2017-04-12
AutoCV system may acquire Loads and Deployment Parameters by further
comprising of a data
acquisition system. A computer program may evaluate the impact of the Loads
and Deployment
Parameters upon the MUA by operating on the MUA Features, said operation
guided by a data-
base constraints selected at least in part from knowledge and/or equations
and/or rules. A com-
puter program may convert the MUA data to a data format for use by a Finite
Element Analysis
program (herein after referred to as "FEA"), also known as an FEA engine, or a
Computer Aided
Design program (herein after referred to as "CAD").
[00217] Regardless of the MUA name, which may comprise any of the above
mentioned
elements, AutoCV: a) scans the MUA to detect a plurality of Features; b)
recognizes the MUA
detected Features and therefore "knows by some detail" the MUA Features; c)
associates and
connects the recognized MUA Features with known definitions, formulas, risks
and MUA histor-
ical data, preferably stored in a database; d) creates an MUA mathematical
and/or geometrical
and/or numerical description compiled through the mathematical, geometrical
and numerical de-
scription of the MUA recognized Features (herein after referred to as
"Mathematical Descrip-
tion"); e) converts the MUA recognized Features into a data format for use by
an FEA and/or a
CAD program; f) calculates Feature change-chain and compares with stored
failure-chains for a
match; g) calculates a remediation to disrupt the Feature change- chain
(disrupt the failure-chain
early on) and h) updates the MUA historical data database.
[00218] The MUA Mathematical Description is then acted upon by the
theoretical Loads
and Deployment Parameters, sufficient for calculating an MUA FFS and RULE to
predict an
MUA behavior under deployment in accord with an embodiment of AutoCV operation
under
various loads, for example the loads result in bends of the riser, pipe, or
umbilical, for example
depending on the length and water currents. Furthermore, the MUA Mathematical
Description
may be converted to an MUA functional model or prototype which may be operated
to verify
MUA functionality directly and/or through a CAD program and/or through an FEA
program.
[00219] AutoCV assesses accurately the risk factors associated with the
specific riser joint
under the specific deployment loads and thus, it disrupts a failure-chain with
exact knowledge
that is continually updated.
42
CA 2964243 2017-04-12
[00220] In the event of an emergency, AutoCV preferably will announce the
emergency
and the corrective action in multiple languages preferably to match the native
languages of all
the crew members.
[00221] The flaw spectrum is then processed by a system of identifier
equations, as illus-
trated in Fig. 3B, resulting in a Mathematical Description of the MUA compiled
through the
Mathematical Description of its Features. At least one computer 20 utilizes
stored constraints
and/or knowledge and/or rules and/or equations and/or MUA historical data to
identify the nature
and/or characteristics of MUA Features so that at least one computer 20 knows
by some detail
the MUA Features and connects and associates the MUA Features with known
definitions, for-
mulas, Mathematical Description, PEA, CAD and similar items resulting in
Identification Coef-
ficient(s) Ki. It should be understood that Ki may be a number and/or an
equation, an array of
numbers and/or equations, a matrix, a table or a combination thereof (see Page
24)
[00222] At least one computer 20 further calculates and verifies that the
MUA is operating
within the safe-operating zone(s) of the operational-envelop.
[00223] Furthermore, comparison of historical data similar strings and
Failure-chains may
reveal a Feature change, a Feature morphology migration, a Feature propagation
and the calcula-
tion and identification of a subtle change-chain that matches an early stage
of at least one of
stored Failure-chains that may be disrupted through remediation before it
progresses to a Failure-
chain and eventually to an Accident-chain.
[00224] Computer 20 may further calculate a simpler flaw spectrum by
combining all Fea-
tures of a section, such as a circumference, into an equivalent flaw spectrum
using Ki, Kn, Kf
coefficients, stored formulas, charts, tables and historical data.
[00225] Fig. 4 illustrates the MUA resulting simpler flaw spectrum, a
Critically-Flawed-
Path (Herein after referred to as "CFP") (Critically-Flawed-Path and CFP are
trademarks of
STYLWAN). It should be understood that there is no physical correspondence
between the CFP
and the MUA Features as CFP is a mathematical construct that only preserves
the MUA perfor-
mance.
43
CA 2964243 2017-04-12
[00226] It is a unique feature of the present invention that the
Mathematical Description of
the MUA may be further manipulated to address system specific requirements and
to optimize
the system operation.
[00227] While tripping out of the well, AutoCV 10A would then scan the
drill pipe 7 and
compare the actual Features, FFS and RULE to the predicted Features by the
Assessment while
drilling and fine tune the Assessment through these continuous measurements.
[00228] It should also be understood that the lengthwise drill pipe
lengths are in reference
to the surface AutoCV 10A assessment head 12. At least one computer 20 through
data acquisi-
tion system 35 may measure Deployment Parameters such as, but not limited to,
angle, direction,
distance, heave, position, location, speed and similar items to calculate
instantly the location of
the surface assessment head 12 in reference to other locations such as the BOP
8 rams or a dog-
leg and therefore reference said flagged lengths to said other locations. This
calculation may be
utilized alone and/or may provide a backup for the subsea AutoCV 10C when one
is deployed. In
addition, AutoCV may calculate the drill pipe stretch using measured
Deployment Parameters
and Historical data.
[00229] The above is an example of how AutoCV may use data from one system
compo-
nent, the as-is drill string for example, to examine its impact on the overall
system. Another
unique and novel feature of the present invention is that it may also assess
the impact of the
overall process upon a component.
[00230] It should also be noted that AutoCV could utilize an accelerometer
and/or other
sensors to measure the sharpness of the storm jolts and bumps and convert them
to an estimated
aircraft (or watercraft) speed.
[00231] Another unique and novel feature of the present invention is the
functional model
of the as-is MUA that may be operated by the software. For example, the
software may close and
open a BOP 8 ram (will operate the software model of BOP 8) and verify that
the as-is BOP 8,
under the measured Loads and Deployment Parameters, is still operable. This
involves at mini-
mum, assembling a system using preferably the as-is components; calculating
the effects of the
Loads and Deployment Parameters on each component and verifying that there is
no undue dete-
rioration or interference between the components during the operation.
44
CA 2964243 2017-04-12
1002321 AutoCV would then be programmed to recite the findings to the
operator and the
crew and suggest corrective actions to disrupt the failure-chain.
1002331 It should be understood that the present invention Assessment of
complex MUA
(complex system) starts with the complex MUA analysis to define the
operational-envelope of
the sub-systems and the components and then, to define failure-chains. It may
take multiple itera-
tions to complete this first step. Then, Assessment scans and measures the
components with suf-
ficient resolution so that Assessment knows by some detail the as-is component
structure, its Fit-
ness-For-Service (herein after referred to as "FFS") and its Remaining-Useful-
Life (herein after
referred to as "RUL") within its operational-envelop. FFS estimation is herein
after referred to as
"FFSE" and RUL estimation is herein after referred to as "RULE". Assessment
then closes the
loop by starting from the simplest components and progress upwards in
complexity. Assessment
may assemble and assess an as-is sub-system and eventually the complex MUA by
assembling
the as-is components into an MUA functional model.
[00234] For example, an offshore drilling rig is a sea going vessel that
comprise of most
MUA listed above including, but not limited to BOP, casing, CT, DP, engine,
pump, Riser, struc-
ture, tensioner each further comprising, at least in part, of simpler
components such as beam, en-
closure, fastener, frame, piston, rod and tube.
[00235] Loads act upon the "as-built" and/or "as-is" MUA features
impacting its FFS and
RULE. A list of MUA features includes, but is not limited to, ballooning,
blemish, blister,
boxwear, coating, collar, corrosion, corrosion-band, coupling, crack, crack-
like, critically-
flawed-area (herein after referred to as "CFA"), critically-flawed-path
(herein after referred to as
"CFP"), cross-sectional-area (herein after referred to as "CSA"), defect,
deformation, dent, den-
sity, dimension, duration, eccentricity, erosion, fatigue, flaw, geometry,
groove, groove-like,
gauge, gauge-like, hardness, key-seat, lamination, loss-of-metallic-area
(herein after referred to
as "LMA"), metallic-area, mash, misalignment, neck-down, notch, ovality,
paint, pit, pitting-
band, pit-like, profile, proximity, rodwear, scratch, seam, sliver,
straightness, stretch, surface-
finish, surface-profile, taper, thickness, thread, threaded-connection, tool
joint, wall, wall-
thickness, wall-profile, wear, weld, wrinkles, a combination thereof and
similar items, (herein
after referred to as "Features").
CA 2964243 2017-04-12
[00236] An MUA Feature that was not in the MUA design is herein after
referred to as
"Imperfection". Imperfections are undesirable and often arise due to
fabrication non-compliance
with the design, transportation, deployment conditions, mishaps and MUA
degradation. An Im-
perfection that exceeds an alert-threshold is herein after referred to as
"Flaw". Typically a Flaw
places the MUA in the category of in-service monitoring. An Imperfection that
exceeds an
alarm-threshold is herein after referred to as "Defect".
[00237] In addition, it should be understood that even MUA that is free of
any damage
may still be unfit for service in a particular application and/or deployment
as design assumptions
and/or knowledge, such as Mean-Time-Between-Failures (herein after referred to
as "MTBF")
and similar measures and/or statutory requirements, and/or operating
conditions and/or mishaps
may render the MUA unfit for service. This is the reason FFS and RUL
estimation should pref-
erably monitor and/or measure MUA deployment parameters, a non-limiting list
involving one
or more of absorption, AC parameters, acceleration, amplitude, angle,
brittleness, capacitance,
conductivity, color, critical-point temperature, cyclic loading, DC
parameters, deformation, den-
sity, depth, diameter, dimension, direction, distance, ductility, ductile-
brittle transition tempera-
ture, eccentricity, eccentric loading, echo, flow, flow rate, fluid level,
force, frequency, geome-
try, impedance, heave, horsepower, image, impedance, impulse, inductance,
length, loads, load
distribution, location, longitude, misalignment, moments, motion, number of
cycles, number of
rotations, number of strokes, opacity, ovality, penetration rate,
permeability, ph, phase, plastic
deformation, position, power, power consumption, pressure, propagation,
proximity, radius, re-
flectivity, reluctance, resistance, rotation, rpm, shear, size, sound,
specific gravity, speed, static
loading, strain, stress, temperature, tension, thermal loading, torque,
torsion, twisting, velocity,
vibration, volume, wave, weight, weight on bit, width, relative values of the
above, combina-
tions of the above and similar items (herein after referred to as "Deployment
Parameters").
[00238] Maintenance
[00239] Typically MUA is maintained on an interval, such as time or number
of cycles,
commonly referred to as preventive maintenance. Predictive maintenance
theoretically uses a
data analysis to determine when the MUA requires maintenance. Theoretically,
this approach
appears to be more efficient and cost effective. In practice however,
predictive maintenance re-
quires MUA diagnostic data and detailed knowledge of the MUA deployment loads
that, at best,
46
CA 2964243 2017-04-12
are difficult and/or expensive to obtain resulting in over maintaining MUA
that does not need
maintenance and under maintaining MUA that does need maintenance. Predictive
maintenance is
not a realistic option for most MUA and would most likely result in repair
maintenance because
of the lack of useful data. Repair maintenance refers to MUA that is used
until it fails. Lack-of-
(detailed) knowledge of the as-is MUA n is the weakest link among all the
maintenance pro-
grams which primarily rely on inspection, such as Non-Destructive Inspection
(herein after re-
ferred to as "NDI"). NDI is also referred to as Non-Destructive Evaluation and
as Non-
Destructive Examination, both shortened to "NDE" in the literature.
