Note : Les descriptions sont présentées dans la langue officielle dans laquelle elles ont été soumises.
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AUTONOMOUS FITNESS FOR SERVICE ASSESSMENT
TECHNICAL FIELD
This invention relates, generally, to non-destructive fitness for service
assessment method and equipment, and more specifically, to provide automatic
and/or
continuous non-destructive acquisition of material features, including
evaluators and
predictors of detected features, and autonomous evaluation capability of the
material
fitness for continuing reliable use.
BACKGROUND OF THE INVENTION
As is known in the art, materials are selected for use based on criteria
including
minimum strength requirements, useable life, and anticipated normal wear.
Safety
factors are typically factored into the design considerations to supplement
material
selection in order to aid in reducing the risk of failures including
catastrophic failure.
Failures occur when the required application strengths exceed the actual
material
strength either due to the misapplication of the material or due to material
deterioration:
During its useful life, material deteriorates and/or is weakened by external
events such
as mechanical and/or chemical actions arising from the type of application,
repeated
usage, hurricanes, earthquakes, storage, transportation, and the like; thus,
raising
safety, operationai, functionality, and serviceability issues. The list of
typical material
includes, but is not limited to, aircraft, bridges, cranes, drilling rigs,
frames, chemical
plant components, engine components, oil country tubular goods (herein after
referred to
as "OCTG"), pipelines, power plant components, rails, refineries, rolling
stoke, sea going
vessels, service rigs, structures, vessels, workover rigs, other components of
the above,
combinations of the above and similar items.
Material owners perform a fitness for service (herein after referred to as
"FFS")
assessment occasionally, often following a component failure. This FFS
assessment is
mostly based on as-designed data occasionally supplemented by Non-Destructive
Inspection (herein after referred to as "NDI") data. Often, the absence of an
NDI
indication comprises the entire FFS assessment. NDI is typically carried out
in order to
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verify that the material deterioration, from some of the known deterioration
causes, has
not reduced the material strength below the minimum application requirements.
Since its inception in the early 1900s, the NDI industry has utilized a
variety of
techniques and devices, alone or in combination with each other, with the
majority based
on the well known and well documented techniques of magnetic flux leakage
(herein
after referred to as "MFL"), eddy-current (herein after referred to as "EC"),
magnetic
particle, ultrasonic (herein after referred to as "UT") radiation, such as x-
ray and gamma
ray, dye penetrant, and dimensional as well as visual and audible techniques.
MFL and
EC are also known as ElectroMagnetic Inspection (herein after referred to as
"EMI").
Typical NDI devices deploy a single sensor per material area and are therefore
classified
as one-dimensional (herein after referred to as "1 D", "1 D-NDI" and "1 D-
EMI").
However, the limited data 1 D-NDI provides for the Material-Under-Inspection
(herein after referred to as "MUI") does not adequately address the demanding
material
application FFS needs. After all, a century ago there was no drilling a 20,000-
foot well in
10,000 feet of water in search for hydrocarbons or trains traveling at speeds
in excess of
100 miles per hour or supersonic aircraft. For example, when 1 D-NDI does not
detect
any corrosion pitting that exceeds its minimum detection capabilities, it is
false to
conclude that the material is fit for the application. It is desirable
therefore to provide
Autonomous FFS (herein after referred to as "AutoFFS") equipment and methods
to the
industry. AutoFFS must detect and recognize the "as-built" and/or "as-is" MUI
features
impacting its FFS including, but not limited to, imperfections.
The Distinction Between FFS Assessment and NDI
As carried out since its inception, NDI is examining the MUI for signals
(flags)
that exceed a preset threshold level while common MUI features, such as welds
and
couplings, typically saturate the NDI processing and they are ignored.
Therefore, the end
result of an NDI can be summarized as "within the limitations of the
inspection
technique(s), there were no material regions that gave rise to signals above
the
threshold level". As will be discussed further, the combination of sensor
signal filtering
and threshold prior to any signal evaluations creates detection dead-zones, a
standard
NDI practice never the less. Such filter/threshold combination can be found
throughout
the patent record, such as in the 1931 U.S. Pat. No. 1,823,810 and the 2003
U.S. Pat.
No. 6,594,591. Therefore, the absence of an NDI indication does not
necessarily imply
that the material is fit for service.
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Another example of an NDI technique with different type detection dead-zones
is
Time of Flight Diffraction (herein after referred to as 'TOFD") of U.S. Pat.
Nos.
6,904,818, 7,082,822, 7,104,125 used for the inspection of marine drilling
risers. The
near-surface TOFD dead zone is due to lateral waves and the far-surface TOFD
dead
zone is due to echoes. It should be noted that the major and minor axis
surfaces of
marine drilling risers experience the maximum vortex-induced-vibration (herein
after
referred to as "VIV") loads and thus, cracking is expected to initiate at
stress
concentrators within the TOFD dead-zones, like the bottom of surface pits or
the heat
affected zone of welds. From actual fatigue and crack growth field runs,
Stylwan has
concluded that weld cracks tend to grow preferentially parallel to the surface
(increase
length) than into the wall (increase depth) and therefore would remain
undetected by
TOFD while undergoing their most rapid growth. The TOFD dead-zones are
significant
on used material, typically exceeding the maximum allowed imperfection depth.
Therefore, the absence of a TOFD indication can be summarized as "there were
no
material regions with cracks deeper than the TOFD detection dead-zones" which
by no
means constitute a sound NDI on used material much less an FFS assessment.
On the other hand, FFS must examine and evaluate, as close as possible, 100%
of the Material-Under-FFS-Assessment (herein after referred to as "MUA") for
100% of
features spanning from fatigue (2-D) all the way to wall thickness changes (A-
WDS) and
declare the MUA fit for continuing service only after all the features impact
upon the
MUA have been evaluated. It is well known that the presence of any
imperfection alters
the FFS of the MUA and impacts its remaining useful life. Thus, it should be
appreciated
that the deployment of the AutoFFS would increase the overall safety and
reliability as it
would lead to MUA repair and/or replacement prior to a catastrophic failure as
well as it
will reduce and/or eliminate its premature replacement due to concerns when
the
conventional inspection periods are spaced far apart and/or when the
conventional
inspection provides an insignificant inspection coverage. In addition, it
should be
understood that material free of any imperfections may still not be fit for
service in the
particular application and/or deployment.
There is a plethora of 1D-NDI systems in the patent record using terms such
as,
"Detect", "(dentify", "Recognize" but only in the context that the sensor
signal exceeds
the preset threshold level and an indication is shown in the 1 D-NDI readout
device. The
1 D-NDI readout device indication prompts the inspector to assign the material
to the
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verification crew for further manual investigation. However, as shown further
in FIGS. 2A
and 2B, 1 D-NDI cannot "connect or associate or know by some detail" the
feature or
even if the sensor signal is indeed associated with a feature; a task assigned
entirely to
the manual verification crew. As opposed to 1D-NDI, the present invention also
uses
terms such as, "Identify" and "Recognize" in the context of "connect or
associate or
know by some detail". As shown further in FIGS. 2C and 2D, AutoFFS "knows by
some
detail" the imperfection and "connects and associates" the imperfection with
known
imperfection definitions. AutoFFS preferably uses fitness for service formulas
and
knowledge and is preferably able to export a file for use by an FEA engine
because
AutoFFS "knows by some detail" the material features. It should be understood
that
different FEA engines use different structure geometry import/export
specifications.
SUMMARY OF THE INVENTION
In one possible embodiment, an evaluation system may be provided to ascertain
and/or to mitigate hazards arising from the failure of a material resulting
from
misapplication and/or deterioration of the material. The system may comprise
elements
such as, for instance, a computer and a material features acquisition system.
The
materials feature acquisition system may be used to scan the material and
identify the
nature and/or characteristics of material features. In one possible
embodiment, the
invention may further comprise a database which may comprise material
historical data
and/or constraints. The first database constraints may be selected at least in
part from
knowledge and/or rules. The knowledge and/or rules may involve stress or
loading
related factors. A non-limiting list of knowledge or rules may involve use of
the material
in applications involving one or more of bending, buckling, compression,
cyclic loading,
deflection, deformation, 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, strain, stress, tension,
thermal loading,
torsion, twisting, vibration, and/or a combination thereof.
In another embodiment, a first computer program may evaluate the impact of the
material features upon the material by operating on the material features. The
operation
may be guided by the database constraints and/or any material historical data.
In one
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possible embodiment, the first computer program evaluates the fitness for
service of the
material under the constraints.
In another possible embodiment, a material evaluation system may comprise a
computer, a material features acquisition system, a first database comprising
of
constraints and/or material historical data; and/or a data conversion program,
whereby
the material features may be rendered in a data format for use by a finite
element
analysis engine.
In another possible embodiment, the invention may comprise a sensor with an
output comprising of signals indicative of features from the material being
scanned, in a
time-varying electrical form. A sensor interface may be provided for the
computer,
wherein the computer converts the signals to a digital format. Additional
elements may
comprise at least one database comprising of material features recognition
constraints
and/or historical data. A computer program may be executed on the computer for
identifying the material features detected by said sensor.
These and other embodiments, objectives, features, and advantages of the
present invention will become apparent from the drawings, the descriptions
given herein,
and the appended 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 therefore do not form a comprehensive or
restrictive list of
embodiments, objectives, features, and/or advantages.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 illustrates a block diagram of a 1 D-EMI non-destructive inspection
system;
FIG. 2A illustrates a 1 D-EMI inspection trace for a mid-wall imperfection;
FIG. 2B illustrates a 1 D-EMI inspection trace for machined (man-made)
calibration notches;
FIG. 2C illustrates the flaw spectrum of the mid-wall imperfection of FIG. 2A;
FIG. 2D illustrates the flaw spectrum of the machined (man-made) calibration
notches of FIG. 213;
FIG. 3A illustrates a section of MUI with an imperfection;
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FIG. 3B illustrates a section of MUI following remediation;
FIG. 3C illustrates a section of MUI following incomplete remediation;
FIG. 3D illustrates the stress concentration;
FIG. 4 illustrates a block diagram of the AutoFFS system according to the
present invention;
FIG. 5 illustrates a block diagram of the AutoFFS system and the speech and
sound interface according to the present invention;
FIG. 6 illustrates a block diagram of a speech synthesizer, a sound
synthesizer
and a Speech recognition engine;
FIG. 7 illustrates a block-diagram of the inspection sensor pre-processor and
the
filter arrangement according to the present invention;
FIG. 8A illustrates a programmable gain amplifier according to the present
invention;
FIG. 8B illustrates the design mathematical formula for the programmable gain
amplifier of FIG. 8A according to the present invention;
FIG. 9A illustrates a programmable 3<sup>rd</sup> order low-pass filter according to
the
present invention;
FIG. 9B illustrates the design mathematical formula for a 1<sup>st</sup> order low
pass
filter of FIG. 9A according to the present invention;
FIG. 8C illustrates the design mathematical formula for a 2<sup>nd</sup> order low
pass
filter of FIG. 9A according to the present invention;
FIG. 10A illustrates a programmable band-pass filter and a 3<sup>rd</sup> order high-
pass filter according to the present invention;
FIG. 10B illustrates the design mathematical formula for a 1<sup>st</sup> order high-
pass filter of FIG. 10A according to the present invention;
FIG. 10C illustrates the design mathematical formula for a 2<sup>nd</sup> order high-
pass filter of FIG. 10A according to the present invention;
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FIG. 11A illustrates the Bilinear Transformation, a mathematical technique to
translate an analog transfer function to the digital domain, according to the
present
invention;
FIG. 11 B illustrates the mathematical formula for the Bilinear Transformation
illustrated in FIG. 11A according to the present invention;
FIG. 11C illustrates a mathematical formula for the frequency response of IIR
Digital filter for the Bilinear Transformation illustrated in FIG. 11A
according to the
present invention;
FIG. 12A illustrates the block-diagram to implement the discrete wavelet
transform decomposition through digital filter banks according to the present
invention;
FIG. 12B illustrates a mathematical formula for a low-pass filter of a HAAR
wavelet of FIG. 12A according to the present invention;
FIG. 12C illustrates a mathematical formula for a high-pass filter of a HAAR
wavelet of FIG. 12A according to the present invention;
FIG. 13 illustrates a block diagram of the signal processing of AutoFFS system
according to the present invention;
FIG. 14 illustrates a flow chart of a typical FFS assessment according to the
present invention;
FIG. 15 illustrates a typical FFS assessment time sequence according to the
present invention;
FIG. 16A illustrates a typical material sample with man-made features;
FIG. 16B illustrates a typical material sample with a critically flawed area;
FIG. 16C illustrates typical reference defects found in 1 D-NDI standards;
FIG. 16D a critically flawed area;
FIG. 17 illustrates a block diagram of a typical NDI process;
FIG. 18 illustrates a block diagram of the AutoFFS process according to the
present invention; and
FIG. 19 illustrates an AutoFFS computer readout.
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DETAILED DESCRIPTION OF EMBODIMENTS OF THE INVENTION
The following Trademarks are referred to herein below in alphabetical order:
Compact Flaw Spectrum, CoiIBOT, CyberCHECK, Cyberlnspector, CyberSCAN,
CyberSCOPE, Defect Numerical Analysis, Flaw Defining Dimension, FDDim, Flaw
Spectrum, InspectionBOT, LineBOT, Material Status Descriptive Value, MSDV,
RaiIBOT, Rig Data Integration System, RDIS-10, RiserBOT, STYLWAN and WeIIBOT
are trademarks of STYLWAN Incorporated.
OCI-5000 series and OCI are trademarks of OLYMPIC CONTROL, Incorporated.
To understand the terms associated with the present invention, the following
descriptions 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.
Autonomous: able to perform a function without external control or
intervention.
Classification: assigning a feature to a particular class.
Compact Flaw Spectrum: a condensed presentation of Flaw Spectrum or
Frequency Flaw Spectrum data. The STYLWAN Compact Flaw Spectrum and trace
color assignments is set out herein below and spans from wall thickness (3-DC)
to
microcracking (2-DC): C-3D (blue), C-3d (green), C-2d (red) and C-2D (magenta)
and
Geometry variations 3-G (yellow).
Constraints: controls in doing something. Constraints include, but are not
limited
to knowledge, rules, boundaries and data.
Decomposed in Frequency: Separating desirable characteristics from a
frequency response gathered during an evaluation process.
Defect: an imperfection that exceeds a specified threshold and may warrant
rejection of the material.
Degradation Mechanism: the phenomenon that is harmful to the material.
Degradation is typically cumulative and irreversible such as fatigue built-up.
Essential: important, absolutely necessary.
Expert: someone who is skilful and well informed in a particular field.
Feature: a property, attribute or characteristic that sets something apart.
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Finite Element Analysis: a method to solve the partial or ordinary
differential
equations that guide physical systems, (herein after referred to as "FEA").
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 under different loads and/or conditions, such as a marine drilling
riser under
tension and enduring vortex induced vibration. An FEA engine may analyze a
structure
with a feature under static and/or dynamic loading, but not a feature on its
own.
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 remaining useful life predictions. In the prior art, FFS
assessment
was typically performed by an expert or a group of experts. Typically, an FFS
assessment is based primarily on as-designed data while the AutoFFS assessment
is
based primarily on as-built or as-is data. When design data is available,
AutoFFS also
monitors compliance with the design data. When less than optimal data is
available,
AutoFFS may perform a Fitness For Service Screening.