[00240] The following further provides additional information regarding
use of the present
invention with risers and umbilicals as used in offshore operations so that
the Riser stress-
engineering-assessment equipment, referred to herein as "RiserSEA, is a more
specific embodi-
ment of Autonomous Constant-Vigilance System, referred to herein as AutoCV.
[00241] Referring now Fig. 5A, Fig. 5B, and FIG. 5C, there is shown a
floating drilling rig
101 with a Riser string extending to the blowout preventer 104. For
illustration purposes the Ris-
er string comprises of the telescopic joint 102 and Riser joints 103. Riser
joints comprise of
joints without buoyancy 103A, joints with buoyancy 103B and joints with
instrumentation 105.
During deployment, the Riser string may be treated as a slender flexible
structure without inher-
ent stability.
[00242] Fig. 6A and Fig. 6B illustrates the end area (coupling) of a
typical marine drilling
riser joint comprising of the main tube 110, hereinafter referred to as "MT",
and the auxiliary
lines, hereinafter referred to as "AUX". The AUX lines comprise of the Choke
and Kill lines 111
hereinafter referred to as "C&K", the Booster line 112 and the hydraulic line
113. Riser joints
without any AUX lines or different combinations of AUX lines are also in use.
[00243] A Riser under deployment is subjected to multiple static, dynamic,
transient and
cyclic Loads from applied tension, pressure, rig motion, sea currents, weight
of fluids and gases
(drilling, production, control), waves, wind and similar items, in addition to
the biological, chem-
ical, electrochemical and mechanical actions of the environment and the
drilling, control and
production fluids and gases, hereinafter after referred to as "Actions".
Actions are mostly time
dependent deterioration processes excluding accidents, such as a collision.
The utilization of Ris-
47
CA 2964243 2017-04-12
ers in greater water depths amplifies significantly the effects of the Loads
and Actions. Calcula-
tion details that until recently could be omitted, are now becoming important.
However, the Riser
1D-NDI spot-checks and analysis still relies on old concepts, addressing old
materials that do not
reflect the modern day needs of deepwater Riser deployment and use.
[00244] A partial list of variables that influence the Riser integrity
comprise of: a) Pres-
sure; b) Geometry (diameter, wall thickness, ovality); c) material properties
such as composition,
yield strength and other; d) shape and neighborhood of Imperfections and e)
Loads and Actions.
[00245] As the water depth increases, Riser designs share the Loads
between the MT and
the AUX, thus significantly complicating the RiserSEA that should also
calculate the MT and
AUX multidimensional stresses corrected for the MT and AUX material properties
and geome-
try.
[00246] Fig. 7 illustrates one embodiment of the RiserSEA comprising of at
least one
computer 220, at least one deployment parameters acquisition system 230 and at
least one stress-
significant-imperfection (hereinafter referred to as "SSI") acquisition system
240. Examples of
deployment acquisition system 230 and acquisition system 240 are shown in my
previous pa-
tents. In this example, riser 103, which are types of risers 103A or 103B, is
being examined, typ-
ically each tube of one riser at a time with each of the risers separate and
available for examina-
tion, such as at a depot as indicated in Fig. 6B. SS1 scanner 50 is run
through each of the tubes
110, 111, 112, and 113 of each riser. Once this is done, the combination of
information can be
utilized as explained above, to determine the fitness of the riser (or
umbilical), what type of
bends it can sustain, whether it should be removed or possibly placed where
less bending will
occur. This process could involve transporting the mathematical description of
the riser to an
FEA model where an analysis is made utilizing anticipated stresses applied to
the riser. Using
such an analysis, or other measurements, a Riser fitness Certificate can then
be issued based on
the results of the testing as indicated in FIG. 9A. In FIG. 9A, it will be
seen that wall thickness
is measured for each tube (such as center tube 110), minimal wall thickness
variations, cross-
sectional variations, estimated remaining strength, and the like.
[00247] It should be understood that SS1 detection may include, but is not
limited, to the
API 16F "geometric stress amplifiers" and ASME B31.4 "stress intensification
factors". Com-
48
CA 2964243 2017-04-12
puter 220 comprises of a local and/ or remote display 221, keyboard 222,
permanent or remova-
ble storage, local and/or remote speaker 223 and/or earphone, local and/or
remote microphone
224 and at least one communication link 225. The deployment parameters
acquisition system
230 and SSI acquisition system 240 monitor sensors distributed around the rig
1, including but
not limited to acoustic, barcode, chemical, color, conductivity, current,
deformation, density,
depth, density, direction, distance, eddy-current, electrical, EMAT, field,
flow, flux-leakage,
force, frequency, geometry, laser, length, level, location, motion, magnetic,
optical, physical
properties, pressure, rate, rfid, reluctance, resistance, rig motion, rpm,
speed, stress, temperature,
time, vibration, voltage, weight, similar items and combinations thereof
and/or along with the
instrumentation 205 on the riser joints.
[00248] Instrumentation 205, if utilized, comprises sensors for the above
listed items that
measure these items on the deployed risers so that instrumentation 205
effectively comprises SSI
sensors. Wiring connections, umbilicals, acoustic mud modems, and the like,
may be utilized to
connect to/from RiserSEA surface processors 220 (or processors in AutoCV 10A,
10B riser pro-
cessors, 10C subsurface processors) and the instrumentation 205 in the
risers/umbelicals.
[00249] In one embodiment, each riser or selected risers in the riser
string would include
an instrumentation 205. At a minimum, the instrumentation 205 could be used to
determine the
overall angles of the deployed riser string and/or stresses on the riser
string 3 as indicated by the
bends shown in FIG. 1 or Fig. 5A. The SSI acquisition system 40 may induce
programmable ex-
citation into the SSI scanner 50 and monitor the SSI sensors.
[00250] SOLVING THE ELASTICITY EQUATIONS
[00251] The main function of RiserSEA is to calculate Riser stress and
strain. In the study
of elasticity, stress and strain are typically expressed as systems of (x, y,
z) partial differential
equations that can be found throughout the literature along with some
solutions using boundary
conditions. A simpler approximation is to replace the partial differential
equations with partial
difference equations as published by C. Runge (Z. Math. Phys. Vol. 56, p.225,
1908) or, prefera-
bly, even simpler equations or look-up tables. Reference 3, Appendix C
"Compendium of Stress
Intensity Factor Solutions" provides a number of practical approximations and
solutions.
49
CA 2964243 2017-04-12
[00252] The selection of the RiserSEA sensors and sensor configuration 351
for SSI scan-
ner 350, shown in FIG. 8, starts by defining the SSI parameters that are Riser
integrity-
significant and stress-significant. This involves solving the stress equations
for the multitude of
SSI parameters and defining the minimum value(s) to be detected early on so
preventive mainte-
nance can be effective. This may involve FEA, test samples, experimentation or
a combination
thereof
[00253] Therefore, the main function of computer 220 is to acquire a
sufficient number of
good quality specific SSI data from the sensor array of SSI scanner 350
through the SSI acquisi-
tion system 240(see for example our prior applications for more details); to
process and translate
the data to an individual Riser 103 or other OCTG description; store said
description in a
lengthwise format; derive the Riser 103 boundaries; acquire Riser 103
deployment parameters
through the deployment parameters acquisition system 230 and solve the
elasticity equations to
decide if Riser 103 is still fit for deployment in a string location, should
be moved to another
string location, should be re-rated, should be removed from deployment for
remediation or be
retired from service. Computer 220 may further suggest the type of remediation
to return Riser
103 to service.
[00254] Figure 8 illustrates aMxN addressable two-dimensional (hereinafter
referred to
as "2D") sensor array 251 of physical sensors, hereinafter referred to as
"Sensors" or "SM,N
preferably installed on the inside or outside of the SSI scanner 250 or both.
It should be under-
stood that M and N represent the number of sensors that provide 100%
inspection coverage and,
therefore, the greater the OCTG size the greater the number of sensors for
constant resolution. A
three-dimensional (hereinafter referred to as "3D") sensor array comprises of
at least two stacked
sensors, such as SM,2, or a partial or complete 2D sensors arrays. 3D sensors
are addressed as
SL,M,N. The sensor arrays are preferably deployed with length measurement or
time measure-
ment converted to the length of the Riser pipe or other OCTG. In other words,
scanner 250 is
lowered through each tube 110, 111, 112, 113 of each individual riser such as
when the risers are
on the surface.
[00255] It should be understood that a particular sensor array 251 may
comprise similar or
different types of sensors and that each type of sensor may require a
different type of fixed or
programmable excitation from the SSI acquisition system 240. The excitation
may be deployed
CA 2964243 2017-04-12
inside SSI scanner 250, may be separately applied on the inside or outside of
Riser 103, may be
applied as a bias prior to the scan or any combination thereof. It should
further be understood
that the fixed or programmable excitation and the Sensors may be disposed on
the inside of a
Riser 3 pipe(s), the outside of a Riser 3 pipe(s) or any combination thereof.
[00256] CONFIGURING THE SENSOR ARRAY
[00257] Each inspection technique has advantages and disadvantages. Most
require thor-
ough cleaning of the Riser 103 and/or the removal of paint/coating and the re-
application of
paint/coating after the inspection. Again, this generates air and water
contaminants in addition to
high cost and low productivity. Once the inspection technique and the
sensor(s) are selected, a
number of Riser test samples with a number of pertinent preferably natural or
man-made SSI
may be used to define the excitation, sensor(s) mounting, detection range,
sensor array configu-
ration and the required signal processing. The sensor(s) excitation, detection
range, the SSI sen-
sor array configuration and signal processing would then define the spacing
among sensors and
the overall configuration of the sensor array 251. It should be understood
that this process may
be fine-tuned through a number of iterations.
[00258] SENSOR ARRAY SIGNAL PROCESSING
[00259] Computer 220 signal processing may address, read and combine
signals from any
of the Sensors from array 250 as shown in Equation 1 (70) through Equation 4
(73) resulting in
virtual sensors, hereinafter referred to as "VSensor" or "VSN".
[00260] VS(70) = K * (S3,2 ¨ S2,2)
(Eq. 1)
[00261] VS(01) = S3,1 + S3,2 + +
S3,N (Eq. 2)
[00262] VS(Olavg) = VS(01) / N
(Eq. 3)
[00263] VS(73) = AiRSN,1)2 + (SN,3)2]
(Eq. 4)
[00264] Equations 1, through 4 and other equations may be a) hardwired
using analog
components such as amplifiers, filters, adder/subtractor 252,
multiplier/divider 253, integra-
tor/differentiator, similar items and combinations thereof; b) analog
computers such as the [254,
252, 255] processing array; c) implemented in software by a digital signal
processor (60) with at
least one analog front end, hereinafter referred to as "DSP"; d) implemented
with field-
51
CA 2964243 2017-04-12
programmable-gate-array, hereinafter referred to as "FPGA" or any combination
thereof Con-
stant K may be of fixed value, variable value through a potentiometer,
variable or fixed value
under computer 220 controls or DSP 260 control or any combination thereof
[00265] The VSensor signals preferably correspond to different types of
SSI and/or may
form a system of equations that allows for the calculation of SSI critical
parameters. It should be
understood that certain physical sensors may be omitted, be replaced by
VSensors or any combi-
nation thereof For example, VS (273) may be an adequate replacement for S (N,
2) thus elimi-
nating physical sensor S (N, 2), or allowing for a different type of sensor to
be installed in the
physical location S (N,2) generating signal 272. The relationship of Signals
272 and 273, gener-
ated by different types of sensors that are focused on the same location, may
provide additional
detailed knowledge about the material condition through the solution of a
system of equations.
[00266] It should also be understood that sensor processing similar to the
[VS(273), 272]
pair or any other combination thereof may be reproduced in all three
dimensions, thus giving rise
to systems of multiple equations focused on specific material locations or
material characteris-
tics. For example, S(2,2) may be reproduced in one direction by \i[(S2,1)2 +
(S2,3)2] and in an-
other direction by 4[(S1,2)2 + (S3,2)2], the combination of all three signals
giving rise to a an-
other system of equations and a more-focused VSensor. Small area resolution
requires fine-focus
sensors, physical or virtual, that may be calculated by combining adjacent
physical sensors such
as above or even more focused such as the VSensor A(S2,1)2 + (S2,2)2].
[00267] It should be apparent from the above that finer resolution results
in a higher num-
ber of systems of equations that must be solved simultaneously and therefore,
finer resolution
requires much higher processing speed. It should also be understood that not
all signals are use-
ful all the time. For example, in one instance signal 270 may be meaningful
and significant while
in another instance signal 275 may be meaningful and significant. Instead of
relying on comput-
er 220 for the entire signal processing, a distributed approach, as shown in
FIG. 4, is a preferable
method to increase processing speed. For example, instead of computer 20
digitizing and pro-
cessing signals 272 and 273, a local DSP 61 may digitize and process the
signals and alert com-
puter 220 only when signal 274 is meaningful and significant. It should also
be understood that a
single FPGA may comprise of multiple DSPs.
52
CA 2964243 2017-04-12
[00268] Again, it should be understood that the sensor array would
comprise of a suffi-
cient number of sensors and processing elements to provide 100% inspection
coverage and,
therefore, the greater the OCTG size the greater the number of sensors for
constant resolution. It
should further be understood that the number and configuration of Sensors 51
and signal pro-
cessing should acquire a sufficient number of good quality specific data to
facilitate the Ris-
erSEA calculation of maximum stresses and strains. Computer 20 may further use
the DSPs 60,
61, 62 for fast processing of the stresses and strains.
[00269] SENSOR ARRAY ASSEMBLY
[00270] Metallurgy and fatigue signal comprise critical SSI parameters.
They are mostly
very low magnitude, typically order(s) of magnitude lower than signals from
visible Imperfec-
tions. In order to detect and recognize such critical signals, the Sensor
array must maintain a con-
stant 3D relationship with the excitation inducer, a constant 3D relationship
among the Sensors, a
constant 3D shape and preferably exhibit no resonance frequencies within the
range of SSI. It
should be noted that the ride chatter of the sensors in US Patent 2,685,672
overshadows the met-
allurgy and fatigue signals. The ride chatter is the result of the spacing
variations between the
sensor and the material.
[00271] The final RiserSEA sensor array 251 configuration would most
likely be complex
resulting in a complex sensor holder that is best manufactured through
machining, molding, ad-
ditive manufacturing, similar techniques and combinations thereof. The sensor
holder may com-
prise of a single or multiple segments. Additive manufacturing, such as using
a 3D printer, al-
lows for greater assembly flexibility, customization and rapid production. For
example, the 3D
printer may be paused; dimensions may be measured and adjusted; components,
including but
not limited to cooling, electronics, heating, hydraulics, pneumatics,
sensor(s), storage and wiring
may be installed; 3D printing may resume and be paused again for adjustments
and the installa-
tion of additional components and so on and so forth until the Sensor array or
a segment is com-
pleted.
[00272] The testing and qualification of the completed Sensor array may
include but is not
limited to detection testing, electrical testing, environmental testing,
isolation testing, insulation
testing, mechanical testing, scanning speed testing, and testing for resonance
frequencies similar
53
CA 2964243 2017-04-12
tests and combinations thereof. These tests would result in calibration
coefficients that noimalize
the performance of the Sensor assembly including, but not limited to,
resonance frequencies cor-
rection factors. The Sensor calibration coefficients may be stored on non-
volatile storage
onboard the Sensor array, on portable storage, on an on-line secure database,
similar items and
combinations thereof.
[00273] SYSTEM SIGNAL PROCESSING
[00274] Again, computer 220 would preferably assemble and solve the Riser
103 elasticity
equations using the good quality specific data that are sufficient in number
to facilitate the Ris-
erSEA calculation of maximum Riser stresses and strains.
[00275] Good Quality Specific Data: The selection of the RiserSEA sensors
and sensor
configuration 251 starts by defining the minimum SSI parameters that is stress-
significant. This
involves solving the stress equations for the multitude of specific SSI
parameters and defining
the minimum value(s) to be detected. It should be noted that the remaining-
wall-thickness alone
is just one of the parameters, not the ultimate decision yardstick.
[00276] Good Quality: Refers to data resolution, such as pre-processing,
sampling rate,
the analog-to-digital conversion bits and SSI detection repeatability. It
should be understood
therefore that the definition of good quality is Imperfection specific.
[00277] Sufficient number of Inspection Data: A Sufficient Number of good
quality spe-
cific data refers to Inspection Coverage, the volumetric percent coverage of
each Riser pipe and
subsystem. Inspection Coverage preferably may be defined by the combination of
minimum SSI
parameters to be detected, the detection sensor configuration and the desired
scanning speed (one
of the financial considerations along with the transportability and ease of
deployment of the Ris-
erSEA equipment).
[00278] Often, the inspection technique and/or the detection sensors are
the controlling
factors that redefine the minimum SSI parameters that can be detected. The
minimum detectable
SSI parameters are preferably defined as a geometric function of wall
thickness (T) like (0.05*T)
L x (0.05*T) W x (0.1*T) D (Length, Width and Depth) that may then be
translated to a VSensor
equation(s). The following examples discuss the Inspection Coverage of a 21.0"
OD, 75' length
MT with 0.750" wall thickness. The inspection is performed from the ID:
54
CA 2964243 2017-04-12
[00279] Sensor overlap method: A 20% sensor reading overlap with a 0.5"
diameter sen-
sor (typical Ultrasonic sensor) results in one reading every 0.4" or a total
of about 346,500 read-
ings for 100% MT inspection coverage.
[00280] Minimum SSI dimensions: Assuming that the minimum SSI dimensions
were
calculated as 1.0" x 1.0" x 0.05", it would translate to about 109,800
readings for 100% MT in-
spection coverage.
[00281] Number of readings per minimum SSI: It is preferable that a
minimum of 2 read-
ings per minimum SSI are obtained resulting in about 219,600 readings for 100%
inspection
coverage (from the ID). The minimum number of readings threshold is typically
set between 5
and 9 in order to eliminate false sensor readings.
[00282] API 579-1/ASME FFS-1 formula 4.1: Although 4.1 addresses General
Metal
Loss, not stress analysis, it could be used as a starting point resulting in
one reading every 1.29"
or about 33,500 readings for the detection of MT general Metal loss. Requiring
a minimum of
20% sensor overlap would result in about 52,400 readings. Requiring a minimum
of 2 readings
would result in about 105,000 readings for 100% MT inspection coverage.
[00283] Once the number of sufficient readings is established, the
scanning speed (produc-
tion rate) may be calculated from the data acquisition speed of the RiserSEA
or the RiserSEA
may be designed to meet the required scanning speed. Again, one way to
increase the production
rate (scanning speed) is through distributed signal processing whereby analog
computers, dis-
creet logic; DSP(s), FPGA(s) and ASIC process certain signals, solve certain
equations or any
combination thereof as shown in FIG. 8.
[00284] As discussed earlier, RiserOEMs preferably take four (4)
Ultrasonic wall thick-
ness readings (90o apart) around the MT circumference every two (2) to five
(5) feet of length.
The maximum number of readings on a 75' joint MT would then be 152 readings,
four readings
every 2'; indeed an insufficient Inspection Coverage for stress-analysis or
even General Metal
Loss fitness calculations.
[00285] CALCULATING STRESSES
CA 2964243 2017-04-12
[00286] A unique and novel feature of the present invention is the tuning
of the Sensor
250 configuration and excitation, the signal pre-processing, the sampling rate
and the final pro-
cessing to the specific characteristics of SSI Imperfections to facilitate and
optimize the solution
of the stress and strain equations by substituting the equation(s) variables
with processed sensor
signals. For example, the CSA of each Riser joint MT may be calculated from
the inspection data
by one or more of VS(01) (Eq. 2), VS(Olavg) (Eq. 3) and other equations using
absolute, aver-
age, corrected, coverage, differential, integral, local, maximum (peak),
minimum and remaining
values, rate of change values, time dependent values, similar items and
combinations thereof.
Again, the calculated CSA and other calculated values of each Riser joint may
be stored in a
lengthwise array in computer 20 memory. Rate of change values, time dependent
values and oth-
er ratios, differences, propagation and similar items may be calculated from
the stored Riser joint
lengthwise arrays of prior inspections.
1002871 In another example, stress is defined as (a = Force/Area); where
Area may be
substituted by the calculated CSA of each Riser joint. Force may comprise of
one or more of
bending, buckling, compression, cyclic loading, deflection, deformation,
drilling induced vibra-
tion, dynamic linking, dynamic loading, eccentricity, eccentric loading,
elastic deformation, en-
ergy absorption, feature growth, feature morphology migration, feature
propagation, impulse,
loading, misalignment, moments, offset, oscillation, plastic deformation,
propagation, shear, stat-
ic loading, recoil, strain, stress, tension, thermal loading, torsion,
transient loading, twisting, vi-
bration, vortex induced vibration and a combination thereof. A force, such as
tension, may be
monitored in real-time by deployment parameters acquisition system 30, thus,
by monitoring the
Riser instantaneous tension, the instantaneous stress may be calculated for
each Riser joint in the
string. Alarm(s) may be raised when the calculated stresses exceed preset
levels.
[00288] The stored CSA values along with all other stored values of each
Riser joint may
be used to arrange the Risers into a Riser string. When the string
configuration is completed,
computer 20 may automatically create a string model using the joint
identification and its loca-
tion in the string translated to water depth. With the mud density known,
computer 20 may calcu-
late, for example, hoop and other stresses for each Riser joint in the string.
[002891 It should be understood that computer 20 may calculate multiple
solutions before
reaching an optimal solution. Computer 20 may be programmed with assessment
procedures and
56
CA 2964243 2017-04-12
Stress and Strain equations and approximations found in the literature,
including but not limited,
to the following references.