Flaw Defining Dimension: (Herein after referred to as "FDDim") typically the
flaw
dimension and/or projection perpendicular (transverse) to the maximum stress.
The
extraction matrix calculates FDDim. The extraction matrix was published in
1994 and it is
beyond the scope of this patent.
Flaw Spectrum: a presentation of data derived from an extraction matrix. The
STYLWAN Flaw Spectrum and trace color assignments is set out herein below and
spans from wall thickness (A-WDS) to microcracking (2-D): A-WDS (maroon), R-
WDS
(black), 3-D (blue), 3-d (cyan), C (green), 2-d (red) and 2-D (magenta) and
Geometry
variations 3-G (yellow). When necessary, categories are further subdivided to
.alpha.,
.beta. and .gamma., such as 2-da. lt should be understood that the one to one
correspondence of simple imperfections to the STYLWAN Flaw Spectrum
occasionally
applies to machined (man-made) imperfections and not to the complex form
imperfections typically found in nature. Therefore, the STYLWAN Flaw Spectrum
elements must be viewed as an entity identification signature, just like DNA,
but not as a
detailed chemical analysis. It should be appreciated that mere changes in
terminology
and/or regrouping and/or recategorizing cannot render such terms as being
outside the
scope of the present invention.
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Frequency Based Flaw Spectrum: a presentation of data derived from one-
dimensional or two-dimensional sensor in combination with filter banks to
decompose,
interpret and categorize the sensor received information in a fashion
substantially similar
to the flaw spectrum. It should be understood that any further processing,
such as the
AutoFFS processing, utilizes the Flaw Spectrum regardless of its origin and
derivation
method.
Imperfection or Flaw: one of the material features--a discontinuity,
irregularity,
anomaly, inhomogenity, or a rupture in the material under inspection.
Knowledge: a collection of facts and rules capturing the knowledge of one or
more specialist.
Normalization: Amplitude, and/or phase, and/or bandwidth, and/or time shifting
adjustments of the inspection sensor output to compensate for the system
implementation idiosyncrasies that affect the features sensor output such as
changes/differences due to scanning speed and/or implementation geometry
and/or
excitation and/or for response characteristics of the inspection sensor.
Productivity: The total amount of material undergone assessment or evaluation.
The productivity rate is defined as the ratio of amount of material undergone
assessment
or evaluation over the amount of time to perform such assessment or
evaluation.
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 material (a structure) is expected to be available for reliable use.
Remaining Useful Life Estimation (herein after referred to as "RULE"):
establishes the next monitoring interval (condition based maintenance) or the
need for
remediation but it is not intended to establish the exact time of a failure.
When RULE can
be established with reasonable certainty, the next monitoring interval may
also be
established with reasonable certainty. When RULE cannot be established with
reasonable certainty, then RULE may establish the remediation method and upon
completion of the remediation, the next monitoring interval may be
established. When
end of useful life is established with reasonable certainty, alteration and/or
repair and/or
replacement may be delayed under continuous monitoring.
Response Characteristics: Desirable characteristics separated from a frequency
response to be evaluated preferably by a computer to determine imperfections.
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Rules: how something should be done to implement the facts.
Scanning Speed: The speed of the material passing the sensor (or the speed of
the sensor along the material).
1 D-EMI Inspection Equipment Description
FIG. 1 illustrates a block diagram of an eight channel 1D-EMI inspection
system
similar to the one in U.S. Pat. No. 2,685,672 utilizing the MFL principle. In
particular, the
sensors and their arrangement as described in 672 FIGS. 5 and 6 are still in
use with
hundreds of units employed worldwide in portable or stationary configurations.
The same
sensor configuration is also illustrated in FIG. 7 of U.S. Pat. No. 2,881,386
and similar
sensors configuration is also used in the pipeline pig of U.S. Pat. No.
3,225,293.
The magnetizing coil 3 of the inspection head 2 induces excitation into MUI 1.
It
should be understood that the magnetic field can be applied in any direction.
U.S. Pat.
No. 2,685,672 shows the induction of a longitudinal magnetic field while U.S.
Pat. No.
3,202,914 shows the induction of a transverse magnetic field. It should
further be
understood that one or more permanent magnets may be use instead of a
magnetizing
coil or a combination thereof. The inspection sensors 4 signals 4A through 4H
are
processed by the high-pass filters 11A through 18A to eliminate low
frequencies and any
dc components. The signals 4A through 4H are then amplified by amplifiers 11 B
through
18B and are then filtered by the low-pass filters 11 C through 18C to
eliminate high
frequencies. The highest signal selector 10 compares the highest of the band-
limited
signals 4A through 4H to a preset threshold level and eliminates all signals
below the
threshold level. Thus, the inspection trace 5 that is presented to the
inspector typically
shows the frequency band-limited highest signal that exceeds a preset
threshold level.
This type of signal acquisition and processing creates detection dead-zones
and it is not
suitable for FFS assessment or screening.
The MFL principle of operation is eloquently described in U.S. Pat. No.
2,194,229: "It is old in the art to test magnetic material for flaws by
passing therethrough
a magnetic flux, providing means responsive to variations in the flux, and
thereby
locating regions of abnormal magnetic reluctance"; and herein lies the problem
that has
plagued the 1 D-NDI all along. 1 D-EMI units flag " . . . regions of abnormal
magnetic
reluctance" in ferromagnetic materials and UT units flag regions of echoes.
They do not
identify the material features; they do not detect the failure-potential of
any feature,
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including but not limited to imperfection, and most importantly, they do not
assess the
material fitness for service under the application constraints. Instead, they
rely upon an
inspector to monitor and interpret the MFL or UT traces and instruct a manual
verification crew to locate the flagged " . . . regions of abnormal magnetic
reluctance" or
echoes on the MUI for further manual investigation, but only for MUI regions
that give
rise to signals that exceed a preset magnitude threshold, a 1 D-NDI
shortcoming that can
still be found, for example, in U.S. Pat. No. 6,594,591 FIG. 9 and will be
discussed in
detail further below. Thus, OCTG owners typically specify that the
verification crew
investigate at least ±six inches on either side of an indication. It is not
uncommon for
the verification crew to miss entirely the flagged MUI region or even the
flagged MUI
from a simple miscount. This manual verification problem is exemplified on
pipelines that
are miles long or railroads, a two vehicle inspection/verification solution
described in
U.S. Pat. No. 5,970,438.
Once an imperfection is located by the verification crew and sufficient
measurements are recorded, the information is forwarded to the owner of the
MUI to
decide its disposition. In order to decide the disposition of the MUI, the
owner preferably
performs an FFS assessment with the limited data the verification crew was
able to
gather. Often, a single pass/fail approach is implemented.
It is therefore desirable to provide means to retrofit AutoFFS to the hundreds
of
1 D-EMI units deployed worldwide. It is imperative therefore, that AutoFFS
detects and
recognizes the "as-is" MUA features impacting its FFS including, but not
limited to,
imperfections. The imperfection recognition was discussed in the AutoNDI prior
application Ser. No. 10/995,692 (U.S. Pat. No. 7,155,369) using the extraction
matrix
and application Ser. No. 11/079,745 (U.S. Pat. No. 7,231,320) using spectral
analysis to
derive a frequency based flaw spectrum for further use by the AutoNDl.
A Brief 1 D-EMI History
The one to one correspondence of FIG. 1 1 D-EMI elements to the elements
illustrated in FIG. I of U.S. Pat. No. 1,823,810 is as follows: A magnetic
field (excitation)
is induced into MUI 1 (810 FIG. 1 magnetizable material 6) by a coil 3 (810
FIG. 1
exciting coil 14). The sensor 4 (810 FIG. 1 search coil 19) signal is
processed by the
high-pass filter 11A (810 FIG. 1 capacitor and resistor connected to the grid
of the
vacuum tube) and it is then amplified by amplifier 11 B (810 FIG. 1 dual
triode vacuum
tube) and presented to the inspector (810 FIG. 1 indicator 21) instead of an
inspection
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trace 5. The limited frequency response of (810 FIG. 1 indicator 21) acts as a
lowpass
filter 11C. Since U.S. Pat. No. 1,823,810 depicts a single channel NDI system,
there is
no need for a highest signal selector 10. However, the sensor 4 (810 FIG. 1
search coil
19) signal is compared to an operator adjustable threshold level (810 FIG. 1
the resistor
connected to the grid of the first vacuum tube is connected to a negative
(threshold)
voltage). Only sensor signals that exceed this threshold (negative voltage)
would
propagate and be shown to the inspector (810 FIG. 1 indicator 21).
The prolific 1 D-EMI unit of U.S. Pat. No. 2,685,672 essentially consists of
eight
U.S. Pat. No. 1,823,810 channels with the addition of a highest signal
selector. It should
be understood that 1 D-EMI units consisting of two to forty eight channels
have also been
constructed and the number of channels any 1 D-EMI deploys should not be
interpreted
as a limitation to this invention. In the 1960s the vacuum tubes were replaced
by
transistors, as shown in U.S. Pat. No. 3,202,914 FIG. 6, and in the 1970s by
integrated
circuit amplifiers. Meters and chart recorders were used for the operator
readout until the
mid 1980s when they were replaced by computers with their colorful displays
and
printouts. However, no matter how sophisticated the operator readout is, it
will never
show information lost during the acquisition and processing of the sensor
signals.
The brief 1 D-EMI history shows that although the electronic circuits have
followed the advances in technology, the inspection philosophy and methods
have not.
The 1 D-EMI limitations and pitfalls of a century ago still plague the modern
I D-EMI,
regardless of the inspection technique used. For example, U.S. Pat. No.
6,594,591
applies the combination of sensor signal filtering and threshold prior to any
signal
evaluation to both EMI and UT.
1 D-EMI Loss of Sensor Signal Frequency Spectrum Information
As discussed earlier, the 1D-EMI high-pass filters 11A through 18A eliminate
low
frequencies and dc components for system stability and the low-pass filters 11
C through
18C to eliminate high frequencies to remove the "noise". Useful frequency
components
of the sensor signal are therefore discarded before being evaluated and any
useful
information they may contain is prematurely and irreversibly lost rendering
this type of
signal acquisition and processing unsuitable for AutoFFS. Referring to FIG. 6
of U.S.
Pat. No. 3,202,914, capacitor 51 and its associated components form a high-
pass filter
that prematurely and irreversibly discards low-frequency components of the
sensor
signal while capacitor 48 and its associated components form a low-pass filter
that
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prematurely and irreversibly discards high-frequency components of the sensor
signal.
Other such examples can be found in U.S. Pat. No. 2,582,437 (see FIG. 1
capacitor 13
and resistor 40); in U.S. Pat. No. 1,823,810 (see FIG. 1, amplifier 20) as
well as in U.S.
Pat. No. 5,671,155 (see FIG. 1, AC-couplers 6) and U.S. Pat. No. 5,943,632
(see FIG. 1,
AC-couplers 6). Another such example using digital filters is shown in FIG. 8
of U.S. Pat.
No. 5,371,462 showing a " . . . flow chart of an algorithm for pre-processing
to remove
DC and low frequency components" from the sensor signal.
Scanning speed effects on the sensor signal U.S. Pat. No. 2,770,773 also
encompasses many elements of the above to detect corrosion pitting and clearly
states
a frequency spectrum processing essential element: the imperfection frequency
spectrum versus scanning speed interdependence. The high-pass filters of FIG.
7
(capacitors 66, 67 and resistors 69, 70) remove many unwanted " . . . signal
producing
variables such as separation from the casing wall, wall roughness,
misfit..."[Column
6, Line 15]. Following the high-pass filter is a band-pass filter " . . . to
pass frequencies in
the band between about 3 and 20 cycles per second, as this is the
characteristic
frequency range of signal due to the passage of the shoe 15 across a casing
corrosion
pit at a transverse speed of twenty feet per minute. This frequency band
related to the
speed of traverse of the instrument 10 through the casing 12 will, of course,
be varied to
suit any other traverse speed selected" [Column 6, Line 33]. Therefore, it is
well known
in the art that the same imperfection will appear differently in the sensor
signal frequency
spectrum depending on the scanning speed. It is also well known in the art
that fixed
frequency filters always pass/discard the same frequency band, thus 1 D-EMI
systems,
such as the ones in U.S. Pat. Nos. 5,671,155 and 5,943,632, always propagate
for
further processing undefined frequency components of the sensor signal again,
rendering this type of signal acquisition and processing unsuitable for
AutoFFS.
Another early observation of the NDI industry is the scanning speed versus
signal amplitude proportional interdependence for coil sensors. U.S. Pat. No.
2,881,386
(see FIGS. 10 and 11) provides a technique for amplitude compensation for the
scanning speed variations.
AutoFFS can only be carried out when the MUA features, including but not
limited to imperfections, are recognized and are identified. Automatic
features
recognition demands that the features signal amplitude and frequency spectrum
be
compensated for the idiosyncrasies of the scanning system and the effects of
the
14
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different scanning speeds. It is one possible objective of this embodiment of
the
invention to determine the inspection system response characteristics, to
track the
scanning speed and preserve, normalize and automatically analyze the sensor
signal
frequency spectrum for all the spectrum components that include non-redundant
information spanning from Fatigue to wall thickness.
1 D-NDI Calibration Limitations
FIG. 2A illustrates the 1 D-NDI inspection trace 5 of an OCTG that failed at
imperfection 5B. The OCTG had two mid-wall imperfections 5A and 5B and failed
during
hydrotesting. Prior to assembly into a marine drilling riser, the OCTG was
inspected by a
state of the art 1 D-NDI. The 1 D-NDI system was calibrated using a
calibration joint with
machined notches, a 1 D inspection industry standard but faulty practice
further
illustrated in FIG. 16C. FIG. 2B illustrates the ID-NDI inspection trace of a
calibration
notch 5C.
FIG. 2C illustrates the flaw spectrum of the same OCTG mid-wall imperfections
6A and 6B, corresponding to 5A and 5B, and FIG. 2D illustrates the Stylwan
flaw
spectrum of the calibration notch 6C, corresponding to 5C. The reason of this
failure can
easily be deduced from FIGS. 2C and 2D. The Stylwan flaw spectrum of FIG. 2C
clearly
shows that the mid-wall imperfections 6A and 6B are utterly unrelated to the
calibration
notches 6C of FIG. 2D. It is also easy to see how 1 D-NDI would mislead
someone to
believe that the mid-wall imperfections 5A and 5B of FIG. 2A are somehow
related to the
calibration notches 5C of FIG. 2B and setup the 1 D-NDI equipment erroneously,
having
no way of knowing any better (Knowing that the high threshold level set by the
calibration will hinder the detection of the mid-wall imperfections 5A and 5B
with 1 D-NDI
until they burst during hydrotesting). Imperfections 5A and 5B were missed by
1 D-NDI
because their signal amplitude did not exceed the threshold level that was
erroneously
set to detect machined calibration notches. It should also be noted that when
a single
pass/fail measure is utilized, it would eventually lead to equipment that
focus on passing
the particular single measure. This is also the case with 1 D-NDI. Closer
scrutiny of the
1 D-NDI sensor structure and signal processing would show that both are fine
tuned to
pass the calibration notches test while they are likely to miss imperfection
7B of FIG. 3,
yet another 1 D-NDI problem root cause. It is another possible objective of an
embodiment of the invention to establish a scanning/inspection system
calibration
means and methods adept for AutoFFS
CA 02632490 2008-05-22
1 D-NDI Remediation Limitations
FIG. 3A illustrates a section of MUI with an imperfection 7A. A typical 1 D-
NDI
remediation practice states: "An external imperfection may be removed by
grinding ....