[00290] API 16F Section 5: Design
[00291] API 16F Section 17: Operation and Maintenance Manuals
[00292] API 16F Appendix A: Stress Analysis
[00293] API 16F Appendix B: Design for Static Loading
[00294] API 16F Appendix D: Bibliography
[00295] API 16Q Section 3: Riser Response Analysis
[00296] API 16Q Appendix B: Riser Analysis Data Worksheet
[00297] API 16Q Appendix D: Sample Riser Calculations
[00298] API 16Q Appendix F: References and Bibliography
[00299] API 579-1 / ASME FFS-1 is herein below referred to as "API 579"
[00300] API 579 Section 2: Fitness-For-Service Engineering Assessment
Procedures
[00301] API 579 Section 3: Assessment of Equipment for Brittle Fracture
[00302] API 579 Section 4: Assessment of General Metal Loss
[00303] API 579 Section 5: Assessment of Local Metal Loss
[00304] API 579 Section 6: Assessment of Pitting Corrosion
[00305] API 579 Section 7: Assessment of Blisters and Laminations
[00306] API 579 Section 8: Assessment of Weld Misalignment and Shell
Distortion
[00307] API 579 Section 9: Assessment of Crack-Like-Flaws
[00308] API 579 Section 12: Assessment of Dents, Gouges and Dent-Gouge
Combina-
tions
[00309] API 579 Appendix B: Stress Analysis overview for a FFS
Assessment
[00310] API 579 Appendix C: Compendium of Stress Intensity Factor
Solutions
57
CA 2964243 2017-04-12
[00311] API 579 Appendix D: Compendium of Reference Stress Solutions
[00312] API 579 Appendix E: Residual Stress in Fitness-For-Service
Evaluation
[00313] API 579 Appendix F: Material Properties for an FFS Assessment
[00314] API 579 Appendix G: Deterioration and Failure Modes
[00315] ASME B31.4 Chapter II Design
[00316] ASME B31.402 Calculation of Stresses
[00317] ASME B31.403 Criteria
[00318] ASME B31.4 Chapter VI Inspection and Testing
[00319] ASME B31.4 Chapter VI I Operation and Maintenance Procedures
[00320] ASME B31.4 Chapter IX Offshore Liquid Pipeline Systems
[00321] As the water depth increases, Riser designs share the tension
between the MT and
the AUX, thus significantly complicating the RiserSEA. For example, sea
currents bend the riser
string as illustrated in Fig. 5A. When pipe bends, its major-axis is under
tension and its minor-
axis is under compression. In order to minimize the stored energy, the pipe
assumes an oval
shape, referred to as out-of-roundness or ovality. When the Loads are shared
between the MT
and the AUX, one of the AUX lines could be on the outside of MT's major axis
(under higher
tension) and one on the outside of MT's minor axis (under higher compression).
In order to min-
imize those stresses, the Riser joint would tend to rotate in order to place
the AUX in the neutral
axis thus resulting in multidimensional stress. Furthermore, each AUX would
also bend and thus
it would undergo ovality under the influence of higher tension and
compression. Therefore, Ris-
erSEA must also translate the MT bending stresses to AUX multidimensional
stresses corrected
for the AUX material properties and geometry.
[00322] Scan the Riser -
[00323] Recognize Features and deterioration mechanism ¨
[00324] Apply time-depended deterioration mechanism correction factors
resulting in up-
dated inspection data.
58
CA 2964243 2017-04-12
[00325] Use the Formulas in Fig. 3A and 3B to calculate Critical Deployment
Parameters
for each Riser joint using the updated inspection data.
[00326] Create a Riser string Model using the Critical Deployment
Parameters of each
Riser joint and calculate Critical Deployment Parameters for the Riser String.
[00327] Monitor Deployment Parameters and calculated Maximum Stresses.
[00328] Alarm if Maximum stresses exceed a preset Threshold.
[00329] RISER FITNESS CERTIFICATE
[00330] As discussed earlier, Fig. 9A and Fig. 9B illustrates a fitness
certificate, with Fig.
9B showing readings on, for example, riser 10. The certificate duration is set
to 75% of the Riser
estimated remaining useful life. Readings may be made for each of the pipes as
indicated by
MT, C, K and B (main tube 110, two choke and kill lines, 111, 111, booster
line 112) wherein
the nominal outer diameters and wall thickness are known. Various parameters
are measured
from each tube. FIG. 9B shows various information including a graph of the
wall thickness pro-
file for the main tube. The main tube is the main load bearing structure of
the riser. The analysis
may comprise use of the critically flawed path of FIG. 4.
[00331] FIG. 10 shows export of measured data to an FEA engine screen is
shown. A res-
olution is selected. A type of FEA analysis is selected. CFP refers to
critically flawed path.
[00332] FIG. 10 shows a particular type of signals that may be produced by
the system
shown in FIG. 2 but the invention is not limited to particular types of
signals but any signals pro-
duced in conjunction with such an analysis that are then used for export to an
FEA machine. In
this case, 3-W signals refers to signals related to thickness changes, tapers,
rodwear, and so forth
regarding general and local metal loss. 3-T signals refer to metallurgy,
hardness changes, corro-
sion, pitting, critically flawed areas, and so forth. 2-T signals measure
approximately 1/8 inch
regarding local metal loss, pitting corrosion, blisters and laminations
regarding pitting corrosion,
crack-like flaws, and fatigue.
[00333] The various types of FEA analysis creates a theoretical string and
subjects the
theoretical string to various theoretical forces, e.g. bending, tension,
torsion, and vibration, to test
the theoretical string. However because the string is based on as-is measured
values (rather than
59
CA 2964243 2017-04-12
the values when manufactured) the analysis is representative of actual strings
that have wear due
to use as detected by the signals discussed above. The resolution is selected
where smaller reso-
lution requires longer FEA analysis.
[00334] A
system of one or more computers can be configured to perform particu-
lar operations or actions by virtue of having software, firmware, hardware, or
a combination of
them installed on the system that in operation cause the system to perform the
actions. One or
more computer programs can be configured to perform particular operations or
actions by virtue
of including instructions that, when executed by data processing apparatus,
cause the apparatus
to perform the actions. One general aspect includes a riser assessment system
of an as-is riser
system including a riser string formed by a plurality of risers, each riser
including a central tube
and a plurality of peripheral tubes parallel to said central tube, including:
a computer with stor-
age, data entry, data readout and communication means; at least one sensor
with an output in
communication with said computer; a database; and calculation software to
calculate maximum-
stresses using said output to determine if said riser string is still fit-for-
deployment or should be
removed from deployment Other embodiments of this aspect include corresponding
computer
systems, apparatus, and computer programs recorded on one or more computer
storage devices,
each configured to perform the actions of the methods.
[00335]
Implementations may include one or more of the following features. The
riser assessment system where said riser features and properties include at
least one of color,
conductivity, corrosion, composition, crack-like-flaws, defects, deformation,
depth, density, fa-
tigue, flaws, geometry, geometric-distortion, groove-like-flaws, hardness,
imperfections, metal-
lurgy, misalignment, pit-like-flaws, reluctance, wall thickness, wear, weight,
stress-
concentrators, geometric stress amplifiers, similar items and combinations
thereof. The riser as-
sessment system where said loads include at least one of bending, buckling,
compression, cyclic
loading, deflection, deformation, depth, drilling induced vibration, dynamic
linking, dynamic
loading, eccentricity, eccentric loading, elastic deformation, energy
absorption, feature growth,
feature morphology migration, feature propagation, impulse, loading,
misalignment, moments,
offset, oscillation, plastic deformation, propagation, shear, static loading,
recoil, strain, stress,
tension, thermal loading, thickness, torsion, transient loading, twisting,
vibration, vortex induced
vibration, weight, any static, dynamic, transient and cyclic combinations
thereof and similar
CA 2964243 2017-04-12
items. The riser assessment system where said parameters include at least one
of actions of drill-
ing, actions of the environment, applied tension, biological, chemical,
composition, depth, densi-
ty, deterioration, dimensions, electrochemical, geometric dimensions and
shape, mechanical, in-
ternal and external pressure, rig motion, sea currents, shape, waves, wind,
weight of fluids and
gases (drilling, production, control), yield strength combinations thereof and
similar items. Im-
plementations of the described techniques may include hardware, a method or
process, or com-
puter software on a computer-accessible medium.
[00336] In one embodiment, a finite-element-analysis system is provided
that may com-
prise at least one computer, at least one material features acquisition system
for the at least one
computer, at least one memory storage for the at least one computer, wherein
the at least one ma-
terial feature can be stored, and a feature recognition program using at least
one of algorithms,
charts, equations, look-up tables and similar items stored in the at least one
memory storage and
executed by the at least one computer to perform at least one of detect,
measure, distinguish,
recognize, identify and connect the at least one material feature with known
definitions and for-
mulas stored in the at least one memory storage resulting in a one, two or
three dimensional
mathematical description of the at least one material feature. A finite
element analysis program
capable of a plurality of solutions is executed on the at least one computer
to analyze the mathe-
matical description of at least one material feature under a plurality of
loads and deployment pa-
rameters.
[00337] The finite-element-analysis system may work many types of material
including
but not limited to at least one of aircraft, beam, bridge, blowout preventer,
bop, boiler, cable, cas-
ing, chain, chiller, coiled tubing, chemical plant, column, composite,
compressor, coupling,
crane, drill pipe, drilling rig, enclosure, engine, fastener, flywheel, frame,
gear, gear box, gen-
erator, girder, helicopter, hose, marine drilling and production riser, metal
goods, oil country
tubular goods, pipeline, piston, plate, power plant, propeller, pump, rail,
refinery, rod, rolling
stoke, sea going vessel, service rig, storage tank, structure, sucker rod,
tensioner, train, transmis-
sion, trusses, tubing, turbine, vehicle, vessel, wheel, workover rig,
subsystems of the above,
components of the above, combinations of the above and similar items.
[00338] The material features may include but not be limited to at least
one of balooning,
blemish, blister, boxwear, coating, collar, corrosion, corrosion-band,
coupling, crack, crack-like,
61
CA 2964243 2017-04-12
critically-flawed-area, cfa, critically-flawed-path, cfp, chemistry, cross-
sectional-area, csa, de-
fect, deformation, dent, density, dimension, duration, eccentricity, erosion,
fatigue, flaw, geome-
try, groove, groove-like, gauge, gauge-like, hardness, key-seat, lamination,
loss-of-metallic-area,
lma, metallic-area, mash, misalignment, neck-down, notch, ovality, paint, pit,
pitting-band, pit-
like, profile, proximity, rodwear, scratch, seam, sliver, straightness,
stretch, surface-finish, sur-
face-profile, taper, thickness, thread, threaded-connection, tool joint, wall,
wall-thickness, wall-
profile, wear, weld, wrinkles, a combination thereof and similar items.
1003391 The plurality of PEA solutions or theoretical loading comprise at
least one of
bending, buckling, compression, cyclic loading, deflection, deformation,
dynamic linking, dy-
namic loading, eccentricity, eccentric loading, elastic deformation, energy
absorption, feature
growth, feature morphology migration, feature propagation, flexing, heave,
impulse, loading,
misalignment, moments, offset, oscillation, plastic deformation, pitch,
propagation, pulsation,
pulsating load, roll, shear, static loading, strain, stress, surge, sway,
tension, thermal loading, tor-
sion, twisting, vibration, yaw, analytical components of the above, relative
components of the
above, linear combinations thereof, non-linear combinations thereof, static
combinations thereof,
time-varying combinations thereof, transient combinations thereof and similar
items.
1003401 The computer can be adapted to operate a data acquisition system
to acquire and
store in the memory storage deployment parameters of the material comprising
but not being
limited to at least one of absorption, AC parameters, acceleration, amplitude,
angle, brittleness,
capacitance, conductivity, color, coordinates, critical-point temperature,
cyclic loading, DC pa-
rameters, deformation, density, depth, diameter, dimension, direction,
distance, ductility, ductile-
brittle transition temperature, eccentricity, eccentric loading, echo, flow,
flow rate, fluid level,
force, frequency, geometry, impedance, heave, horsepower, image, impedance,
impulse, induct-
ance, length, loads, load distribution, location, longitude, misalignment,
moments, motion, num-
ber of cycles, number of rotations, number of strokes, opacity, ovality,
penetration rate, permea-
bility, ph, phase, plastic deformation, position, power, power consumption,
pressure, propaga-
tion, proximity, radius, reflectivity, reluctance, resistance, rotation, rpm,
shear, size, sound, spe-
cific gravity, speed, static loading, strain, stress, temperature, tension,
thermal loading, torque,
torsion, twisting, velocity, vibration, volume, wave, weight, weight on bit,
width, relative values
of the above, combinations of the above and similar items.
62
CA 2964243 2017-04-12
[00341] The at least one computer may also be adapted to operate a
features acquisition
system to acquire at least one of the plurality of features of the material.
At least one sensor with
an output is disposed about the material. The output comprises of signals
indicative of at least
one of the plurality of features, in a time-varying electrical form.
[00342] At least one sensor interface is utilized by the at least one
computer, wherein the
output is in communication with the at least one computer and wherein the at
least one computer
converts the signals to a digital format, producing digital signals that can
be stored in the
memory storage.
[00343] The system may be operable to induce an excitation into the
material wherein the
induction of excitation is controlled, at least in part, by the at least one
computer and wherein an
excitation response characteristic is stored in the memory storage of the at
least one computer.
[00344] The output comprises, at least in part, a response of the material
to the excitation.
[00345] In one embodiment, at least one database of features recognition
equations stored
in the memory storage; historical data of the material stored in the memory
storage; at least one
features recognition program being executed on the at least one computer and
being guided by
the at least one database to utilize the stored digital signals, equations and
material historical data
for identifying at least one of the plurality of the material features
detected by the at least one
sensor and to connect and associate the recognized at least one of the
plurality of the material
features with stored definitions, formulas and equations to convert the
recognized material fea-
tures into a description of the material for use by the finite element
analysis program.
[00346] The system may further comprise at least one output device whereby
an operator
may examine at least one solution of the finite element analysis program, and
at least one
input device whereby an operator may modify, at least in part, he at least one
description of the
material and perform a finite element analysis on the modified description of
the material,
whereby the operator may examine a plurality of descriptions of the material
analyzed by the fi-
nite element analysis program and may select at least one optimum material
description from the
plurality of descriptions. The material may be modified according to the
optimized description.
[00347] A finite-element-analysis system can be used to optimize tubulars
used in the ex-
ploration, drilling, production and transportation of hydrocarbons. In one
embodiment, the sys-
63
CA 2964243 2017-04-12
tern may comprise one or more of a computer, at least one material features
acquisition system
for the at least one computer, at least one memory storage for the at least
one computer, wherein
the at least one material feature can be stored, a feature recognition program
using at least one of
algorithms, charts, equations, look-up tables and similar items stored in the
at least one memory
storage and executed by the at least one computer to perform at least one of
detect, measure, dis-
tinguish, recognize, identify and connect the at least one material feature
with known definitions
and formulas stored in the at least one memory storage resulting in a one, two
or three dimen-
sional mathematical description of the at least one material feature; and a
finite element analysis
program capable of a plurality of solutions, the program being executed on the
at least one com-
puter to analyze the mathematical description of at least one material feature
under a plurality of
loads and deployment parameters.
[00348] In another embodiment, the present invention may include a finite-
element-
analysis system to control Risk through Identification and Assessment followed
by Corrective
action and Monitoring in order to minimize the impact of unfortunate events
and protect the pub-
lic, the personnel, the environment and the property.
[00349] In another embodiment, a material optimization system is disclosed
with at least
one computer; at least one memory storage for the at least one computer,
wherein the at least one
description of the material can be stored, the description based on at least
one of a plurality of the
material variables; and a finite element analysis program capable of a
plurality of solutions, the
program being executed on the at least one computer to optimize the material
the optimization
based on the at least one of a plurality of the material variables.
[00350] The material to be assessed may include at least one of aircraft,
beam, bridge,
blowout preventer, bop, boiler, cable, casing, chain, chiller, coiled tubing,
chemical plant, col-
umn, composite, compressor, coupling, crane, drill pipe, drilling rig,
enclosure, engine, fastener,
flywheel, frame, gear, gear box, generator, girder, helicopter, hose, marine
drilling and produc-
tion riser, metal goods, oil country tubular goods, pipeline, piston, power
plant, propeller, pump,
rail, refinery, rod, rolling stoke, sea going vessel, service rig, storage
tank, structure, sucker rod,
tensioner, train, transmission, trusses, tubing, turbine, vehicle, vessel,
wheel, workover rig, sub-
systems of the above, components of the above, combinations of the above, and
similar items.
64
CA 2964243 2017-04-12
[00351] The material variables may comprise at least one of balooning,
blemish, blister,
boxwear, coating, collar, corrosion, corrosion-band, coupling, crack, crack-
like, critically-
flawed-area, cfa, critically-flawed-path, cfp, chemistry, cross-sectional-
area, csa, defect, defor-
mation, dent, density, dimension, duration, eccentricity, erosion, fatigue,
flaw, geometry, groove,
groove-like, gauge, gauge-like, hardness, key-seat, lamination, loss-of-
metallic-area, lma, metal-
lic-area, mash, misalignment, neck-down, notch, ovalty, paint, pit, pitting-
band, pit-like, profile,
proximity, rodwear, scratch, seam, sliver, straightness, stretch, surface-
finish, surface-profile,
taper, thickness, thread, threaded-connection, tool joint, wall, wall-
thickness, wall-profile, wear,
weld, wrinkles, a combination thereof and similar items.
[00352] The plurality of solutions comprise at least one of bending,
buckling,
compression, cyclic loading, deflection, deformation, dynamic linking, dynamic
loading, eccen-
tricity, eccentric loading, elastic deformation, energy absorption, feature
growth, feature mor-
phology migration, feature propagation, flexing, heave, impulse, loading,
misalignment, mo-
ments, offset, oscillation, plastic deformation, pitch, propagation,
pulsation, pulsating load, roll,
shear, static loading, strain, stress, surge, sway, tension, thermal loading,
torsion, twisting, vibra-
tion, yaw, analytical components of the above, relative components of the
above, linear combina-
tions thereof, non-linear combinations thereof, static combinations thereof,
time-varying combi-
nations thereof, transient combinations thereof and similar items.
[00353] The computer can be adapted to operate a data acquisition system
to acquire and
store in the memory storage deployment parameters of the material comprising
at least one of
absorption, AC parameters, acceleration, amplitude, angle, brittleness,
capacitance, conductivity,
color, coordinates, critical-point temperature, cyclic loading, DC parameters,
deformation, densi-
ty, depth, diameter, dimension, direction, distance, ductility, ductile-
brittle transition tempera-
ture, eccentricity, eccentric loading, echo, flow, flow rate, fluid level,
force, frequency, geome-
try, impedance, heave, horsepower, image, impedance, impulse, inductance,
length, loads, load
distribution, location, longitude, misalignment, moments, motion, number of
cycles, number of
rotations, number of strokes, opacity, ovality, penetration rate,
permeability, ph, phase, plastic
deformation, position, power, power consumption, pressure, propagation,
proximity, radius, re-
flectivity, reluctance, resistance, rotation, rpm, shear, size, sound,
specific gravity, speed, static
loading, strain, stress, temperature, tension, thermal loading, torque,
torsion, twisting, velocity,
CA 2964243 2017-04-12
vibration, volume, wave, weight, weight on bit, width, relative values of the
above, combina-
tions of the above and similar items.
[00354]
[00355] The at least one computer can be adapted to operate a variables
acquisition system
to acquire at least one of the plurality of variables of the material,
comprising: at least one sensor
with an output disposed about the material, the output comprising of signals
indicative of at least
one of the plurality of variables, in a time-varying electrical form; at least
one sensor interface
for the at least one computer, wherein the output is in communication with the
at least one com-
puter and wherein the at least one computer converts the signals to a digital
format, producing
digital signals; and wherein the digital signals can be stored in the memory
storage.
[00356] The variables acquisition system is operable to induce an
excitation into the mate-
rial wherein the induction of excitation is controlled, at least in part, by
the at least one computer
and wherein an excitation response characteristic is stored in the memory
storage of the at least
one computer. The output comprises, at least in part, a response of the
material to the excitation.
[00357] At least one database of variables recognition equations may be
stored in the
memory storage, historical data of the material may be stored in the memory
storage; at least one
variables recognition program may be executed on the at least one computer
which is then guid-
ed by the at least one database to utilize the stored digital signals,
equations and material histori-
cal data for identifying at least one of the plurality of the material
variables detected by the at
least one sensor and to connect and associate the recognized at least one of
the plurality of the
material variables with stored definitions, formulas and equations to convert
the recognized ma-
terial variables into a description of the material for use by the finite
element analysis program.
[00358] At least one output device can be utilized whereby an operator may
examine at
least one solution of the finite element analysis program. At least one input
device may be uti-
lized whereby an operator may modify, at least in part, the at least one
description of the material
and perform a finite element analysis on the modified description of the
material. The operator
may examine a plurality of descriptions of the material analyzed by the finite
element analysis
program and may select at least one optimum material description from the
plurality of descrip-
tions whereby the material is modified according to the optimized description.
66
CA 2964243 2017-04-12
[00359] In another embodiment, a material optimization system to optimize
tubulars used
in the exploration, drilling, production and transportation of hydrocarbons
comprising: at least
one computer, at least one memory storage for the at least one computer,
wherein the at least one
description of the material can be stored, the description based on at least
one of a plurality of the
material variables; and a finite element analysis program capable of a
plurality of solutions, the
program being executed on the at least one computer to optimize the material
the optimization
based on the at least one of a plurality of the material variables.
[00360] A method may be provided for continuous engineering assessment,
comprising
producing an assessment of as-built material, utilizing at least one MxN
addressable sensor cell
with MxN sensors to produce FEA data representative of as-is material,
producing a software
simulation of the as-built material and a software simulation of the as-is
material, and apply-
ing simulated forces to the software simulation of the as-is material software
simulation of the
as-built material, and comparing results of the step of applying the simulated
forces.