Where grinding is performed, generous radii shall be employed to prevent
abrupt
changes in wall thickness .... The area from which the defect is removed shall
be
reinspected . . . to verify complete removal of the defect". This statement
further
illustrates the limitations of 1 D-NDI.
"Grinding" actually does not "remove" an imperfection. It just shifts the
imperfection 7A morphology (shape) from one with high stress concentration 8A
(due to
the "abrupt changes in wall thickness") to imperfection 7B with lower stress
concentration 8B (due to the "generous radii"). For example, if the depth of
the original
external imperfection 7A was 10% of the material wall thickness, the wall loss
in the
OCTG region 7B would still be 10% (or greater) even after the imperfection 7A
was
morphed ("completely removed") into 7B by "grinding", resulting in an OCTG
with altered
FFS and reduced remaining-useful-life.
As discussed earlier, the 1 D-EMI high-pass filters 11A through 18A eliminate
low
frequencies and de components and therefore prematurely and irreversibly
eliminate
imperfection 7B information thus, creating "detection dead-zones" misleading
great
many to believe and actually verify the "complete removal of the defect", when
in fact,
the form-shifted "external imperfection" 7B is still clearly visible with the
naked eye and
the wall loss is still 10% (or more). If imperfection 7B was the result of
OCTG stretching,
such as a neckdown, instead of grinding, 1 D-NDI would miss imperfection 7B
entirely
and classify the OCTG with the reduced cross sectional area erroneously.
Strength of
material knowledge teaches that the reduced cross sectional area of the
material
reduces the ability of the material to absorb energy thus altering its FFS and
reduces its
remaining-useful-life.
FIG. 3C illustrates a dangerous condition where imperfection 7A was partially
morphed leaving behind a failure seed 7C with increased stress concentration
8C. A
similar example is shown in FIG. 16D element 159. 1D-NDI would miss
imperfection 7C
as a result of the 1 D-NDI detection dead-zones arising from the combination
of filters
and threshold. It should be understood that this recommended 1 D-NDI
remediation
method does not take into account the imperfection neighborhood nor does it
optimize
the FFS or the remaining useful life of the OCTG. It is yet another possible
objective of
16
CA 02632490 2008-05-22
an embodiment of the invention to establish remediation means and methods
adept for
AutoFFS.
1 D-NDI Magnitude Threshold Versus Imperfection Pattern Recognition
As FIG. 2C illustrates, the Stylwan flaw spectrum detection of imperfections
6A
and 6B is based on pattern recognition, not signal amplitude alone. Therefore,
failure
seeds, like imperfection 6A, can be detected early on regardless of their
signal
amplitude.
By now, it should be easy to recognize the calibration notches 6C. However,
those notches were machined on new defect-free material and they meet strict
geometry
standards, as it is further shown in FIG. 16C. Therefore, their flaw spectrum
signature is
extremely simple and easily recognizable. On the other hand, imperfections in
nature are
mostly found on used material, they are rarely alone, they are multifaceted
and give rise
to complex flaw spectrums that are not always easily recognizable.
Furthermore, a key
weaknesses of any manual process, such as the manual verification, is the
uncontrollable "human factors". If imperfection 6A was found instead on
heavily used
material along with other imperfections, would a trained inspector always
distinguish it in
the resulting flaw spectrum clutter? It is the aim of this invention to answer
this question
with confidence by providing a computer, a sensor interface and a program to
scan the
sensor signals for patterns to recognize material features, including but not
limited to
imperfections. Again, features recognition demands that the frequency spectrum
of the
sensor signals that include non-redundant information spanning from Fatigue to
wall
thickness is preserved and normalized.
Root Cause Identification Versus Simplistic Explanation
It should be apparent from the above that the 1D-NDI detection dead-zones,
limitations and pitfalls of a century ago do not adequately address the
material needs of
the modern applications and they fall short in active failure prevention.
Again, a century
ago there was no drilling a 20,000 foot well in 10,000 feet of water in search
for
hydrocarbons or trains traveling at speeds in excess of 100 miles per hour or
supersonic
aircraft. These detection dead-zones, limitations and shortcomings of the 1 D-
NDI lead to
a futile long term cycle as detailed below.
Often, when a failure occurs, the focus is on repairing/replacing the damaged
material as rapidly as possible in order to reduce downtime and at the lowest
possible
17
CA 02632490 2008-05-22
cost. Occasionally, the obligatory "why?" is asked and a simplistic
explanation like
"fatigue cracks are a fact of life" is accepted as an adequate answer; a human
decision
that may lead to a catastrophic failure much later. Occasionally, an inspector
or even a
1 D-NDI service provider may be replaced with another one using the exact same
methods and equipment. One should bear in mind the heavy dependence of 1 D-NDI
upon the inspector and the industry desire to preserve the 1 D-NDI equipment
"reputation". At some point, someone examines the number of failures over the
years
and discovers that there is a compatible number of failures with 1 D-NDI as it
is without
1D-NDI. The simplistic explanation then is that NDI is a pointless expense and
it is
therefore reduced or bypassed entirely; yet another human decision that may
lead to a
catastrophic failure much later. For example, the manufacturer and the owner
of the
marine drilling riser depicted in FIGS. 2A through 2D may reach such a
conclusion. The
compound effect of those decisions, often spanning many years, eventually
leads to a
spectacular catastrophic failure somewhere. The simplistic explanation for
this
spectacular failure is easily identified as the reduced or bypassed NDI and
the simplistic
motive is identified as "greed". Again, it should be noted that this string of
latent root-
causes typically spans many years and possibly different groups of individuals
making it
difficult, if not impossible, to pinpoint its origins. The "greed" simplistic
explanation
however, is readily accepted and after the obligatory hearings, firings and
fines, 1 D-NDI
is mandated starting the vicious cycle all over again. The increased awareness
is
typically short-lived.
However, this approach treats the results of a problem and does not seek to
identify and analyze the root-cause of the problem. Unless an excavator
accidentally hits
a pipeline'for example, pipeline material deterioration occurs over time
eventually
leading to a failure. There is no single deterioration root-cause acting
equally upon a 500
mile pipeline for example with some of it failing within the 1 D-NDI detection
dead-zones.
Along the pipeline length, different deterioration root-causes may be acting
upon the
pipeline resulting in different deterioration rates, but 1 D-NDI is inherently
incapable of
effectively identifying those root-causes as illustrated in FIGS. 2A and 2B.
The objective
of this invention to recognize features, including imperfections, is the first
step in
identifying the root-cause of material deterioration leading to effective
failure preventive
action.
Description of AutoFFS Computer
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FIG. 4 illustrates an AutoFFS block diagram further illustrating the computer
20,
the features detection interface 30, the speech and sound interface 40 and the
preferable information exchange among the components of the AutoFFS. It should
be
understood that the AutoFFS computer 20 may include more than just one
computer
such as a cluster of interconnected computers. It should be understood that
the
computer 20 does not necessarily comprise a laptop or portable personal
computer and
such misinterpretation should not be made from the illustrations in the
figures and shall
not be read as a limitation herein. The computer 20 preferably comprises a
display 21,
keyboard 22, storage 23, for storing and accessing data, a microphone 27, a
speaker 28
and a camera 29. It should be understood that the display 21, the keyboard 22,
the
speaker 28 and the microphone 27 may be local to the computer 20, may be
remote,
may be portable, or any combination thereof. It should be further understood
that
camera 29 may comprise more than one camera. Further camera 29 may utilize
visible
light, infrared light, any other spectrum component, or any combination
thereof. The
camera 29 may be used to relay an image or a measurement such as a temperature
measurement, a dimensional measurement (such as 3-G of the flaw spectrum), a
comparative measurement, character and/or code recognition, such as a serial
number,
or any combination thereof including information to identify the MUA 9 and/or
the
authorized operator through biometric recognition. It should be appreciated
that the
storage 23 may comprise hard disks, floppy disks, compact discs, magnetic
tapes,
DVDs, memory, and other storage devices. The computer 20 may transmit and
receive
data through at least one communication link 26 and may send data to a printer
or chart
recorder 24 for further visual confirmation of the inspection data 25 and
other related
information. It should be understood that communication link 26 may be in
communication through wired or wireless means, including but not limited to
RFID,
optical links, satellite, radio and other communication devices. At least one
communication link 26 may facilitate communication with an expert in a remote
location
or an identification tag, such as RFID, embedded in MUA 9. Such embedded
identification tags are described in U.S. Pat. No. 4,698,631, No. 5,202,680,
No.
6,480,811 and No. 7,159,654 and are commercially available from multiple
sources. The
computer 20 preferably provides for data exchange with the features detection
interface
30 and the speech and sound interface 40.
Speech and Voice Control
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CA 02632490 2008-05-22
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 AutoFFS would preferably enable the operator to focus on the MUA 9 and
other
related activities while maintaining full control of the AutoFFS. Furthermore,
the AutoFFS
natural speech interaction preferably allows the operator to operate the
AutoFFS while
wearing gloves or with dirty hands as he/she will not need to constantly
physically
manipulate the system. Although various types of voice interaction are known
in the art,
many problems still exist in an industrial setting due to the potential of an
excessive
noise environment. Thus, this invention preferably provides for natural speech
interaction between the human operator and the AutoFFS capable of deployment
under
adverse conditions.
FIG. 5 illustrates a block diagram of the AutoFFS system and the natural
speech
and sound interface 40 according to the present invention. Preferably, a
natural speech
command is received by the microphone 27 or other sound receiving device. The
received sound is preferably amplified, such as by the amplifier 72. Amplifier
72 may be
a programmable gain amplifier 80 as depicted in FIG. 8A. A feature of the
embodiment is
that the microphone amplifier 72 is followed by a bank of band-reject notch
filters 71.
Preferably, the operator and/or the software can adjust the amplifier 72 gain
and the
center frequency of the notch filters 71. Such a notch filter may be
constructed by
moving the low-pass filter 90 of FIG. 9A to the output 108 of the high-pass
filter 100 of
FIG. 10A. Since industrial noise is primarily machine generated, it typically
consists of a
single frequency and its harmonics. Therefore, adjustable notch filters 71 are
well suited
for the rejection of industrial noise. The notch filters 71 are preferably
followed by the
speech and sound recognition engine 70. The data from the speech and sound
recognition engine 70 is preferably exchanged with the computer 20. Data from
the
computer 20 may be received by a sound synthesizer 50 and a speech synthesizer
60.
The data received by the speech synthesizer 60 is converted into natural
speech and is
preferably broadcast through a speaker 28 It should be understood that each
synthesizer may be connected to a separate speaker or multiple speakers and
that in a
different embodiment the speech synthesizer 60 and the sound synthesizer 50
may be
integrated into a single function, the speech and sound synthesizer.
AutoFFS may be deployed on location, such as a wellsite, a chemical plant or
refinery, an airport tarmac or a bridge, a storage yard or facility, a
manufacturing facility,
CA 02632490 2008-05-22
such as a pipe mill, a locomotive and in general in a noisy industrial and/or
transportation environment. AutoFFS rarely is deployed in a laboratory where
typical
sound levels, similar to a bank lobby, may be in the range of 40 db to 50 db
while factory
or industrial sound levels may exceed 80 db. A frequency bandwidth of
substantially 300
Hz to 2500 Hz and a dynamic range of substantially 40 db may be adequate for
good
quality speech with good quality listenability and intelligibility. Industrial
noise may also
be present in the same frequency range. The notch filters 71 may be "parked"
outside of
this frequency range or bypassed altogether when the noise level is
acceptable. When a
machine, a jet engine, or other device starts suddenly, the notch filters 71
would
preferably sweep to match the predominant noise frequencies. The notch filters
71 may
be activated either manually or through a fast tracking digital signal
processing
algorithm. Narrow notch filters 71 with a substantially 40 db rejection are
known in the art
and can thus be readily designed and implemented by those skilled in the art.
Furthermore, it should be understood that standard noise cancellation
techniques could
also be applied to the output of the sound synthesizer 50 and the speech
synthesizer 60
when the speaker 28 comprises a set of earphones such as in a headset.
Language Selection
It should be further understood that different AutoFFS may be programmed in
different languages and/or with different commands but substantially
performing the
same overall function. The language capability of the AutoFFS may be
configured to
meet a wide variety of needs. Some examples of language capability, not to be
viewed
as limiting, may comprise recognizing speech in one language and responding 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
instructions; simultaneously recognizing more than one language or
simultaneously
responding in more than one language; or any other desired combination
therein. It
should be understood that the multilanguage capability of the AutoFFS 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.
21
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Operator Identification and Security
Preferably, at least some degree of security and an assurance of safe
operation,
for the AutoFFS, is achieved by verifying the voiceprint of the operator
and/or through
facial or irisscan or fingerprint identification through camera 29 or any
other biometric
device. With voiceprint identification, the likelihood of a false command
being carried out
is minimized or substantially eliminated. It should be appreciated that
similar to a
fingerprint, an irisscan, or any other biometric, which can also be used for
equipment
security, a voiceprint identifies the unique characteristics of the operator's
voice. Thus,
the voiceprint coupled with passwords will preferably create a substantially
secure and
false command immune operating environment.
It should be further understood that the authorize operator may also be
identified
by plugging-in AutoFFS 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.
Assessment Trace to Sound Conversion
The prior art does not present any solution for the conversion of the
assessment
signals, including but not limited to inspection signals, also referred to as
"assessment
traces", to speech or sound. The present invention utilizes psychoacoustic
principles and
modeling to achieve this conversion and to drive the sound synthesizer 50 with
the
resulting sound being broadcast through the speaker 28 or a different speaker.
Thus, the
assessment signals may be listened to alone or in conjunction with the AutoFFS
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
AutoFFS console and the typical readout instruments. Furthermore, the audible
feedback is selected to maximize the amount of information without overload or
fatigue.
This trace-to-sound conversion also addresses the dilemma of silence, which
may occur
when the AutoFFS has nothing to report. Typically, in such a case, the
operator is not
sure if the AutoFFS is silent due to the lack of features or if it is silent
because it stopped
operating. Furthermore, certain MUI 1 features such as, but not limited to,
collars or
welds can be observed visually and the synchronized audio response of the
AutoFFS
adds a degree of security to anyone listening. A wearable graphics display,
such as an
eyepiece, could serve as the remote display 21 to further enhance the process
away
from the AutoFFS console.
22
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It shoUld be understood that the assessment trace(s) to sound conversion is
not
similar to an annoying chime indicating that an automobile door is open, or
that there is a
message in an answering machine. The time varying AutoFFS processing results
are
converted to sound of sufficient amount and quality through psychoacoustic
principles
and modeling, as to enable the operator to monitor and carry out the entire
AutoFFS
process from a remote location without annoying the operator or resulting in
operator
overload or operator "zone-out". It should further be understood that a switch
contact
closure indicating that an automobile door is open or a vending machine bin is
empty
does not constitute an FFS assessment as it is not different than turning on
the lights in
a room. Conversely, a chime may be attached to the light to indicate that it
is on or even
a voice synthesizer to say "the light is on". Similarly, lights may be
attached to a doorbell
switch closure to assist a hearing impaired person, however, none of these
devices or
actions constitute an FFS assessment.