[00361] In one embodiment, the present invention provides a material
assessment system
to assess a material comprising at least one computer, a material features
acquisition system op-
erable to detect a plurality of material features, a features recognition
system operable to recog-
nize the plurality of material features and to associate the recognized
material features with
known definitions, and software to operate upon the recognized material
features to create a
mathematical description of the material.
[00362] The material may include, but is not limited to, at least one of
aircraft, beam,
bridge, blowout preventer, bop, boiler, cable, casing, chain, chiller, coiled
tubing, chemical plant,
column, composite, compressor, coupling, crane, drill pipe, drilling rig,
enclosure, engine, fas-
tener, flywheel, frame, gear, gear box, generator, girder, helicopter, hose,
marine drilling and
production riser, metal goods, oil country tubular goods, pipeline, piston,
power plant, propeller,
pump, rail, refinery, rod, rolling stoke, sea going vessel, service rig,
storage tank, structure, suck-
er rod, tensioner, train, transmission, trusses, tubing, turbine, vehicle,
vessel, wheel, workover
rig, components of the above, combinations of the above, and similar items.
[00363] The plurality of material features may include, but is not limited
to, at least one
of balooning, blemish, blister, boxwear, coating, collar, corrosion, corrosion-
band, coupling,
67
CA 2964243 2017-04-12
crack, crack-like, critically-flawed-area, cfa, critically-flawed-path, cfp,
cross-sectional-area, csa,
defect, deformation, dent, density, dimension, duration, eccentricity,
erosion, fatigue, flaw, ge-
ometry, groove, groove-like, gauge, gauge-like, hardness, key-seat,
lamination, loss-of-metallic-
area, lma, metallic-area, mash, misalignment, neck-down, notch, ovality,
paint, pit, pitting-band,
pit-like, profile, proximity, rodwear, scratch, seam, sliver, straightness,
stretch, surface-finish,
surface-profile, taper, thickness, thread, threaded-connection, tool joint,
wall, wall-thickness,
wall-profile, wear, weld, wrinkles, a combination thereof and similar items.
[00364] The system may further include at least one sensor with an output
comprising of
signals indicative of plurality of features from the material under
assessment, in a time-varying
electrical form. A sensor interface may be provided for the at least one
computer, wherein the
output is in communication with the at least one computer and wherein the at
least one computer
converts the signals to a digital format, producing digital signals. A memory
storage may be pro-
vided for the at least one computer to store the digital features.
[00365] The material features acquisition system may be operable to induce
an excitation
into the material under assessment wherein the induction of excitation is
controlled, at least in
part, by the at least one computer and wherein an excitation response
characteristic is stored in
the memory storage of the at least one computer.
[00366] The system may further include at least one database of material
features recogni-
tion equations and material historical data stored in the memory storage. At
least one program
being executed on the at least one computer and being guided by the at least
one database to uti-
lize the stored digital signals, equations and material historical data for
identifying the plurality
of material features detected by the at least one sensor and to connect and
associate the recog-
nized material features with stored definitions, formulas and equations to
convert the recognized
material features into a mathematical description of the material under
assessment.
[00367] The material features acquisition system may be adapted to operate
a data acquisi-
tion system to acquire material deployment parameters including, but not
limited to, at least one
of absorption, AC parameters, acceleration, amplitude, angle, brittleness,
capacitance, conduc-
tivity, color, critical-point temperature, cyclic loading, DC parameters,
deformation, density,
depth, diameter, dimension, direction, distance, ductility, ductile-brittle
transition temperature,
68
CA 2964243 2017-04-12
eccentricity, eccentric loading, echo, flow, flow rate, fluid level, force,
frequency, geometry, im-
pedance, heave, horsepower, image, impedance, impulse, inductance, length,
loads, load distri-
bution, location, longitude, misalignment, moments, motion, number of cycles,
number of rota-
tions, number of strokes, opacity, ovality, penetration rate, permeability,
ph, phase, plastic de-
formation, position, power, power consumption, pressure, propagation,
proximity, radius, reflec-
tivity, reluctance, resistance, rotation, rpm, shear, size, sound, specific
gravity, speed, static load-
ing, strain, stress, temperature, tension, thermal loading, torque, torsion,
twisting, velocity, vibra-
tion, volume, wave, weight, weight on bit, width, relative values of the
above, combinations of
the above and similar items.
[00368] The data acquisition system may be programmed to acquire loads
endured by the
material under assessment including at least one of bending, buckling,
compression, cyclic load-
ing, deflection, deformation, dynamic linking, dynamic loading, eccentricity,
eccentric loading,
elastic deformation, energy absorption, feature growth, feature morphology
migration, feature
propagation, flexing, heave, impulse, loading, misalignment, moments, offset,
oscillation, plastic
deformation, propagation, pulsation, pulsating load, shear, static loading,
strain, stress, tension,
thermal loading, torsion, twisting, vibration, analytical components of the
above, relative com-
ponents of the above, linear combinations thereof, non-linear combinations
thereof and similar
items.
[00369] The at least one computer may be programmed to apply at least one
of the de-
ployment parameters, loads or a combination thereof on the mathematical
description of the ma-
terial under assessment to calculate at least one of an as-is material,
fitness for service, remaining
useful life, remediation, and/or combinations thereof and similar items.
[00370] The material features may be partially obtained and inputted into
the least one
computer from a video camera in communication with the least one computer. In
another embod-
iment, the identification of the material is partially obtained and inputted
into the least one com-
puter from a visual or an identification tag affixed onto or into the material
under assessment.
The material identification may be utilized to access stored historical data
of the material under
assessment.
69
CA 2964243 2017-04-12
[00371] The system may provide a speech synthesizer and at least one of a
loudspeaker
and an earphone, wherein the at least one computer requests a data input from
an operator
through natural speech.
[00372] The computer may inform the operator about the material under
assessment status
through natural speech.
[00373] A speech recognition engine and at least one microphone may be
provided,
wherein at least one of command, the material historical data, recognition and
similar items is
inputted at least in part into the least one computer by an operator through
natural speech.
[00374] A sound recognition engine and at least one microphone, wherein at
least one of
the material deployment parameters, material historical data, loads and
similar items is obtained
at least in part from the sound recognition engine.
[00375] The system may further include a sound synthesizer and at least one
of loud-
speaker and earphone, wherein the computer converts the material status into
audible sound.
[00376] The conversion of recognized plurality of material features into
the mathematical
description may further comprise a data format fit for use by a finite element
analysis program or
a computer aided design program or a combination of the above.
[00377] The conversion of the recognized plurality of material features may
further com-
prise an operational model of the as-is material, the as-is material
operational model being oper-
ated by the at least one computer, the operation guided by the at least one
database to make at
least one determination of whether the as-is material is functional as-
designed, the as-is material
is operating within the operational-envelop, the as-is material is fit for use
for a service or should
be removed from use in the service or a combination thereof.
[00378] The operation of the as-is material operational model may be
operated by the at
least one computer and the operation guided by the at least one database to
determine a failure
mode of the as-is material under at least one of the deployment parameters,
the loads or combi-
nation thereof and to calculate a remediation to avert the failure.
CA 2964243 2017-04-12
[00379] In another embodiment of the present invention, a material
assessment system is
disclosed which may include, but is not limited to, at least one computer with
storage, a material
features acquisition system operable to detect a plurality of material
features, a features recogni-
tion system operable to recognize a plurality of material features and to
associate the recognized
material features with known definitions, a database comprising of the
material historical data
stored in the storage, and software to operate upon the historical data and
recognized material
features to determine a change in the recognized material features and to
store the change in the
database of the material historical data.
[00380] The database may further comprise a plurality of risks, failure-
chains, failure-
modes and remediation of the material under assessment.
[00381] The at least one computer may be programmed to calculate a material
change-
chain using the stored historical data the calculation being guided by the
database.
[00382] The at least one computer is further programmed to compare the
material change-
chain with the plurality of risks, failure-chains and/or failure-modes, the
calculation being guided
by the database, to determine if the material change-chain matches an early
stage of at least one
of the risks, plurality of failure-chains and/or failure-modes and to
recommend a remediation to
disrupt the evolution of the change-chain into a failure-chain.
[00383] Another embodiment discloses a method to disrupt at least one
failure-chain, in-
cluding the steps of analyzing a system utilizing system risks and failure
chains and at least one
of system historical data, loads, deployment parameters, environment, to
define the system oper-
ational-envelop, reducing the system into sub-systems and components, and
analyzing the sub-
systems and components utilizing subsystem and component risks and failure-
chains and at least
one of subsystem and components historical data, loads, deployment parameters,
environment, to
define the sub-systems and components operational-envelop. The components are
assessed to
determine the as-is components and the as-is components are assessed on an
ongoing basis to
calculate changes in the as-is components. Further steps include assessing the
sub-systems to de-
termine the as-is sub-systems using the as-is components and assessing the as-
is subsystem to
calculate changes in the as-is sub-systems, assessing the system to determine
the an as-is system
using the as-is sub-systems and as-is components and assessing the as-is
system to calculate
71
CA 2964243 2017-04-12
changes in the as-is system, and identifying and remediating at least one of
the system risks and
failure-chains and at least one of the subsystem and components risks and
failure-chains associ-
ated with at least one of the changes, thereby disrupting the at least one
failure-chain.
[00384] The method may further comprise calculating at least one of a
fitness for service,
remaining useful life or a combination thereof.
[00385] In another embodiment, a continuous vigilance sensor cell to
monitor a material is
disclosed including an MxN array of addressable sensors positioned adjacent
the material, opera-
tors for the senor cell to receive signals from selected of the addressable
sensors and combine
data to produce virtual sensor data, and at least one computer to control
addressing and use of the
operators to produce the virtual sensor data.
[00386] In other embodiments, a method for optimizing materials for use is
shown includ-
ing the steps of inducing an excitation into the material and detecting the
response of the material
to the excitation with at least one sensor with an output signal in a time-
varying electrical form.
The output signal is then communicated to at least one computer with memory
storage and the
signal converted to a digital format resulting in a digital signal stored in
the memory storage.
Further steps include inputting and storing in the memory storage at least one
set of recognition
equations and historical data of the material, inputting at least one set of
constrains into the at
least one computer, wherein the at least one set of constrains are evaluated
by the at least one
computer for recognizing the types of imperfections detected by the at least
one imperfection de-
tection sensor, and finally storing the at least one set of constrains and/or
the output into at least
one memory storage.
[00387] Recognizing the types of imperfections may further comprise at
least one mathe-
matical array of coefficients, wherein the coefficients comprise converted
and/or decomposed
signals from the at least one imperfection detection sensor, and/or baseline
data comprising data
from known material imperfection, and/or historical data comprising data
previously gathered
from the material being inspected, wherein the converted at least one
imperfection signal is pro-
cessed by the at least one computer using a mathematical array of coefficients
and constants. The
coefficients comprise converted signals from the at least one imperfection
detection sensor, and
wherein the constants are derived, at least in part, from baseline data
comprising data from
72
CA 2964243 2017-04-12
known material imperfection, and/or historical data comprising data previously
gathered from
the material being inspected.