AutoFFS Speech
Text to speech is highly advanced and may be implemented without great
difficulty. Preferably, when utilizing text to speech, the Auto AutoFFS 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 AutoFFS
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
AutoFFS clock
and/or calendar utilizing, but not limited to, such phrases as "ten thirty two
am", or
"Monday April eleven".
However, t should be understood that the primary purpose of the AutoFFS is to
relay MUA 9 information to the operator. Therefore, AutoFFS would first have
to decide
what information to relay to the operator and the related utterance structure.
AutoFFS Operation Through Speech
Preferably, the structure and length of AutoFFS utterance would be such as to
conform with the latest findings of speech research and in particular in the
area of
speech, meaning and retention. It is anticipated that during the AutoFFS
deployment, the
23
CA 02632490 2008-05-22
operator would be distracted by other tasks and may not access and process the
short
term auditory memory in time to extract a meaning. Humans tend to better
retain
information at the beginning of an utterance (primacy) and at the end of the
utterance
(regency) and therefore the AutoFFS 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
AutoFFS speech output immediately with a button or with the command "mute" and
enable the speech output with the command "speak".
The "repeat" command may be invoked at any time to repeat an AutoFFS
utterance, even when speech is in progress. Occasionally, the "repeat" command
may
be invoked because the operator failed to understand a message and therefore,
"repeat"
actually means "clarify" or "explain". Merely repeating the exact same message
again
would probably not result in better understanding, occasionally due to the
brick-wall
effect. Preferably, AutoFFS, 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. Furthermore, subsequent
"repeat"
commands may invoke the help menu to explain the meaning of the particular
utterance
in greater detail.
The operator may remain in communication with the AutoFFS in a variety of
conventional ways. Several examples, which are not intended as limiting, of
possible
ways of such communication are: being tethered to the AutoFFS; being connected
to the
AutoFFS through a network of sockets distributed throughout the site including
the
inspection head(s); being connected to the AutoFFS through an optical link
(tethered or
not); or being connected to the AutoFFS through a radio link. This frees the
operator to
move around and focus his/her attention wherever needed without interfering
with the
production rate.
It should be appreciated that the present invention may be 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 AutoFFS
process.
AutoFFS Sound Recognition
24
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AutoFFS would preferably be deployed in the MUA 9 use site and would be
exposed 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 9 speed of travel may be derived from the rig engine
sound. An
unfamiliar sound, for example, would originate from an injector head 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 AutoFFS analysis range when the AutoFFS is
equipped with
appropriate microphone(s) 27. It should also be noted that an equipment
unexpected
failure may affect adversely the MUA 9, thus training the AutoFFS to the site
familiar,
and when possible unfamiliar sounds, would be advantageous.
As discussed earlier, the notch filters 71 would preferably sweep to match the
predominant noise frequencies, thus, a noise frequency spectrum may be derived
that
may further be processed for recognition using standard AutoFFS recognition
techniques.
Description of Speech and Sound Interface
FIG. 6 illustrates a block diagram of a preferred sound synthesizer 50, speech
synthesizer 60, and speech and sound recognition engine 70. It should be
understood
that these embodiments should not be viewed as limiting and may be tailored to
specific
inspection constraints and requirements.
The sound synthesizer 50 and the speech synthesizer 60 may comprise a tunes
and notes table 51 and a vocabulary table 61 respectively. The digital-to-
analog (herein
after referred to as "D/A") converter 52, 62, the reconstruction filter 53,
63, and the
variable gain output amplifier 54, 69 are in communication with computer 20.
The tunes
and notes table 51 and a vocabulary table 61 may be implemented in a read only
memory (ROM) or any other storage device. The computer 20 preferably sequences
through the entire address sequence so that the complete digital data of the
utterance
(word, phrase, melody, tune, or sound), properly spaced in time, are converted
to an
analog signal through the D/A 52, 62. The analog signal is then preferably
band-limited
by the reconstruction filter 53, 63, amplified by the amplifier 54, 64, and
sent to the
speaker 28. Preferably, the computer 20 can vary the bandwidth of the
reconstruction
filter 53, 63 and adjust the gain of the amplifier 54, 64 which may be
programmable gain
CA 02632490 2008-05-22
amplifiers 80 as depicted in FIG. 8A. In a different embodiment, the gain of
the amplifier
54, 64 may be adjusted by the operator.
It should be understood that the tunes and notes table 51 and a vocabulary
table
61 may incorporate a built in sequencer with the computer 20 providing the
starting
address of the utterance (word, phrase, melody, tune, or sound). It should be
further
understood that the sound synthesizer 50 and the speech synthesizer 60 may
comprise
separate devices or even be combined into one device, the speech and sound
synthesizer, or even be part of a complete sound and video system such devices
being
commercially available from suppliers such as YAMAHA. It should be understood
that an
utterance may comprise of a word, a short phrase and/or sound effects such as
melodies, tunes and notes. A variable length of silence may be part of the
utterance,
which may or may not be part of the vocabulary table 61 and/or the tunes and
notes
table 51 in order to save storage space. Instead, the length of the silence
may be coded
in the table 51 and 61 and then be produced through a variable delay routine
in
computer 20.
The speech and sound recognition engine 70, may comprise an analog-to-digital
(herein after referred to as "A/D") converter 73, a spectral analyzer 74, and
the voice and
sound templates table 75 which may be implemented in a read only memory (ROM)
or
any other storage device. The description of the sequence of software steps
(math,
processing, etc.) is well known in the art, such as can be found in Texas
Instruments
applications, and will not be described in detail herein. An exemplary
hardware device is
the YAMAHA part number 4MF743A40, which provides most of the building blocks
for
the entire system.
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 75. Different 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 improve security. As discussed herein
above, code
words can be used as commands. The commands 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. Further reduction of false triggers can be
accomplished by a dual sequence of commands, such as "AutoFit" and upon a
response
26
CA 02632490 2008-05-22
from AutoFFS, such as "ready", followed by the actual command such as "Start"
issued
within a preset time interval. It should be understood that command pairs may
or may
not share trigger commands. Hardware trigger, such as a switch closure,
followed by a
speech command will be most effective in reducing false triggers.
Description of the Features Detection Interface
Computer 20 also controls and monitors a plurality of power supplies, sensors
and controls 34 that facilitate the AutoFFS process including but not limited
to MUA 9
identification and safety features. Further, computer 20 monitors/controls the
data
acquisition system 35 which preferably assimilates data from at least one
sensor 36 and
displays 21C and stores such data 23. The sensor 36 preferably provides data
such as,
but not limited to, MUA 9 location (feet of MUA 9 that passed through the head
2),
penetration rate (speed of MUA 9 moving through the head 2), applied torque,
rate of
rotation (rpm), and coupling torque. It should be appreciated that the data to
be acquired
will vary with the specific type of MUA 9 and application and thus the same
parameters
are not always measured/detected. For example, the length of the MUA 9, such
as a drill
pipe joint, may be read from the MUA 9 identification markings or from the
identification
tag embedded in the MUA 9. Furthermore and in addition to the aforementioned
techniques, computer 20 may also monitor, through the data acquisition system
35,
parameters that are related to the assessment or utilization of the MUA 9
and/or
parameters to facilitate FFS and/or remaining useful life estimation. Such
parameters
may include, but not be limited to, the MUA 9 pump pressure, external
pressure, such as
the wellhead pressure, temperature, flow rate, tension, weight, load
distribution, fluid
volume and pump rate and the like. Preferably, these parameters are measured
or
acquired through sensors and/or transducers mounted throughout the MUA 9
deployment area, such as a rig. For ease of understanding, these various
sensors and
transducers are designated with the numeral 37. The STYLWAN Rig Data
Integration
System (RDIS-10) is an example of such a hybrid system combining inspection
and data
acquisition. For instance, computer 20 may monitor, log and evaluate the
overall drilling
performance and its impact on the MUA 9 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
27
CA 02632490 2008-05-22
of the drilling process, may be processed and used as a measure to further
evaluate the
MUA 9 imperfections and its FFS and/or remaining useful life.
It should be understood that sensors, measuring devices and/or a data
acquisition system may already be installed in the MUA 9 deployment area, such
as a
drilling rig, measuring at least some of the aforementioned parameters, which
may be
available to AutoFFS through storage devices and/or through a communication
link 26
as real time data and/or as historical data.
It should be appreciated that the RDIS-10 uses the extraction matrix and
multidimensional sensors 4. When however, the multidimensional sensors and
extraction matrix are replaced with a different sensor interface and a bank of
frequency
filters, as described herein below, the RDIS-10 will substantially work as
described
herein below utilizing the frequency derived flaw spectrum.
Regardless of the specific technique utilized, the AutoFFS device will
preferably
scan the material after each use, fuse the features data with relevant
material use
parameters, and automatically determine the MUA 9 status. Thus, a function of
the
features detection interface 30 is to generate and induce excitation 31 into
the MUA 9
and detect the response, of the MUA 9, to the excitation 31. Preferably, at
least one
assessment head 2 is mounted on or inserted in the MUA 9 and the head 2 may be
stationary or travel along the MUA 9. It should be appreciated that the head 2
can be
applied to the inside as well as the outside of the MUA 9. It should be
understood that
the head 2, illustrated herein, may comprise at least one excitation inducer 3
and one or
more features sensors 4 mounted such that the FFS assessment needs of MUA 9
are
substantially covered. For features acquisition utilizing MFL, the excitation
inducer 3
typically comprises of at least one magnetizing coil and/or at least one
permanent
magnet while sensor 4 comprises of sensors that respond to magnetic field.
There is a
plethora of sensors that respond to the magnetic field such as coils, Hall-
probes,
magneto diodes, etc. The computer 20 preferably both programs and controls the
excitation 31 and the head 2 as well as receives features data from the head
sensors 4
through the features sensor interface 33. The head 2, excitation 31, and the
features
sensor interface 33 may be combined within the same physical housing. In an
alternative
embodiment, the features sensors 4 may comprise computer capability and memory
storage and thus the sensors 4 can be programmed to perform many of the tasks
of the
computer 20 or perform functions in tandem with the computer 20. It should be
also
28
CA 02632490 2008-05-22
understood that the application of the excitation 31 and the assessment of the
MUA 9
may be delayed such as AutoFFS utilizing far-field or the residual magnetic
field
whereby the MUA 9 is magnetized and it is scanned at a later time, thus the
excitation
inducer 3 and the features sensor 4 may be mounted in different physical
housings. It
should be further understood, that in such configuration, the excitation
inducer 3 may be
applied on the inside or on the outside of MUA 9 while the inspection sensor 4
may be
applied on the same side or on the opposite side of the excitation inducer 3.
It should be
further understood that either or both the excitation inducer 3 and the
features sensor 4
may be applied on both the inside and on the outside of MUA 9 so that the
assessment
needs of MUA 9 are substantially covered.
Sensor Signal Processing
Preferably, the head 2 relates time-varying continuous (analog) signals, such
as,
but not limited to, echo, reluctance, resistance, impedance, absorption,
attenuation, or
physical parameters that may or may not represent a feature of the MUA 9. For
features
acquisition utilizing MFL, head 2 relates reluctance signals in an analog
form. The
processing of Eddy-Current amplitude and phase would also result in similar
analog
signal. Features generally comprise all received signals and may include MUA 9
design
features such as tapers, imperfections, major and minor defects or other MUA 9
conditions such as surface roughness, hardness changes, composition changes,
scale,
dirt, and the like. Signals from three-dimensional sensors 4 are processed by
the
extraction matrix, that was published in 1994 and it is beyond the scope of
this patent.
The exemplary RDIS-10 uses the extraction matrix to decompose the converted
digital
signals into relevant features.
Typically, those in the 1 D-NDI art have always relied on both an inspector
and a
manual verification crew for the interpretation of the inspection signals and
any
subsequent disposition of the MUI 1. However, based on extensive strength-of-
materials
knowledge, it is well known that the severity of an MUI 1 imperfection is a
function of its
geometry, its location, and the applied loads. It is also well known, in the
art, that this
information cannot be readily obtained by a verification crew when the
imperfections in
question are located underneath coating, in the near subsurface, in the mid
wall, or in
the internal surface of the MUI 1. Any destructive action, such as removing
any coating
or cutting up the MUI 1 is beyond the scope of non-destructive inspection.
Detailed
29
CA 02632490 2008-05-22
signal analysis can extract the pertinent information from the NDI signals.
Preferably,
such detailed signal analysis would utilize signals that are continuously
related in form,
kind, space, and time.
AutoFFS Retrofit to 1 D-NDI Equipment
As discussed earlier, it is desirable to provide means to retrofit AutoFFS to
the
hundreds of 1 D-EMI units deployed worldwide. The analog signals from 1 D-NDI
or two-
dimensional sensors are decomposed in frequency. This frequency decomposition
can
take place in continuous or discrete form. In the continuous form the signals
are
decomposed through a bank of analog frequency filters and they are then
digitized by
the computer 20. In the discrete form the signals are digitized by the
computer 20 and
they are then decomposed through a bank of digital frequency filters or
mathematical
transforms.
The list of 1 D-NDI retrofit candidates includes, but is certainly not limited
to, the
OCTG inspection units described in U.S. Pat. No. 2,685,672, No. 2,881,386, No.
5,671,155, No. 5,914,596 and No. 6,580,268; the pipeline pigs described in
U.S. Pat.
No. 3,225,293, No. 3,238,448 and No. 6,847,207 and the rail inspection systems
described in U.S. Pat. No. 2,317,721, No. 5,970,438 and No. 6,594,591 and
derived or
similar units. The simplest retrofit would store the sensor information in a
memory or
transmit the sensor information through a communication link and the AutoFFS
would
post-process the data. The retrofit may consist of three-dimensional sensors
and signal
processing or frequency decomposition and signal processing as described
herein
below.
Frequency Decomposition with Analog Filters
FIG. 7 illustrates a block-diagram of the addition to the exemplary RDIS-10
imperfection sensor interface 33, illustrated as preprocessor 32, and the
filter
arrangement to decompose the inspection signals frequency spectrum and extract
relevant features in an analog format. The features extraction of the present
invention is
accomplished through a filter bank comprising of a low-pass filter 90 and a
number of
band-pass filters 100 through 160N. There is no limit on the number of band-
pass filters
that may be used, however six to eight filters are adequate for most
applications thus
dividing the sensor frequency spectrum into seven to nine features, the exact
number
depending on the specific application. For a scanning speed of 60 feet/minute
a typical
CA 02632490 2008-05-22
alignment time shift (also known as time delay) is 42 milliseconds and a
typical nine filter
sequence comprises one 12 Hz low-pass filter 90 and eight band-pass filters
100
through 100N with center frequency (bandwidth) of 15 Hz (6 Hz), 25 Hz (10 Hz),
35 Hz
(15 Hz), 50 HZ (21 Hz), 70 Hz (30 Hz), 100 Hz (42 Hz), 140 Hz (58 Hz) and 200
Hz (82
Hz). The attenuation of the filters depends on the resolution of the analog-to-
digital
converter and the processing with 40 to 60 decibels been sufficient for common
applications.
The passband ripple is another important filter consideration. In the past,
the 1 D-
EMI industry has mostly used Butterworth (also known as maximally-flat)
filters. These
are compromise filters with a 3 db passband variation. For typical 1 D-NDI
applications,
better performance is achieved with Chebyshev or Elliptic filters. For
example, a 0.5 db
Chebyshev filter has less passband variation and sharper rolloff, thus
resulting in a lower
order filter than an equivalent Butterworth. The above specifications (filter
type, center
frequency, bandwidth and attenuation) are sufficient to design the filters
without
additional experimentation. Filter design software, some available free of
charge, is also
available from multiple component vendors such as, MicroChip, Linear
Technology, and
many others.
Preferably, the computer 20 may read and gather relevant sensor 4 information
from the sensor 4 onboard memory and may write new information into the sensor
4
onboard memory. It should be understood that the sensor 4 relevant information
may
also be stored in other storage media, such as hard disks, floppy disks,
compact discs,
magnetic tapes, DVDs, memory, and other storage devices that computer 20 may
access. The sensor 4 analog signal 4A is amplified by a programmable gain
amplifier
(herein after referred to as "PGA") 80. This Gain of the PGA 80 is controlled
by the
computer 20. FIG. 8A through 8D illustrate a PGA 80 and its design equations
for clarity.
PGAs are well known in the art and multiple designs can be found throughout
the
literature. PGA integrated circuits are also commercially available from such
vendors as
Analog Devices, Linear Technology, Maxim, National Semiconductors, Texas
Instruments, and many others. In its simplest form a PGA comprises a
differential
amplifier 851 with a variable resistor 83 inserted in its feedback loop.
Preferably, the
variable resistor 83 is a digitally controlled potentiometer such as the ones
offered by
XICOR. Computer 20 may vary the variable resistor 83 value thus adjusting the
gain of
the PGA 80. The PGA 80 gain adjustment is primarily controlled by the sensor 4
31
CA 02632490 2008-05-22
information, the instantaneous scanning speed derived by the computer 20 from
sensor
36 and the specific MUI 1. The output of PGA 80 is connected to a filter bank
in order to
decompose the inspection signals frequency spectrum and extract relevant
features.
The low frequency components are extracted by the low-pass filter 90. It
should
be understood that the term low-frequency features are not in absolute terms
but in
relative terms to the scanning speed. Therefore, the cutoff frequency of the
low-pass
filter 90, denoted as Fc in FIGS. 9B and 9C, may be set to 5 Hz for one
scanning speed
and to 50 Hz for a higher scanning speed. The exact cutoff frequency of the
low-pass
filter 90 depends on the sensor information 4, the instantaneous scanning
speed derived
by the computer 20 from sensor 36, and the specific MUI 1. FIGS. 9A, 9B and 9C
illustrate a programmable 3<sup>rd</sup> order low-pass analog filter and its design
equations
for clarity. Low-pass filters are well known in the art and their design can
be found
throughout the literature. Filter design software, some available free of
charge, is also
available from multiple component vendors such as, MicroChip, Linear
Technology, and
many others. The low-pass filter of FIG. 9A consists of two sections. A
1<sup>st</sup> order
filter comprising of resistor 91 and capacitor 92 cascaded with a 2<sup>nd</sup>
order low-pass
analog filter. It should be understood that all other filter orders can be
obtained by
cascading additional filter sections. Preferably, the variable resistors 91
and 93 are
digitally controlled potentiometers such as the ones offered by XICOR and a
fixed
resistor value (not shown) similar to the FIG. 8A network 83 and 84. Computer
20 may
vary the variable resistor 91 and 93 value thus adjusting the cutoff frequency
of the low-
pass filter.
All other frequency components of the sensor signal 4 are extracted by the
band-
pass filters 100 through 100N. Again, it should be understood that the
frequency bands
are not stated in absolute terms but in relative terms to the scanning speed.
Therefore,
the center frequency of a band-pass filter 100 may be set to 40 Hz for one
scanning
speed and to 200 Hz for a higher scanning speed. The exact center frequency of
the
band-pass filters 100 through 100N depends on the sensor information 4, the
instantaneous scanning speed derived by the computer 20 from sensor 36 and the
specific MUI 1. FIG. 10A illustrates a programmable 3<sup>rd</sup> order band-pass
filter that
is made up from a low-pass filter 90 cascaded with a 3<sup>rd</sup> order high-pass
filter. The
3<sup>rd</sup> order high-pass filter and its design equations are shown for
clarity. High-pass
filters are well known in the art and its design can be found throughout the
literature.
32
CA 02632490 2008-05-22
Filter design software, some available free of charge, is also available from
multiple
component vendors such as, MicroChip, Linear Technology, and many others. The
high-
pass filter of FIG. 10A includes two sections. A 1<sup>st</sup> order filter
comprising of
capacitor 101 and resistor 102 cascaded with a 2<sup>nd</sup> order high-pass
filter. It should
be understood that all other filter orders can be obtained by cascading
additional filter
sections. Preferably, the variable resistors 102 and 104 are digitally
controlled
potentiometers such as the ones offered by XICOR and a fixed resistor value
(not
shown) similar to the FIG. 8A network 83 and 84. Computer 20 may vary the
variable
resistor 102 and 104 value thus adjusting the cutoff frequency of the high-
pass filter. It
should be further understood that this band-pass filter configuration allows
for individual
adjustment of both the leading and trailing transition bands. Other band-pass
filter
configurations can also be found throughout the literature.
Frequency Decomposition in the Digital Domain
The features extraction filter bank that was described above using analog
filters
may also be realized with switched capacitor filters and/or digital filters
and/or
mathematical transforms or any combination thereof. Switched capacitor filters
may be
substituted for the analog filters 90 and 100 through 100N with the computer
20 varying
the clock frequency to program the resulting switched capacitor filter bank.
It should be understood that no modification to the front end of the
inspection
sensor interface 33 (i.e. no preprocessor 32 as described hereinabove) of the
exemplary
RDIS-10 is required in order to implement the present invention using digital
filters
and/or mathematical transforms as the exemplary RDIS-10 is designed for
digital
domain operation.
The sensor signal therefore, is converted to digital format and the analog
filters
described above may be converted to their digital counterpart using bilinear
transform
which is well known to the art and well publicized resulting in Infinite
Impulse Response
digital filters (known to the art as IIR filters) and is illustrated in FIGS.
11 A, 11 B and 11 C.
The block diagram of FIG. 7 may then be used to derive the flowchart of the
digital signal
processing form of the present invention. In another implementation, digital
filters may
be designed using direct synthesis techniques that are also well known to the
art and
well publicized. Finite Impulse Response digital filters (known to the art as
FIR filters)
may also be employed at the expense of computing power. FIR implementations,
such
as Kaiser, Hamming, Hanning etc, are also well known to the art and well
publicized.
33
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There are many mathematical transforms that are well known and well
publicized. However, not all are useful for features extraction for the
transient NDI
signals. The NDI industry in the past has proposed the use of Fourier
Transform or its
Fast Fourier Transform (FFT) implementation, a misapplication for the brief
transient NDI
imperfection signals. Fourier Transform, in all of its implementations, is
useful to analyze
long periodic signals (long waves). Furthermore, the Fourier Transform
provides
information in the frequency domain and none in the time domain which is
essential for
the analysis of NDI signals. This drawback of the Fourier Transform was noted
by the
French Academy and in particular by J. L. Lagrange who objected to the Fourier
Transform trigonometric series because it could not represent signals with
comers such
as the ones often encountered in NDI. Subsequently, the Academy refused to
publish
the Fourier paper. In order to overcome the drawbacks of the Fourier
Transform,
alternatives were developed over the years, notably the Short Time Fourier
Transform
(commonly referred to as STFT), wavelets and coiflets all of which are well
known to the
art and well publicized. The main disadvantages of the transforms are their
fixed
resolution and their demand for higher computer power.
The STFT offers uniform time and frequency resolution throughout the entire
time-frequency domain using a fixed window size, which results in its main
drawback. A
small window blind the STFT to low frequencies while a large window blinds the
STFT to
brief signal changes mostly associated with use induced MUI 1 imperfections.
Wavelets (short waves) are better tuned to the needs of NDI. Wavelets vary the
width of the window thus offer better time resolution for the higher
frequencies that are
typically associated with use induced MUI I imperfections. Wavelets are
typically
implemented using filter banks and they are also well known in the art and
well
publicized. FIGS. 12A, 12B and 12C illustrate the implementation of the
discrete wavelet
transform decomposition using filter banks and downsampling.
Sensor Signal Normalization
Referring back to FIG. 7, the bank of PGAs 80A through 80N follows the
frequency decomposition filter bank. The frequency response of the inspection
sensors 4
is typically non-linear. The response of the inspection sensor 4 to the same
MUI 1
feature would then vary depending on the scanning speed and level of
excitation which
is continuously monitored by computer 20. The sensor 4 response to different
scanning
speeds, in the unique setting of the inspection head 2 under varying
excitation 31 levels,
34
CA 02632490 2008-05-22
can be characterized. This is accomplished by scanning MUI 1 samples with test
imperfections at different speeds and different levels of excitation while
recording the
sensor 4 signals. Preferably, these sensor characterization tests would be
repeated
multiple times so that a sufficiently large database for the specific sensor
is obtained.
The characteristics of the particular sensor 4 are then preferably stored in
the memory
onboard the sensor 4. Computer 20 reads the sensor 4 characteristics and
adjusts the
bank of PGAs 80A through 80N to normalize the sensor signal. This band signal
amplitude compensation along with the capability of computer 20 to adjust both
the
pass-band width and the transition slopes of the filters allows computer 20 to
fully
normalize the imperfection signals.
The outputs of the bank of PGAs 80A through 80N are then converted to digital
form through an analog-to-digital converter of sufficient resolution,
typically 10 to 14 bits,
and speed which is defined by the number of channels and maximum scanning
speed.
AutoFFS Processing
AutoFFS processing operates upon the flaw spectrum that was derived from
signals, such as, but not limited to, echo, reluctance, resistance, impedance,
absorption,
attenuation, sound or physical parameters acquired through one-dimensional or
multi-
dimensional sensors. The processing of Eddy-Current amplitude and phase, for
example, may also be utilized to derive the flaw spectrum as well as frequency
decomposition as described herein above. Regardless of the signal origin or
the
frequency decomposition method used, the frequency components of the signals
then
become the flaw spectrum for use by the AutoFFS in a manner illustrated by
element
21A in FIG. 13. It should be understood that computer 20 can manipulate and
present
the signals in any desirable format. It should be further understood that the
signals of
geometrically offset sensors, such as the ones shown in FIG. 7 of U.S. Pat.
No.
2,881,386, are aligned by computer 20 through time shifting (time delay)
primarily
controlled by the scanning speed preferably derived from sensor 36. This may
comprise
memory, a bucket-brigade, or any combination of the above. Variable length
analog
delay lines may also be deployed, the delay length controlled primarily by the
scanning
speed. It should be understood that sensor 36 may comprise a number of sensors
distributed along the length of MUI 1 for direct measurement or coupled to MUI
1
transport components, such as the lifting cable, or a combination thereof.
CA 02632490 2008-05-22
It should be understood that the exemplary RDIS-10 extraction matrix is
compiled
through a software program, that was published in 1994 and it is beyond the
scope of
this patent, and decomposes the converted digital signals into relevant
features. The
extraction matrix may be adjusted to decompose the signals into as few as two
(2)
features, such as, but not limited to, the 1 D-NDI presentation of wall and
flaw. It should
be understood that no theoretical decomposition upper limit exists, however,
fifty (50) to
two hundred (200) features are practical. The selection of the identifier
equations, further
described herein below, typically sets the number of features. In the
exemplary RDIS-10,
the decomposed signals, regardless of their origin, are known as the flaw
spectrum 6
(see FIG. 2C).
Feature Recognition
Humans are highly adept in recognizing patterns, such as facial features or
the
flaw spectrum 6 and readily correlating any pertinent information. Therefore,
it is easy for
the inspector to draw conclusions about the MUI I by examining the flaw
spectrum 6, as
further illustrated in FIGS. 15A through 15E. During the inspection, the
inspector further
incorporates his/her knowledge about the MUI 1 present status, his/her
observations, as
well as the results of previous inspections. The success of this inspection
strategy of
course, solely depends on how well the inspector understands the flaw spectrum
6 data
and the nuances it may encompass.
Computers can run numerical calculations rapidly but have no inherent pattern
recognition or correlation abilities. Thus, a program has been developed that
preferably
derives at least one mathematical procedure to enable the computer 20 to
automatically
recognize the patterns and nuances encompassed in decomposed inspection and/or
sound data streams such as presented in the flaw spectrum 6. The detailed
mathematical procedures are described hereinbelow and enable one skilled in
the art to
implement the AutoFFS described herein without undue experimentation.
FIG. 13 illustrates a block diagram of an AutoFFS data processing sequence
that
allows the creation of a software flowchart and the translation of the
practice to a
computer program. For stand-alone operation, the AutoFFS must be optimal in
regard to
the FFS assessment criteria and application limitations, commonly defined by
approximations and probabilities which are referred to herein as constraints.
It should be
understood therefore, that the AutoFFS state variables must be tuned for
optimal
performance under different constrains depending on the MUA 9 and its
application. The
36
CA 02632490 2008-05-22
fundamental operation of the AutoFFS is performed by the identifier equations
which
preferably capture the optimal mutual features in accordance to the
constraints. It should
be understood that a number of identifier equations may be paralleled and/or
cascaded,
each one utilizing a different set of optimal mutual features. Furthermore, it
should be
understood that the processing of the identifier equations may be carried out
by a single
computer 20 or by different computers in a cluster without effecting the
overall result.
The first stage identifier equations, with elements denoted as a<sub>jk</sub> 112,
114,
use for input N features 111 mostly derived from the flaw spectrum 21A.
Additional
features may be provided by fixed values referred to herein as bias 113, 123,
133. Bias
may be a single constant or a sequence of constants that may be controlled,
but not
limited, by time or by the MUA 9 length. Backwards chaining 119 limits
irrelevant
processing and enhances stability while forward chaining 139 propagates
features to
later stages or it may inform computer 20 that an MUA 9 condition has been
determined
and no further analysis is required. It should be further understood that both
forward and
backward chaining may be direct, through memory, through a bucket-brigade, or
any
combination of the above. It should be further understood that all or any
subsystem of
the AutoFFS may be implemented as a casual system or as a non-casual system.
In a
casual implementation only past and present features 111 are utilized. In a
non-casual
implementation, features 111 are utilized through memory, through a bucket-
brigade, or
any combination of the above thus aflowing for the use of future values of the
features
111. Future values of the features 111 may be used directly or indirectly as
signal masks
and may be propagated through the forward chaining 139. Utilization of future
values of
features 111 increases the AutoFFS stability and reduces the probability of a
conflict In
Equations 1-3, shown below, such features are denoted as Xa. Based on the
constrains,
the identifier equations reduce the features 111 and bias 113 to identifiers
115, 116
denoted as Ya of the form: Y× times. a ij = M× k = 1 N× a ik
× X
× times. a kj ( Equation times. times. 1 )
The identifiers Ya 115, 116 can be fed back through the backwards chaining
119,
can be used directly through the forward chaining 139, can be used as
variables to
equations or as features 121, 131 in following stages or in their most
practical form, as
indexes to tables (arrays) which is shown in Equation 2 for clarity. Y×
times. a ij =
M.function. [ 1 + e - k = 1 N× a ik times. Xa kj )- 1( Equation times.
times. 2)
where T is a Look-up Table or Array.