[00388] The at least one memory storage may also be the at least one
computer.
[00389] The at least one memory storage may comprise more than one memory
storage,
and the at least one imperfection detection sensor may comprise a memory
storage.
[00390] The method may further comprise the step of developing the
coefficients includ-
ing inputting parameters associated with a material being inspected into a
database. The parame-
ters may comprise physical characteristics of the material being inspected.
[00391] The processing of the converted at least one imperfection signals
by the at least
one computer may further comprise scaling the converted at least one
imperfection signals,
wherein the scaling accounts for variations in testing parameters, decomposing
the converted at
least one imperfection signals which separates the converted at least one
imperfection signals
into components indicative of various imperfections, and generating
identifiers by fusing the de-
composed signal with parameters and/or database data and/or historical data
associated with the
material being inspected.
[00392] The identifiers may provide a prediction of the type of
imperfection.
[00393] The method may further comprise searching a database of prior
information
and/or identifiers, relating to the material being inspected, to implement an
imperfection identifi-
cation.
[00394] The at least one computer may analyze the database of prior
information and the
identifiers to assign a preliminary determination of the imperfection.
[00395] The preliminary determination may be compared to baseline data
comprising data
from known material imperfection, and/or historical data comprising data
previously gathered
from the material being inspected to resolve conflicting determination of the
imperfection.
[00396] The resolving of conflicting determination of the imperfection may
include as-
signing a determination based on the substantial criticality of the
imperfection to the material be-
ing inspected, a re-evaluation and resolution of the conflicting determination
of the imperfection,
and coding and storing new data in a decomposed signals database.
73
CA 2964243 2017-04-12
[003971 In other embodiments, a method to recognize imperfections in
materials is dis-
closed including, but not limited to, operating an imperfection detection
sensor which emits an
electronic signal regarding an element to be inspected, band limiting the
electronic signal which
comprises passing the electronic signal through at least one filter, scaling
the electronic signal to
account for variations in testing parameters, converting the electronic signal
into a digital signal,
and inputting the digital signal into at least one computer. Further steps
include de-noising the
digital signal, wherein the de-noising comprises separation and/or removal of
a component of the
digital signal, decomposing the digital signal into components indicative of
various imperfec-
tions, calculating at least one first identifier from the components
indicative of various imperfec-
tions, wherein the calculating is performed by the at least one computer,
comparing the at least
one first identifier to a pre-established identifier, wherein the pre-
established identifier is stored
in a pre-established database, and recognizing an imperfection from the
comparison, wherein the
recognition is performed by the at least one computer and is stored in the pre-
established data-
base and/or outputted from the at least one computer.
[00398] The method may further comprise the step of resolving a
recognition conflict.
[00399] The method may further comprise the step of resolving an
instability in the recog-
nition of the imperfection, wherein instability comprises recognizing more
than one imperfection
during the comparison.
[00400] The method may further comprise the step of inducing an excitation
into a materi-
al and detecting the response of the excitation through the imperfection
detection sensor; wherein
the inducing of the excitation is controlled by the at least one computer.
[00401] In another embodiments, a method to inspect materials for locating
desired char-
acteristics is provided, including, but not limited to, operating an
imperfection detection sensor
which emits an electronic signal regarding an element to be inspected, band
limiting the elec-
tronic signal which comprises passing the electronic signal through at least
one filter, scaling the
electronic signal to account for variations in testing parameters, converting
the electronic signal
into a digital signal, and inputting the digital signal into at least one
computer. Further steps in-
clude de-noising the digital signal, wherein the de-noising comprises
separation and/or removal
of a component of the digital signal, decomposing the digital signal into
components indicative
74
CA 2964243 2017-04-12
of various imperfections, calculating at least one first identifier from the
components indicative
of various imperfections, wherein the calculating is performed by the at least
one computer,
comparing the at least one first identifier to a pre-established identifier,
wherein the pre-
established identifier is stored in a pre-established database, and
recognizing an imperfection
from the comparison, wherein the recognition is performed by the at least one
computer and is
stored in the pre-established database and/or outputted from the at least one
computer.
1004021 The method may further comprise the step of resolving a
recognition conflict.
[00403] The method may further comprise the step of resolving an
instability in the recog-
nition of the imperfection, wherein instability comprises recognizing more
than one imperfection
during the comparison.
[00404] The method may further comprise the step of inducing an excitation
into a materi-
al and detecting the response of the excitation through the imperfection
detection sensor; wherein
the inducing of the excitation is controlled by the at least one computer.
[00405] Another embodiment provides for a material assessment system
comprising at
least one computer, a material features acquisition system operable to detect
a plurality of mate-
rial features, a features recognition system operable to recognize a plurality
of material features
and to associate the recognized material features with known definitions, and
software to operate
upon the recognized material features to create a mathematical description of
the material under
assessment.
[00406] The material may comprise at least one of aircraft, beam, bridge,
blowout pre-
venter, bop, boiler, cable, casing, chain, chiller, coiled tubing, chemical
plant, column, compo-
site, compressor, coupling, crane, drill pipe, drilling rig, enclosure,
engine, fastener, flywheel,
frame, gear, gear box, generator, girder, helicopter, hose, marine drilling
and production riser,
metal goods, oil country tubular goods, pipeline, piston, power plant,
propeller, pump, rail, refin-
ery, rod, rolling stoke, sea going vessel, service rig, storage tank,
structure, sucker rod, tensioner,
train, transmission, trusses, tubing, turbine, vehicle, vessel, wheel,
workover rig, subsystems of
the above, components of the above, combinations of the above, and similar
items.
[00407] The material features may include at least one of balooning,
blemish, blister,
boxwear, coating, collar, corrosion, corrosion-band, coupling, crack, crack-
like, critically-
CA 2964243 2017-04-12
flawed-area, cfa, critically-flawed-path, cfp, cross-sectional-area, csa,
defect, deformation, dent,
density, dimension, duration, eccentricity, erosion, fatigue, flaw, geometry,
groove, groove-like,
gauge, gauge-like, hardness, key-seat, lamination, loss-of-metallic-area, lma,
metallic-area,
mash, misalignment, neck-down, notch, ovality, paint, pit, pitting-band, pit-
like, profile, proximi-
ty, rodwear, scratch, seam, sliver, straightness, stretch, surface-finish,
surface-profile, taper,
thickness, thread, threaded-connection, tool joint, wall, wall-thickness, wall-
profile, wear, weld,
wrinkles, a combination thereof and similar items.
[00408] The system may further include at least one sensor with an output
comprising of
signals indicative of plurality of features from the material under
assessment, in a time-varying
electrical form. A sensor interface may be provided for the at least one
computer, wherein the
output is in communication with the at least one computer and wherein the at
least one computer
converts the signals to a digital format, producing digital signals. A memory
storage may be pro-
vided for the at least one computer to store the digital features.
[00409] The material features acquisition system may be operable to induce
an excitation
into the material under assessment wherein the induction of excitation is
controlled, at least in
part, by the at least one computer and wherein an excitation response
characteristic is stored in
the memory storage of the at least one computer.
[00410] The output may comprise at least in part a response of the
material under assess-
ment to the excitation.
[00411] The system may further include at least one database of material
features recogni-
tion equations and material historical data stored in the memory storage. At
least one program
being executed on the at least one computer and being guided by the at least
one database to uti-
lize the stored digital signals, equations and material historical data for
identifying the plurality
of material features detected by the at least one sensor and to connect and
associate the recog-
nized material features with stored definitions, formulas and equations to
convert the recognized
material features into a mathematical description of the material under
assessment.
[00412] The material features acquisition system may be adapted to operate
a data acquisi-
tion system to acquire material deployment parameters including, but not
limited to, at least one
of absorption, AC parameters, acceleration, amplitude, angle, brittleness,
capacitance, conduc-
76
CA 2964243 2017-04-12
tivity, color, critical-point temperature, cyclic loading, DC parameters,
deformation, density,
depth, diameter, dimension, direction, distance, ductility, ductile-brittle
transition temperature,
eccentricity, eccentric loading, echo, flow, flow rate, fluid level, force,
frequency, geometry, im-
pedance, heave, horsepower, image, impedance, impulse, inductance, length,
loads, load distri-
bution, location, longitude, misalignment, moments, motion, number of cycles,
number of rota-
tions, number of strokes, opacity, ovality, penetration rate, permeability,
ph, phase, plastic de-
formation, position, power, power consumption, pressure, propagation,
proximity, radius, reflec-
tivity, reluctance, resistance, rotation, rpm, shear, size, sound, specific
gravity, speed, static load-
ing, strain, stress, temperature, tension, thermal loading, torque, torsion,
twisting, velocity, vibra-
tion, volume, wave, weight, weight on bit, width, relative values of the
above, combinations of
the above and similar items.
[00413] The data acquisition system may be programmed to acquire loads
endured by the
material under assessment including at least one of bending, buckling,
compression, cyclic load-
ing, deflection, deformation, dynamic linking, dynamic loading, eccentricity,
eccentric loading,
elastic deformation, energy absorption, feature growth, feature morphology
migration, feature
propagation, flexing, heave, impulse, loading, misalignment, moments, offset,
oscillation, plastic
deformation, propagation, pulsation, pulsating load, shear, static loading,
strain, stress, tension,
thermal loading, torsion, twisting, vibration, analytical components of the
above, relative com-
ponents of the above, linear combinations thereof, non-linear combinations
thereof and similar
items.
[00414] The at least one computer may be programmed to apply at least one
of the de-
ployment parameters, loads or a combination thereof on the mathematical
description of the ma-
terial under assessment to calculate at least one of an as-is material,
fitness for service, remaining
useful life, remediation, and/or combinations thereof and similar items.
[00415] The calculation may further comprise of at least one of axial
stress, burst yield,
collapse yield, fluid volume, hoop stress, overpull, radial stress, stretch,
ultimate load capacity,
ultimate torque, yield load capacity, yield torque, similar items and
combination thereof
[00416] The calculation further determines an effect that at least one of
the recognized
material feature has upon another of the recognized material feature.
77
CA 2964243 2017-04-12
[00417] The material features may be partially obtained and inputted into
the least one
computer from a video camera in communication with the least one computer.
[00418] The identification of the material may be partially obtained and
inputted into the
least one computer from a visual or an identification tag affixed onto or into
the material under
assessment.
[00419] The material identification may be utilized to access stored
historical data of the
material under assessment.
[00420] The system may further include a speech synthesizer and at least
one of loud-
speaker and/or earphone and/or a speech emanating device, wherein the at least
one computer
requests a data input from an operator through natural speech.
[00421] The computer may inform the operator about the material under
assessment status
through natural speech.
[00422] The inspection system may include at least one language selector,
wherein the
speech synthesizer produces voice output in more than one language.
[00423] The inspection system may further include a speech recognition
engine and at
least one of microphone and/or electroacoustic device, wherein at least one of
command, the ma-
terial historical data, recognition and similar items is inputted at least in
part into the least one
computer by an operator through natural speech.
[00424] The inspection system may include at least one language selector,
wherein the
speech recognition engine may accept and recognize more than one language.
[00425] The inspection system may include an automatic language selector,
wherein the
speech recognition engine may automatically accept and recognize more than one
language.