37
. .. .. . .
CA 02632490 2008-05-22
Another useful identifier form is shown in Equation 3. Y× times. a ij =
T ( M
× k = 1 N× a ik times. Xa kj )( Equation times. times. 3 ) where M
is a
scaling constant or function.
It should be understood that each stage may comprise multiple identifier
equations utilizing equations 1, 2, or 3. There is no theoretical upper limit
for the number
of identifiers calculated, however, five (5) to ten (10) identifiers are
practical.
Some of the identifiers Ya 115, 116 may be sufficient to define the
disposition of
the MUI alone and thus propagate to the output stage 139 while others may
become
features for the second stage 120 of identifier equations along with features
121
pertinent to the Ya identifiers, all denoted as Xb. It should be appreciated
that in the
exemplary STYLWAN RDIS-10, depending on the constrains, those features can be
obtained from the operator interface, from the computer 20 memory, from the
camera
29, or by connecting directly to the STYLWAN RDIS-10 Data Acquisition System
transmitters that measure various parameters illustrated FIG. 41 (21C).
Examples of
such transmitters include the OCI-5000 series manufactured by OLYMPIC
CONTROLS,
Inc, Stafford, Tex., USA, such as transmitters that measure pressure (OCI-5200
series),
temperature (OCI-5300 series), speed and position (OCI-5400 series), weight
(OCI-
5200H series), fluid level (OCI-5200L series), flow (OCI-5600 series),
dimensions (OCI-
5400D series), AC parameters (OCI-5400 series), DC parameters (OCI-5800
series), as
well as other desired parameters. The second stage 120 identifier equations,
with
elements denoted as b<sub>lm</sub>, produces identifiers 125,126 denoted as Yb of
similar
form as the Ya identifiers 115, 116.
Again, some of the identifiers Yb may be sufficient to define the disposition
of the
MUI 1 alone and thus propagate to the output stage 139 while others may become
features for the third stage 130 identifier equations along with features
pertinent to the
Yb identifiers, all denoted as Xc. For the RDIS-10, depending on the
constrains, those
features can be obtained from data or functions entered by the operator 138,
stored in
historical data 137, or other predetermined sources (not illustrated). It
should be
understood that this process may repeat until an acceptable solution to the
constrains is
obtained, however, three stages are typically adequate for the exemplary
STYLWAN
RDIS-10. It should further be understood that each stage 110, 120 and 130 may
comprise multiple internal stages.
Determination of Coefficients
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CA 02632490 2008-05-22
For the determination of the a<sub>ik</sub> coefficients, the tuning of the
identifier
equations, a set of flaw spectrums 6 of known similar imperfections that are
pertinent to
a current inspection application are required. These data sets, of flaw
spectrums 6, are
referred to herein as baseline spectrums. Preferably, all the a<sub>ik</sub>
coefficients are
initially set equal. It should be understood that because this is an iterative
process the
initial values of the a<sub>ik</sub> coefficients could also be set by a random
number
generator, by an educated guess, or by other means for value setting.
Since the baseline spectrums are well known, typically comprising data taken
for
similar imperfections, the performance measure and the constrains are clearly
evident
and the coefficients solution is therefore objective, although the selection
of the
imperfections may be subjective. By altering the coefficient values through an
iterative
process while monitoring the output error an acceptable solution would be
obtained.
There are multiple well-known techniques to minimize the error and most of
these techniques are well adept for computer use. It should be appreciated
that for the
AutoFFS limited number of features a trial-and-error brute force solution is
feasible with
the available computer power. It should be further expected that different
solutions would
be obtained for every starting set of coefficients. Each solution is then
evaluated across
a variety of validation spectrum as each solution has its own unique
characteristics. It is
imperative, therefore, that an extensive library of both baseline spectrums
and validation
spectrums must be available for this evaluation. It should be further
understood that the
baseline spectrums cannot be used as validation spectrums and visa versa.
Furthermore, it should be understood that more than one solution may be
retained and
used for redundancy, conflict resolution, and system stability. Still further
in applications
of the AutoFFS, the terms "acceptable" or "good enough" are terms of art to
indicate
that, in a computational manner, the computer has completed an adequate number
of
iterations to compile an answer/solution with a high probability of accuracy.
Once a set or sets of coefficients are obtained, the number of non-zero
coefficients is preferably minimized in order to improve computational
efficiency. This is
important because each identifier equation is just a subsystem and even minor
inefficiencies at the subsystem level could significantly affect the overall
system real time
performance. Multiple techniques can be used to minimize the number of non-
zero
coefficients. A hard threshold would set all coefficients below a
predetermined set point
to zero (0). Computers typically have a calculation quota, so a quota
threshold would set
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CA 02632490 2008-05-22
to zero a sufficient number of lower value coefficients to meet the
calculation quota. A
soft threshold would subtract a non-zero constant from all coefficients and
replace the
negative values with zero (0). Since an error measure exists, the new set of
coefficients
can be evaluated, the identifier equations can be tuned again and the process
could
repeat until the admissible identifier equation is determined. It is preferred
that multiple
admissible identifier equations are determined for further use. It should be
appreciated
that although the preference for multiple admissible identifiers may appear to
complicate
potential resolutions, the use of computer power makes a large number of
iterations
feasible.
For the assessment of materials, an acceptable solution would always contain
statistics based on false-positive and false-negative ratios. A false-positive
classification
rejects fit material while a false-negative classification accepts unfit
material. Using more
than one identifier equation lowers the false ratios more than the fine-tuning
of a single
identifier equation. It should be understood that this process theoretically
provides an
infinite number of solutions, as an exact formulation of the inspection
problem is elusive
and always based on constrains. Furthermore, for a solution that can be
obtained with a
set of coefficients, yet another solution that meets the performance measure
may also
be obtained by slightly adjusting some of the coefficients. However, within
the first three
to five proper iterations the useful solutions become obvious and gains from
additional
iterations are mostly insignificant and hard to justify.
Once all of the Stage-I 10 admissible identifier equations have been
determined,
their identifiers become features in Stage-II 120 along with the additional
features 121,
bias 123, and forward and backwards chaining 129. The starting set of baseline
spectrums is then processed through the admissible identifier equations and
the results
are used to tune the Stage-II 120 identifier equations in a substantially
identical process
as the one described above for the Stage-I 110. The process repeats for the
Stage-III
130 identifier equations and any other stages (not illustrated) that may be
desired or
necessary until all the admissible subsystems are determined and the overall
system
design is completed. It should be appreciated that in practice, preferably
only two to five
stages will be necessary to obtain required results. When the final
coefficients for all of
the equations are established, the overall system performance may be improved
by
further simplifying the equations using standard mathematical techniques.
CA 02632490 2008-05-22
A previous assessment with the same equipment provides the best historical
data 137. The previous FFS assessment, denoted as Ys<sub></sub>(-1), is ideally
suited for
use as a feature 131 in the current inspection as it was derived from
substantially the
same constrains. Furthermore, more than one previous FFS assessment 137 may be
utilized. Features 131 may be backwards chained 129, 119. Multiple historical
values
may allow for predictions of the future state of the material and/or the
establishment of a
service and maintenance plan.
Determination of Bias
In conventional inspection systems, previous state data, that was derived
through
a different means under different constrains, could not necessarily be used
directly or
used at all. If utilized, the data would more likely have to be translated to
fit the
constrains of the current application. It should be appreciated that such a
task may be
very tedious and provide comparatively little payoff. For example, there is no
known
process to translate an X-Ray film into MFL pertinent data. However, the
AutoFFS
system described herein allows for the use of such data in a simple and direct
form. In
the X-Ray example, the opinion of an X-Ray specialist may be solicited
regarding the
previous state of the material. The specialist may grade the previous state of
the
material in the range of one (1) to ten (10), with one (1) meaning undamaged
new
material. The X-Ray specialist opinion is an example of bias 113, 123, 133.
Bias 113, 123, 133 may not necessarily be derived in its entirety from the
same
source nor be fixed throughout the length of the material. For example,
information from
X-Rays may be used to establish the previous material status for the first
2,000 feet of
an 11,000 foot coiled tubing string. Running-feet may be used to establish the
previous
material status for the remainder of the string except the 6,000 foot to 8,000
foot range
where OD corrosion has been observed by the inspector 138. From the available
information, the previous material status for this string (bias per 1,000
feet') may look like
[2, 2, 4, 4, 4, 4, 7, 7, 4, 4, 4] based on length. Other constrains though may
impose a
hard threshold to reduce the bias into a single value, namely [7], for the
entire string.
An example of a bias array would be a marine drilling riser string where each
riser joint is assigned a bias based on its age, historical use, Kips, vortex
induced
vibration, operation in loop currents, visual inspection, and the like. The
bias for a single
riser joint may then look like [1, 1, 3, 1, 2, 2]. Identifier equations may
also be used to
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CA 02632490 2008-05-22
reduce the bias array into a bias value or a threshold may reduce the bias
into a single
value.
Fitness for Service Assessment
AutoFFS provides means to move the FFS process from the laboratory or the
engineering department to the field and apply FFS to the in-service material
using actual
as-is field data. Furthermore, it should be understood that AutoFFS may be
utilized to
gather actual filed data to create FFS methods, charts, tables and formulas or
to verify
the validity of proposed or existing FFS methods, charts, tables and formulas.
AutoFFS
may utilize industry standard or custom FFS methods, charts, tables and
formulas,
utilize original design data and criteria, material test reports, deployment
history, prior
inspection records, prior FFS records, repair and/or alteration records along
with FFS
assessment techniques and/or formulas and/or data sets, imperfection allowance
rules
and/or formulas and/or data sets, acceptance criteria, remediation options
and/or
formulas and/or data sets. AutoFFS makes provisions to accept such
information/data
either as a mathematical or logical (crisp or fuzzy) formula, as a sequence of
data, such
as bias, or even as a single constant.
Typically and in addition to FDDim, AutoFFS would evaluate material utilizing:
a)
absolute values, such as actual wall thickness; b) parameter ratios or
remaining ratios,
such as (strength of damaged material)/(strength of undamaged material); c)
coverage
ratios, such as (pitted area surface)/(material surface) and d) rates of
change, such as
feature morphology, size, density, coverage and any combination thereof.
Preferably,
AutoFFS would also utilize a measure of the damage mechanism time-dependency.
AutoFFS would apply FFS assessment for each feature and/or damage mechanism
and
then fuse the results of each assessment to determine the status of the
material. It
should be understood that the combination of FDDim with the other AutoFFS
measured/calculated values would result in a multidimensional pointer
sufficient to select
the material status from a multidimensional group of tables or charts or to
solve a system
of equations. For example, remaining wall thickness FFS tables and charts may
be
indexed on the (maximum) operating pressure and temperature. By continuously
monitoring the actual operating pressure and temperature, AutoFFS would then
select
the appropriate FFS assessment path and alert the operator when operating
pressure
and temperature exceed a limit. In a different embodiment, AutoFFS could
establish
communication with a pressure and temperature monitor using communication port
26
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CA 02632490 2008-05-22
and download the pressure and temperature historical data from the monitor
memory.
Such data may also be available in a storage device 23.
AutoFFS may also utilize the damage mechanism time-dependency for
prognosis or prediction of the remaining useful life. Since AutoFFS would
preferably be
monitoring other controlling parameters, such as pressure, temperature,
deflection etc, it
should be understood that AutoFFS prognosis and/or predictions would be based
on
measured parameters instead of estimated parameters. It should be further
understood
that even small changes in the application and/or environment might result in
significant
FFS changes. Therefore, any AutoFFS prognosis or prediction would be bound by
the
monitored stability of the controlling parameters.
AutoFFS preferably may a) scan the MUA 9 after each use; b) identify the
features of MUA 9; c) quantify the features of MUA 9; d) assess the impact of
the
features upon the MUA 9, e) determine the FFS of MUA 9 under the constraints
of the
application and f) (optional) generate and export a file for use by an FEA
engine. It
should also be apparent that AutoFFS deployment and utilization should be
economically sound.
FIG. 14 illustrates a typical FFS flaw chart. As mentioned earlier, AutoFFS
assessment is based primarily on as-built or as-is data 110, 120 and 130. The
first
AutoFFS step is to separate design features and imperfections 140. When design
data is
available, AutoFFS also monitors compliance with the design data 142.
Typically, once
each imperfection has been identified, its severity 141A may be established by
applying
stress concentration correction factors and neighborhood information
correction factors.
The imperfection identification may also be utilized to establish the MUA 9
degradation
mechanism 141 B. An FFS for the feature is then calculated 141 C.
For each feature, including imperfections, the acceptance/rejection criteria
are
then applied 142. When the degradation mechanism is known, preventive action
146
may reduce/prevent further MUA 9 deterioration, such as relocating the OCTG in
a
string, repairing damaged protective coating or using corrosion inhibitors.
Conversely,
comparison with previous FFS records 137 may measure the effectiveness of any
prior
preventive action. Occasionally, re-rating 148 the MUA 9 early on may result
in an
extended useful life in service 151.
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When MUA 9 does not meet the minimum acceptable criteria for the application
and it cannot be repaired 144, the MUA 9 may be re-rated and used in a
different
application 151. However, repeat AutoFFS scans should minimize the number of
unanticipated MUA 9 rejections. MUA 9 deterioration should be tracked and
preventive
action 146 and 148 should maximize the MUA 9 useful life.
FIG. 15 illustrates a typical FFS time sequence of a coiled tubing work coil.
The
baseline 155A shows the flaw spectrum of a new coil. Since C02 is predominant
in the
work area, it is anticipated that future FFS scans would detect C02 type
corrosion (2-d).
Preferably AutoFFS would include imperfection growth paths, morphology
migration
evaluation paths and root-cause identification. For example, the depth of a
corrosion pit
may increase and/or the corrosion pitting density may increase and/or a crack
forming at
the bottom of the pit would result in a critically flawed area (herein after
referred to as
"CFA"). C02 type corrosion pitting appears in scan 155B exactly as expected
and it is
predominant by scan 155C. Scans 155B and 155C show features morphology
migration.
Because the work coil is undergoing bending in the plastic region (plastic
deformation),
the pits act as stress concentrators increasing the cyclic fatigue built-up
rapidly. The
morphology shift toward fatigue cracking (2-D) is shown in 155D along with
significant
growth. The work coil shown in 155D is no longer fit for service due to the
imperfection
severity (2-d). The only feasible remediation option is to remove the coil
from service
work and re-rate it 148 as a production string where the coil will no longer
be subjected
to plastic deformation cycles. However, since the coil is under continuous in-
service
monitoring, the coil was subjected to a few extra cycles, shown in 155E, when
cracks (2-
D) appeared, probably at the bottom of the C02 corrosion pits (2-d). Cracks, a
late
fatigue life manifestation shown in 155E, grow rapidly and the coil would
break within the
next 3 cycles.
AutoFFS would preferably utilize a number of FFS paths, some dedicated into
prognosis. For example, when computer 20 monitors, logs and evaluates the
overall
drilling performance, the FFS paths may be selected and its impact on the MUA
9. The
impact of the drilling may be established 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 dictate that a different FFS path and/or
constraints should be
utilized to further evaluate the MUA 9 FFS including imperfections 140.
Furthermore,
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CA 02632490 2008-05-22
changes in the strata and/or in the efficiency of the drilling process may
indicate
conditions that primarily induce imperfection morphology migration, not just
growth, thus
AutoFFS should also include the anticipated deterioration mechanism acting on
the
imperfections.
Feature Duration
As mentioned earlier, it should be understood that the one to one
correspondence of simple imperfections to the STYLWAN Flaw Spectrum
occasionally
applies to machined (man-made) imperfections and not to the complex form
imperfections typically found in nature. Therefore, the STYLWAN Flaw Spectrum
elements must be viewed as an entity identification signature, just like DNA,
but not as a
detailed chemical analysis. It would be erroneous for example to conclude that
a weld is
made up form a pit, three gouges and a wall thickness increase, the result of
a chemical-
analysis-like interpretation of the Flaw Spectrum data. The correct Flaw
Spectrum
interpretation would recognize the signature of a weld and therefore, the
first AutoFFS
task would be to recognize complex imperfections, such as welds.
It should be readily apparent that complex imperfections would have
significant
3D dimensions, as opposed to a single crack for example, and therefore their
Flaw
Spectrum would have a much longer time and/or length duration. If the AutoFFS
was
allowed to interpret signals instantaneously, the AutoFFS would behave
erroneously, in
a chemical-analysis-like fashion, where a weld would be reported as a string
of pits,
gouges, CFAS and wall thickness changes. For example, the feature shown in
155C is a
corrosion band, not a large number of corrosion pits, and the root-cause of
the corrosion
band is identified as C02. Therefore, preventive action 146 should focus at
minimizing
the impact of the C02 environment on the work coil. Similarly, 155E shows
multiple
CFAs and borderline CFAs, not just pits and cracks. Therefore, interpreting
155C
through 155E instantaneously may lead to erroneous conclusions and possible
instability.
It is desirable therefore, that AutoFFS processing preferably incorporates
feature
duration data and/or trigger along with the ability to revisit prior data
and/or decisions. It
should be noted that any time delay between the feature passing through the
head 2 and
an AutoFFS decision would be insignificant and unnoticeable by the operator.
Furthermore, it should be noted that feature duration refers to sufficient
duration that
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CA 02632490 2008-05-22
would lead to a valid AutoFFS conclusion and not necessarily for the duration
of the
entire feature.
For example, a coiled tubing taper (a wall thickness change) may be many
thousands of feet long while a localized wall loss could be six inches long.
On the onset
of such a feature, it would be advisable to examine a greater MUA 9 length,
ten feet for
example, before the AutoFFS makes a decision. At 180'/minute scanning speed,
ten feet
delay would amount to about one third (1/3) of a second that would certainly
go
unnoticeable by the operator. Furthermore, even the AutoFFS shortest
utterance, like
"taper" or "weld", would take longer than one third (1/3) of a second.
Complex Features
Again, complex features may be included in the MUA 9 by design, such as
tapers, collars and welds, and therefore may be shown in the historical data
records
and/or may be anticipated; may reflect repairs and/or alterations that are not
shown in
the historical data records and may or may not be anticipated, such as a
repair weld and
lastly, they may reflect imperfections that were not encountered on previous
AutoFFS
scans. Once the complex features is recognized 140, AutoFFS processing would
then
proceed with the evaluation tasks prescribed for the particular complex
features and its
ramifications upon the AutoFFS processing.
As discussed earlier, AutoFFS may retain more than one identifier for
redundancy, conflict resolution, and system stability. It should then be
understood that
the recognition of complex features may involve more than one identifier.
Furthermore,
complex features are the most likely cause of AutoFFS instability and as a
precaution
therefore, AutoFFS, once it reaches a decision, may re-examine the same
features
under longer duration. This re-examination diminishes the probability of
instability and
increases the AutoFFS certainty, especially if different identifiers are
implemented for the
re-examination.
Assessment of Welds
Welding is the joining of two material pieces by applying heat with or without
the
use of filler material. Rarely used cold welding is accomplished by applying
high
pressure. Welding induces residual stresses that FFS and FEA typically assume
to be
uniform throughout the material thickness (uniform stain field). During
multipass welding
for example, the same point undergoes multiple thermal cycles multiple times
and
46
CA 02632490 2008-05-22
secondly, not all points undergo the same number of thermal cycles. Therefore,
it would
be erroneous to assume that the weld residual stresses are uniform throughout
the
material thickness. The heat-affected zone (herein after referred to as "HAZ")
is the
portion of the base material that did not melt during welding, but the welding
heat altered
its properties.
Welds are complex features that are very common, just like couplings. Often,
material with welds is derated, such as coiled tubing with a butt weld. In
addition, a
different derating factor is used for factory butt welds and field butt welds.
AutoFFS
cannot make that distinction automatically. However, AutoFFS may search the
local or
remote history and/or alteration record and/or may inquire for an entry from
the operator
138 and/or an expert. In the absence of additional information, preferably
AutoFFS
would evaluate the weld as a complex feature, feature rating, and rate the
material
pessimistically, statutory rating. Preferably, AutoFFS would retain and report
both
ratings.
Fatigue Assessment
For centuries, practicing engineers recognized that subjecting metal to stress
cycles resulted in fractures although the forces involved were a fraction of
the forces
required for static failure. The term Fatigue was introduced in the 19<sup>th</sup>
century
probably by J. V. Poncelet (1788-1867). Fatigue initiates at the crystal
imperfections,
commonly known as dislocations. Dislocations can be viewed as atomic level
microcracks that act as stress concentrators starting the slip mechanism.
Fatigue is
cumulative and with additional stress cycles, fatigue progresses to cracking
as the
microcracks grow and bridge, a point where failure is rapid.
Even the most sophisticated prediction models lack most of the detailed
information required for a valid prediction. For example, OCTG may contain
10<sup>10</sup>
dislocations/in<sup>3</sup> on the average and while deployed may be subjected to
unanticipated significant loads. Even if all the loads and the exact nature of
each
dislocation were precisely known, any type of calculation, such as FEA, would
be
prohibitive. Furthermore, the problem of fatigue cracks rapidly magnifies when
the
material is subjected to cyclic loading in corrosive environments.
The advantage of AutoFFS is the large number of repeated assessments and
data that can be collected without interfering with the deployment of the MUA
9 or the
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CA 02632490 2008-05-22
production rate. AutoFFS detects the actual condition of the MUA 9 fatigue
regardless of
the underline causes. Fatigue build-up tests with the exemplary RDIS-10
revealed that
fatigue up to 50% of the life cycle falls in the 2-D.alpha. spectrum segment,
between
.apprxeq.50% and .apprxeq.75 lo falls in the 2-D.beta. spectrum segment and
above
.apprxeq.75% falls in the 2-D.gamma. spectrum segment.
Most software failure prediction models are aimed at predicting the alpha
failure
location (herein after referred to as "(.alpha.FL)"); the location where the
rate of fatigue
build-up is the highest and therefore, it is the location where the first
failure is expected
to occur. RDIS-10 fatigue build-up tests revealed that multiple (aFL) can be
identified at
the boundary transition between 2-D.alpha. and 2-D.beta. while the failure
location can
be identified at about 65% of the life cycle when preventive action 143
becomes
extremely important.
The most catastrophic form of failure is Early alpha failure (herein after
referred
to as "(E.alpha.FL)") that is not predicted by any model but AutoFFS would
easily detect
142 the rapid fatigue build-up. An (EaFL) most likely would be the result of
MUI 1 that
does not meet the specifications or material that was damaged during
transportation and
handling following the inspection.
Crack-Like Imperfection Assessment
In-service fatigue build-up typically initiates surface cracking. Cracks also
initiate
at the bottom of other imperfections, such as pits, that act as stress
concentrator as
shown in 155E. Modeling and predicting crack growth is extremely imprecise,
just like
modeling fatigue. Again, AutoFFS scans, preferably after every use, would
track the
actual crack growth and propagation= regardless of the underline causes. A
measure of
the energy released per crack surface area may be calculated from the AutoFFS
data.
Without additional loads and when crack growth reaches its limit, AutoFFS may
calculate
the residual stresses that contributed to the crack growth. Such data may
supplement
the historical data of all materials deployed in similar applications.
Preferably, such
database would reside in a central remote location in communication with
AutoFFS.
Significant remaining useful life of the MUA 9 may be recovered if the crack
7A in FIG.
3A is morphed 145 into a 3-D type imperfection 7B (much lower stress
concentration) as
shown in FIG. 3B, but only if the neighborhood of crack 7A is free from other
imperfections. Therefore, effective preventive action 146 is essential.
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CA 02632490 2008-05-22
Crack growth and propagation is highly sensitive to changes in the application
or
the environment. As carried out, FFS assessment typically utilizes theoretical
data
and/or experimental data that were obtained in a laboratory under carefully
controlled
conditions. Such data are not always appropriate for field use. AutoFFS data
on the
other hand, reflect actual field conditions and material performance and
therefore
capture the actual material FFS for the particular application and/or
environment.
Pitting Assessment
For isolated pits, 2-d through 3-d assessment would examine the proximity of
other imperfections to the pit that may form a CFA under the regiment of
anticipated
loads as shown in 155E. Once the material is determined to be free of CFAs,
discussed
further below, AutoFFS would then establish severity of the pit.
For corrosion bands, 2-d through 3-d assessment would first establish the
boundaries of the corrosion region (imperfection duration and area coverage).
Then
AutoFFS would determine if the corrosion region damage is still acceptable 142
and that
the region is not growing at an unacceptable rate by utilizing previous FFS
records 137,
such as 155C and 155D. AutoFFS would then attempt to identify the nature of
the
corrosion mechanism. Different mechanisms result in different types of
corrosion pitting
such as narrow base cylindrical pits all the way to broad based conical pits
and FDDim
may be used as a corrosion mechanism guide and thus a guide to the root-cause
identification and the proper remediation 145, 146.
For example, when C02 type pits appear on MUI 1 that was free of C02 pits in
previous AutoFFS scans, it is reasonable to conclude that C02 backflooding has
reached the particular well-site. This change in the operating environment
significantly
impacts the remaining MUA 9 life which can be recalculated and extended by the
proper
application of inhibitors or by simply rearranging the tubing in a well.
Furthermore, early
detection of the C02 presence may redefine the next preventive maintenance
service
intervai. This unique and novel feature of the AutoFFS is not available with
the sporadic
inspections which more likely would take place after the MUA 9 failed
prematurely
because of the accelerated C02 corrosion.
This example also demonstrates another AutoFFS strength versus FFS and 1 D-
NDI as carried out. Lets assume that the production tubing was in a well for 4
years prior
to C02 reaching the well site and that a tubing failure occurs 1 year after
C02 reached
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CA 02632490 2008-05-22
the well site. FFS assessment and 1 D-NDI would then reasonably conclude that
the
tubing time to failure in the particular well is 5 years (tube useful life),
when in fact it is
only 1 year. Due to costs involved, it is unlikely that 1 D-NDI would be
deployed during a
workover and even if 1 D-NDI is deployed, 1 D-NDI could not detect the change
in the
environment. By the time the owner figures out the new oilfield realities,
following
multiple tubing failures, a vast number of production tubing strings may need
replacing
while an AutoFFS assessment would alert the owner about the subterranean
environment changes and recommend a preventive action 143 early on, thus
extending
the life of multiple production strings. It should then be understood that
AutoFFS
frequent utilization, preferably on every workover, could have significant
ramifications for
the entire operation, not just the particular well.
Critically Flawed Area Assessment
CFA is a complex encounter where imperfections in proximity are dynamically
linked under loading, such as a corrosion pit with a crack at the bottom
(similar to the
CFA of FIG. 3C) or imperfections in proximity and orientation as to experience
increased
stress concentration. The detection of such a CFA early on may not necessarily
mean
rejection of MUA 9 as simple precautions 146, such as minimizing the cycling
of the
particular MUA 9 location, may be sufficient and it may extend the use of the
MUA 9. In
addition, with a AutoFFS continuously monitoring the CFA, the full useful life
of the MUA
9 may be used despite the presents of the CFA as long as the CFA growth and/or
morphology migration remain within acceptable limits as shown in 155D and
155E.
It should also be noted that the AutoFFS processing is diametrically opposing
the
1 D-NDI processing whereby a single uncorrected signal is used to pass or send
the
material for verification. Since the uncorrected signal of a small crack at
the bottom of a
pit does not significantly alter the pit signal, 1 D-NDI would pass the
material with the
CFA as long as the pit signal itself does exceed the preset magnitude limit.
It is also
important to observe that corrosion pits occur at the surface of materials and
in materials
that endure dynamic loading, such as coiled tubing, drill pipe and marine
drilling risers,
the pits, the welds and other imperfections act as stress concentrators.
Cracking would
then initiate at the stress concentrators, like the bottom of the pits or the
heat affected
zone of welds, but such CFAs would go unnoticed by the TOFD of U.S. Pat. No.
6,904,818 because the CFAs would fall within the TOFD near-surface and far-
surface
detection dead-zones.
CA 02632490 2008-05-22
3-d and 3-D Assessment
Imperfections like grooves and gauges along with material hardness changes
typically fall into this segment of the flaw spectrum. Grooves typically arise
from erosion
or corrosion while gauges are mostly the result of mechanical damage. Dents
and
deformations, discussed further below, often include gauges, scratches and
notches. 2-
D and 2-d remediation action, as shown in FIG. 3B, also results in
imperfections that
typically fall into this segment.
When an excavator accidentally hits a pipeline, it will dent it, thus it would
plastically change the pipeline material. Interaction with the environment may
change the
material properties and it may change the plastically deformed dent region at
a different
rate than the undamaged pipeline material. During pumping, the pipeline
pressure varies
at a frequency that may lead to a crack in the deformed area.
Hardness estimates the strength of the material and its resistance to wear.
Hardness changes, such as a hard spot, effect the remaining useful life of the
MUA 9
differently from wall thickness related features. For example, in material
enduring cycles
of tension and compression the vicinity of the hard spot would experience
significantly
increased loading and increased fatigue built-up, a potential (EaFL).
Wall Thickness Assessment
Wall thickness assessment may utilize the wall thickness profile (minimum,
nominal, design, maximum), the wall thickness variation profile, the cross-
sectional area
profile and the average wall thickness profile, preferably all covering one-
hundred
percent (100%) of the MUI 1 continuously.
As mentioned earlier, wall thickness changes, by design or otherwise, may be
used to alter the AutoFFS processing. For example, a pipe coupling would
appear as a
significant wall thickness increase and may be used to invoke the AutoFFS
coupling
inspection.
3-G Deformation Assessment
Irregularities in the MUA 9 geometry, such as ballooning, dents, eccentricity,
neck-down, ovality, misaligned welds and straightness typically fall into this
category.
Deformations may originate in manufacturing, such as eccentricity; may be the
result of
a repair, such as a misaligned weld and lastly deformations may be induced
during
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CA 02632490 2008-05-22
deployment, such as dents, ovality and ballooning. Dents and gouges are
typically the
results of mechanical action, such as an excavator hitting a pipeline. The
fact that
material is not straight, such as a bend drill pipe joint, is an indication
that the material's
yield strength was exceeded during deployment. A bend drill pipe joint would
most like
vibrate, increase the fatigue build-up and increase the wear on both the joint
and any
casing is deployed through.