[00426] The inspection system may include an automatic language selector,
wherein the
speech recognition engine may automatically and substantially simultaneously
recognize more
than one language.
[00427] The inspection system may further comprise at least one of a
fingerprint, voice-
print, iris scan, face recognition and other biometric identification
capability to recognize an op-
erator.
78
CA 2964243 2017-04-12
[00428] The inspection system may include a sound recognition engine and at
least one of
microphone and/or electroacoustic device, wherein at least one of the material
deployment pa-
rameters, the material historical data, the loads, the deployment parameters
and similar items is
obtained at least in part from the sound recognition engine.
[00429] A sound synthesizer and at least one of loudspeaker and/or earphone
and/or a
speech emanating device may be provided so the computer converts the material
under assess-
ment status into audible sound.
[00430] The conversion of recognized plurality of material features into
the mathematical
description may comprise a data format fit for use by a finite element
analysis program and/or a
computer aided design program and/or another program or a combination of the
above. It may
also further comprise an operational model of the as-is material under
assessment, the as-is mate-
rial under assessment operational model being operated by the at least one
computer, the opera-
tion guided by the at least one database to make at least one determination of
whether the as-is
material under assessment is functional as-designed, the as-is material under
assessment is oper-
ating within the operational-envelop, the as-is material under assessment is
fit for use for a ser-
vice or should be removed from use in the service or a combination thereof.
[00431] The operation of the as-is material under assessment operational
model may be
operated by the at least one computer and the operation guided by the at least
one database to
determine a failure mode of the as-is material under at least one of the
deployment parameters,
the loads or combination thereof and to calculate a remediation to avert the
failure.
[00432] The at least one computer may be programmed to calculate at least
one change in
at least one of the recognized features comprising of a difference, a feature
change, a feature
morphology migration, a feature morphology shift, a feature propagation, a
coverage change,
combinations thereof and similar items utilizing, at least in part, the
material under assessment
stored historical data.
[00433] The at least one computer may compare at least one of the material
under assess-
ment change with a plurality of failure-chains stored in the material under
assessment historical
data to determine a match indicative of an evolution of a failure-chain.
79
CA 2964243 2017-04-12
[00434] The at least one computer may recommend remediation to disrupt the
evolution of
the failure-chain. The remediation may comprise at least one of utilization,
redeployment and
alteration to a shape of at least one of the recognized material features.
[00435] The at least one computer may be programmed to calculate at least
one change in
at least one of the loads and the deployment parameters to correlate and/or
associate and/or con-
nect at least in part, with the change in at least one of the recognized
features utilizing, at least in
part, the material under assessment stored historical data.
[00436] The at least one computer may be programmed to calculate at least
one sensitivity
in at least one of the recognized material features to the loads and/or the
deployment parameters
change.
[00437] The location of the material recognized features is in reference to
the at least one
sensor.
[00438] The at least one computer may calculates the location of at least
one of the mate-
rial recognized features in reference to other locations utilizing the
deployment parameters and
the historical data.
[00439] The system may comprise at least one communication link. The at
least one
communication link may include, but is not limited to, at least one of a
radio, a wireless, sonic,
underwater modem, other types of communicators, chain or relay stations, a
combination thereof
and similar items. The communication link may provide bidirectional access to
the material as-
sessment system whereby the material assessment system may be monitored and/or
controlled
from a remote location.
[00440] Another embodiment may provide a material assessment system
comprising, but
not limited to, at least one computer with storage, a material features
acquisition system operable
to detect a plurality of material features, a features recognition system
operable to recognize a
plurality of material features and to associate the recognized material
features with known defini-
tions, a database comprising of the material historical data stored in the
storage, and software to
operate upon the historical data and recognized material features to determine
a change in the
recognized material features and to store the change in the database of the
material historical da-
ta.
CA 2964243 2017-04-12
[00441] The database may further comprise at least one of a risk, failure-
chain, failure-
mode, sensitivity of failure-chain to change, sensitivity of failure-chain to
initial conditions, re-
mediation, combinations of the above and similar items of the material under
assessment.
[00442] The at least one computer may be programmed to calculate a material
change-
chain using the stored historical data the calculation being guided by the
database.
[00443] The at least one computer may be further programmed to compare the
material
change-chain with the at least one of risk and/or failure-chain and/or failure-
mode, the compari-
son being guided by the database, to determine if the material change-chain
matches an early
stage of at least one of the risk and/or failure-chain and/or failure-mode and
to recommend a re-
mediation to disrupt the evolution of the change-chain into a failure-chain.
[00444] In another embodiment, a method to disrupt at least one failure-
chain is provided
including the steps of analyzing a system utilizing system risks and failure-
chains and at least
one of system historical data, loads, deployment parameters and environment to
define system
operational-envelop, reducing the system into subsystems and components,
analyzing the subsys-
tems and components utilizing subsystem and component risks and failure-chains
and at least
one of subsystem and component historical data, loads, deployment parameters
and environment
to define the subsystems and components operational-envelop, assessing the
components to de-
termine as-is components and assessing the as-is components on an ongoing
basis to calculate
changes in the as-is components, assessing the subsystems to determine as-is
subsystems using
the as-is components and assessing the as-is subsystems on an ongoing basis to
calculate changes
in the as-is subsystems, assessing the system to determine an as-is system
using the as-is subsys-
tems and as-is components and assessing the as-is system on an ongoing basis
to calculate
changes in the as-is system, and identifying and remediating at least one of
the system risks and
failure-chains and at least one of the subsystem and component risks and
failure-chains associat-
ed with at least one of the changes to disrupt the at least one failure-chain.
[00445] The method may further comprise calculating at least one of a
fitness for service,
remaining useful life or a combination thereof
[00446] In another embodiment, a material assessment system is provided,
comprising at
least one computer, an operable material software model stored in the at least
one computer, a
81
CA 2964243 2017-04-12
material features acquisition system operable to detect a plurality of
material features, a parame-
ters and loads acquisition system operable to detect a plurality of parameters
and loads endured
by the material, a database comprising at least one of material utilization
constraints and material
historical data, a features recognition system operable to recognize a
plurality of material fea-
tures and to associate the recognized material features with known
definitions, a model update
system to translate the recognized material features under the plurality of
parameters, loads and
utilization constraints to update the material software model, and a constant
vigilance system to
operate the material software model to determine a status of the material.
[00447]
[00448] In yet another embodiment, a material assessment system is
provided for compris-
ing at least one computer, a material features acquisition system operable to
detect a plurality of
material features, a features recognition system operable to recognize a
plurality of material fea-
tures and to associate the recognized material features with known
definitions, and software to
operate upon the recognized material features to create a mathematical
description of the materi-
al.
[00449] The material features may comprise at least one of balooning,
blemish, blister,
boxwear, coating, collar, corrosion, corrosion-band, coupling, crack, crack-
like, critically-
flawed-area, cross-sectional-area, defect, deformation, dent, density, CSA,
dimension, duration,
eccentricity, erosion, fatigue, flaw, geometry, groove, groove-like, gauge,
gauge-like, hardness,
key-seat, lamination, loss-of-metallic-area, LMA, metallic-area, mash,
misalignment, neck-
down, notch, ovality, paint, pit, pitting-band, pit-like, profile, proximity,
rodwear, scratch, seam,
sliver, straightness, taper, thickness, thread, threaded-connection, tool
joint, wall, wall-thickness,
wall-profile, wear, weld, wrinkles, a combination thereof and similar items.
[00450] The parameters may comprise at least one of acceleration,
capacitance, conductiv-
ity, color, density, dimension, distance, flow, force, frequency, horsepower,
heave, image, in-
ductance, intensity, interference, length, level, loading, load distribution,
Loads measurement,
number of cycles, number of rotations, number of strokes, opacity, penetration
rate, permeability,
ph, position, power, power consumption, pressure, proximity, reflectivity,
reluctance, resistance,
82
CA 2964243 2017-04-12
rotation, temperature, time, specific gravity, strain, tension, torque,
velocity, volume, weight and
combinations of the above and similar items.
[00451] The loads may comprise at least one of bending, buckling,
compression, cyclic
loading, deflection, deformation, dynamic linking, dynamic loading,
eccentricity, eccentric load-
ing, elastic deformation, energy absorption, Feature growth, Feature
morphology migration, Fea-
ture propagation, impulse, loading, misalignment, moments, offset,
oscillation, plastic defor-
mation, propagation, shear, static loading, strain, stress, tension, thermal
loading, torsion, twist-
ing, vibration, combinations thereof and similar items.
[00452] The assessment system of Claim 99, further comprising a speech
synthesizer and
at least one of loudspeaker and earphone, wherein the at least one computer
requests input of at
least one of the constraints and material historical data from an operator
through natural speech.
The computer may inform the operator about the material status through natural
speech.
[00453] A speech recognition engine and at least one microphone may be
provided where
at least one of the constraints and material historical data is inputted at
least in part into the least
one computer by an operator through natural speech.
[00454] The system may include a sound recognition engine and at least one
microphone,
wherein at least one of the constraints and material historical data is
obtained at least in part from
the sound recognition engine.
[00455] A sound synthesizer and at least one of loudspeaker and earphone
may be includ-
ed so the computer may convert the material status into audible sound.
[00456] The material features may be partially obtained and inputted into
the least one
computer from a video camera in communication with the least one computer. The
material may
be partially obtained and inputted into the least one computer from a visual
or electromagnetic
identification tag affixed onto or into the material.
[00457] The material utilization constraints may further comprise at least
one of coeffi-
cients, rules, knowledge and data developed and inputted into the at least one
computer prior to
the assessment of the material.
83
CA 2964243 2017-04-12
[00458] In yet another embodiment, a method to evaluate material is
disclosed comprising
detecting physical phenomena in an environment in which a material under
evaluation is utilized,
scanning the material under evaluation to detect material features, and
programming a computer
to utilize digital signals produced in response to the detecting and the
scanning to calculate a re-
maining useful life of the material under evaluation.
[00459] Another embodiment of the present invention discloses a method to
evaluate ma-
terial including, but not limited to, the steps of repeatedly scanning a
material under evaluation
over time to detect new material features and monitor previously detected
material features,
and programming a computer to analyze data produced during the step of
repeatedly scanning to
determine at least one degradation mechanism from a plurality of possible
degradation mecha-
nisms affecting the material under evaluation from a plurality.
[00460] Another step may comprise programming the computer to recommend a
preventa-
tive action to inhibit the at least one degradation mechanism.
[00461] It may be seen from the preceding description that a novel stress
engineering as-
sessment system has been provided. Although specific examples may have been
described and
disclosed, the invention of the instant application is considered to comprise
and is intended to
comprise any equivalent structure and may be constructed in many different
ways to function
and operate in the general manner as explained hereinbefore. Accordingly, it
is noted that the
embodiments described herein in detail for exemplary purposes are of course
subject to many
different variations in structure, design, application and methodology.
Because many varying and
different embodiments may be made within the scope of the inventive concept(s)
herein taught,
and because many modifications may be made in the embodiment herein detailed
in accordance
with the descriptive requirements of the law, it is to be understood that the
details herein are to
be interpreted as illustrative and not in a limiting sense.
84