Coiled tubing endures plastic deformation and it is an example of use induced
deformation. When tubing bends, the fibers at the major axis have to travel
further
(extend) than the fibers on the minor axis (compress). This involves an amount
of stored
energy. In order to minimize the amount of stored energy, the tube swells
sideways
(neutral axis) and assumes an oval cross-section (ovality). By doing so, it
minimizes the
major axis fiber extension and the minor axis fiber compression. AutoFFS uses
3-G
information directly and/or as a processing selection guide.
Material Deployment Loads
During deployment, materials may experience bending, buckling, compression,
cyclic loading, deflection, deformation, dynamic linking, dynamic loading,
elastic
deformation, eccentric loading, feature propagation, impulse, loading,
misalignment,
moments, offset, oscillation, plastic deformation, propagation, shear, static
loading,
strain, stress, tension, thermal loading, torsion, twisting, vibration, and/or
a combination
thereof.
As it is well known, MUA 9 features behave differently under different loading
and
therefore AutoFFS would have to evaluate the features it encounters under all
the
anticipated types of loading 140 and any combination thereof. For example,
drill pipe in a
dog leg would also be subjected to bending in addition to torsion and loading.
Furthermore, re-rating 148 MUA 9 early on may extend the MUA 9 useful life.
AutoFFS Feasibility
The overall system must be feasible not only from the classification
standpoint
but also from the realization standpoint. In addition to the classification
and minimum
error, the system constrains also include, but are not limited to, cost,
packaging,
portability, reliability, and ease of use; all of which should be addressed in
each step of
the design. The system design preferably must assign initial resources to each
level and
should attempt to minimize or even eliminate resources whose overall
contribution is
52
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negligible. This can be accomplished by converting certain features to bias
and
evaluating the resulting error.
Computer 20 preferably recognizes the feature by comparing the final array of
identifiers 135, 136, 139 with a stored features template database. Once a
feature is
recognized, computer 20 may verify the correctness of the recognition by
further
evaluating intermediate identifiers.
AutoFFS Instability and Conflict Resolution
Occasionally, the feature recognition becomes unstable with the final array of
identifiers toggling between two solutions on each iteration. For example,
during the
inspection of used production tubing, the recognition may bounce back and
forth
between a large crack or a small pit. Resolution of such instability may be
achieved by
varying the feature duration length, utilizing intermediate identifiers, by
utilizing the
previous recognition value, or by always accepting the worst conclusion
(typically
referred to as pessimistic classification). However, AutoFFS instability may
also be the
outcome of improper backwards chaining or even faulty constrains. Slight
increase in the
coefficients of the backwards chained features may produce an output
oscillation thus
rapidly locating the problem feature and/or coefficients.
A conflict arises when the final array of identifiers points into two or more
different
MUA 9 conditions with equal probability. Again, resolution of such conflict
may be
achieved by utilizing intermediate identifiers, by utilizing the previous
recognition value or
by always accepting the worst conclusion. However, a definite solution may be
obtained
by eliminating features that the conclusions have invalidated and by
reprocessing the
signals under the new rules.
The AutoFFS is preferably designed to reason under certainty. However, it
should also be capable of reasoning under uncertainty. For example, during the
assessment of used production tubing of a gas well, rodwear is detected. Since
there
are no sucker rods in the gas well, the conclusion is that this is either used
tubing that
was previously utilized in a well with sucker rod or there is a failure in the
AutoFFS. The
AutoFFS could query the operator 138 about the history of the tubing and
specifically if it
was new or used when initially installed in the well. The answer may be
difficult to obtain,
therefore a 50-50 chance should be accepted. A bias value may then be altered
and the
signal may be reprocessed under the new rules.
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Alternate coefficients may be stored for use when certain failures are
detected.
For example, the wellhead pressure transmitter may fail. Upon detection of the
failure,
the alternate set of coefficients should be loaded for further use. It should
be understood
that even a simple bias may substitute for the failed transmitter.
FFS Calibration Sample
FIG. 16A illustrates a calibration sample with four features for use with
AutoFFS
to evaluate the AutoFFS feature identification capabilities and tune its
parameters for the
specific FFS needs of the particular material/application. Imperfection 156A
is a crack-
like imperfection, 156B is a pit-like imperfection, 156C is a gouge-like
imperfection and
156D is a wall thickness feature. It should be understood that the calibration
sample may
contain multiple features and/or multiple examples of similar features with
varying
geometries. It should further be understood that features may be located on
the OD or
the ID of the material or both the OD and ID.
FIG. 16B illustrates a calibration sample with two coexisting imperfections
for use
with AutoFFS to evaluate the AutoFFS coexisting imperfection separation and
identification capabilities and tune its parameters for the specific FFS needs
of the
particular material/application. Imperfection 157 is a crack-like imperfection
coexisting
with a pit-like imperfection. It should be understood that the calibration
sample may
contain multiple coexisting features and/or multiple examples of similar
coexisting
features with varying geometries. It should further be understood that
coexisting features
may be located on the OD or the ID of the material or both the OD and ID.
Not shown are calibration samples with additional features, such as couplings,
welds, deformation and the like, that may be utilized, as dictated by the
particular
material and/or application. Therefore it would be appreciated that standard
threaded
connections and/or welded sections, and the like, may be used for calibration.
FIG. 16C illustrates a range of 1D-NDI recommended calibration/reference
imperfections. It is of interest to notice the machining precision specified
for the
reference imperfections. As a general rule, the tighter the machining
tolerances for the
reference imperfection, the least likely the imperfection would be encountered
in nature.
Furthermore, MUI with any diameter pit, 1/16" or otherwise, should be rejected
for
further use way before the pit becomes a hole (100% penetration), regardless
of the
machining tolerances. Again, as shown in FIGS. 2A and 2B, 1 D-NDI would easily
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mislead someone to believe that a 5% notch or a 100% pit (a hole) are
appropriate
calibration/reference standards and the tight machining tolerances add a false
sense of
confidence in 1 D-NDI.
FIG. 16D illustrates yet another situation that ID-NDI would mislead the
inspector. Imperfection 159 consists of a number of imperfections 158. The
highest
signal selector 10 of 1 D-NDI would propagate to the readout 5 the signal of
only one of
the imperfections 159 resulting in an identical inspection trace for
imperfections 158 and
159. Strength of material knowledge (and common sense) teaches that the MUA 9
will
break at 159 when subjected to loads such as bending, torsion, cyclic loading
etc. If
imperfection 158 did not cross the 1 D-NDI threshold level, then 159 will not
cross the
1 D-NDI threshold level either due to the 1 D-NDI signal processing. Even if
imperfections
158 and 159 did cross the 1 D-NDI threshold level, it is unlikely that 159
would be
recognized as a CFA by the verification crew and it is highly unlikely if
imperfections 158
and 159 were located in the ID of MUA 9. On the other hand, AutoFFS would
evaluate
each 159 imperfection on its own and apply neighborhood correction factors,
thus
distinguishing imperfection 159 from 158.
Remediation
As discussed earlier and referring back to FIG. 3, 1 D-NDI will typically miss
imperfection 7B as it will also miss FIG. 16D imperfection 159. Furthermore, 1
D-NDI
recommended remediation for imperfection 7A does not account for the vicinity
of
imperfection 7A. For example, if imperfection 7B was located on the ID below
imperfection 7A, the 1D-NDI remediation action for 7A would instead result in
a
differently defective material that is acceptable by I D-NDI but rejectable by
AutoFFS.
AutoFFs must calculate the optimal remediation profile along with the
remediation feasibility. For example, it will be straight forward for AutoFFS
to calculate
the optimal remediation profile 7B for extemal imperfections 7A or 158 and
such
remediation is feasible. It will be by far more complex to calculate the
optimal
remediation profile for external imperfections 159. AutoFFS will first
calculate the optimal
remediation profile for each one of the imperfections making up 159. AutoFFS
would
then examine the neighborhood for each morphology shifted imperfection making
up
159. This may result in a remediation profile that is no longer optimal and
therefore,
AutoFFS will calculate an optimal remediation profile combining two or more of
the
morphology shifted imperfections making up 159. This iterative process may
continue
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CA 02632490 2008-05-22
until an optimal remediation profile for 159 is calculated or until AutoFFS
decides that no
remediation is feasible. For example, repeat remediation iterations for
imperfection 159
may lead to an optimal remediation profile resulting in a groove around the
circumference of MUA 9. This groove may render MUA 9 unfit for continuing
service.
AutoFFS would then have to calculate an optimal remediation profile for the
groove that
would result in a fit for continuing service MUI or re-rated 148 MUI.
Therefore, AutoFFS
optimal remediation profile calculations will continue until at least two
consecutive unfit
for service calculations have been performed.
NDI and AutoFFS Utilization
FIG. 17 illustrates a typical NDI process. As practiced today, NDI dictates
termination of the material utilization altogether in order to accommodate the
inspection
process, which, is typically carried out by shipping the material to an
inspection facility.
The cost of inspection is therefore increased by the transportation cost and
the material
downtime. In addition, shipping and handling the material, especially after
the inspection
165, may induce damage to the material that could result in an unanticipated
early
catastrophic failure.
During inspection 160, the MUI 1 is examined for indications (flags), such as
"...
regions of abnormal magnetic reluctance (or echo, or phase shift etc)", that
exceed a
preset threshold level. A typical 1 D-NDI equipment "standardization" practice
sets the
threshold level by scanning a "reference standard" as shown in FIG. 16C.
Again,
referring back to FIGS. 2A and 2B, it is easy to see how someone may be
mislead to
believe that "standardization of the 1 D-NDI equipment" would somehow be
equally
accomplished by "referencing" a 1 D-NDI unit on a "through-wall drilled hole",
a 3-d
imperfection with 0% remaining wall thickness, or a "5% OD notch", a 2-D
imperfection
with 95% remaining wall thickness. Therefore, the 1 D-NDI equipment is
"standardized"
to flag imperfections with wall loss anywhere between 5% up to 100% depending
on the
geometry of the imperfection; the 1 D-NDI practice that led to the material
failure
illustrated in FIGS. 2A through 2D. The flagged material is then send for
verification 161.
Material 165 may then contain an assortment of imperfections, some because of
the 1 D-NDI "standardization" practice, like an 75% drilled hole; some because
of missed
imperfections, due to "sensor liftoff or "detection dead-zones", and some
because of
errors and/or omissions either by the inspector or by the verification crew.
Material 165 is
then exposed to potential accidental damage during transportation and handling
to the
56
CA 02632490 2008-05-22
site of use 169. During deployment, the material may endure unexpected loads
or suffer
unexpected damage 167, but the condition of the material 168 will not be
ascertained
again until the next inspection cycle or after a failure.
Because of its implementation and the intrusion NDI imposes, typical
inspections
have been expensive and are thus performed at rare intervals or not performed
at all.
For example, NDI costs of OCTG can be as high as 30% of the material
replacement
cost.
In the rare occasion that an analysis follows the NDI, the inspection results
163
are send for evaluation while the material is shipped to the use site 169. The
evaluation
process 164 may incorporate design and historical data 162 and eventual
approval for
the material use may be granted well after the material has reached the use
site 169.
Because of the evaluation process 163 inherent delay and cost, along with
other
economic pressures, the material 166 is typically put to use immediately upon
arrival at
the use site 169 and the evaluation process is reduced to a search for the
failure
mechanism of the rejected material.
Pipelines on the other hand, are typically inspected by internal inspection
units
commonly known as pipeline pigs or pigs. Following the scan, the inspection
data is sent
for evaluation 163 while the pipeline is put back into service. It is obvious
that areas of
concern cannot be identified until trained inspectors examine the inspection
data, a
process that typically takes weeks if not months. It is not uncommon for a
verification
report to be generated months after the inspection identifying hundreds of
areas of
concern requiring manual verification. Manual verification for pipelines
involves crews
with heavy equipment that would travel to the designated areas, dig up the
pipeline and
perform manual inspections to evaluate the nature and extent of the
imperfections that
gave rise to the pig signals. The verification results would then be sent for
evaluation
163 and approval 164, months after the pipeline was put back into service
following the
inspection. In the meanwhile, a pipeline leak may develop in one of the areas
designated
for verification or even in an area that was not flagged by the pig. Such
detection failure
may arise from the 1 D-NDI limitations that result in specialized inspection
pigs such a
pitting inspection pigs, crack inspection pigs etc.
On the other hand, AutoFFS must examine and evaluate, as close as possible,
100% of the MUA 9 for 100% of pertinent features and declare the MUA 9 fit for
continuing service only after the impact of all the detected features upon the
MUA 9
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CA 02632490 2008-05-22
have been evaluated; diametrically opposing the I D-NDI methodology. It is
well known
that the presence of any imperfection alters the expected (designed) life of
the MUA 9
and thus impacts its remaining useful life. Thus, it should be appreciated
that the
deployment of the AutoFFS would increase the overall safety and reliability as
it would
lead to MUA 9 repair/replacement prior to a catastrophic failure as well as it
will reduce
and/or eliminate its premature replacement due to concerns when the
conventional
inspection periods are spaced far apart and/or when the conventional
inspection
provides an insignificant inspection coverage.
FIG. 18 illustrates a typical AutoFFS process. Preferably, an AutoFFS baseline
170 is obtained prior to the deployment of the MUA 9. It should be understood
that any
subsequent onsite AutoFFS scans 171 become the baseline, historical data 162,
for the
next scan, therefore, the first baseline may also be obtained during the first
AutoFFS
scan 171 at the deployment site 169. Onsite AutoFFS scans 171 would assure
that
material 168 is still fit for service "as-is" including any transportation
and/or handling
damage 166 or any use-induced damage 167. A remote expert 172 may review the
AutoFFS data, may convert and run the AutoFFS data with finite element
analysis
engine and/or may alter the AutoFFS processing.
FIG. 19 illustrates a typical AutoFFS operator readout 180 configured for
drill
pipe. It should be understood that the AutoFFS operator readout 180 is
provided in
addition to the speech and sound interface. It should be further understood
that this
particular AutoFFS implementation is for illustration purposes only and should
not be
interpreted as limiting in any fashion. This particular AutoFFS operator
readout 180
comprises of the NDI readout 181, the AutoNDl readout 182 and the AutoFFS
readout
183. This particular AutoFFS assigns a fitness number to the MUA 9 between 0
and 100.
Fit for service material is assigned a number between 50 and 100 (green).
Material that
is fit for service under continuous monitoring is assigned a number between 25
and 49
(yellow). Unfit for service material is assigned a number between 0 and 24
(red).
The NDI readout 181 shows a drill pipe joint body wall, a tool joint 184 (a
complex feature) and a second joint with a machined wall loss 185. As
discussed earlier,
if the AutoFFS was allowed to interpret the tool joint 184 signals
instantaneously, the
AutoFFS would behave erroneously, in a chemical-analysis-like fashion, and
will report
that the tool joint is made up of wall thickness increase and a number of
assorted
imperfections. Instead, AutoFFS feature duration processing identified the
tool joint 186,
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altered the processing path and calculated the FFS of the tool joint 188 using
a different
assessment path than the drill pipe body wall path 187. This particular
AutoFFS
assessment declared both the drill pipe body wall 187 and the tool joint 188
fit for service
(green-above mid point). AutoFSS assessed a short section of the machined wall
loss
189 as fit for service under continuous monitoring (yellow--below mid point)
and the rest
of the machined wall loss 190 as unfit for service (red). All of the AutoFFS
data are
available for examination by the operator and the remote expert 172.
Similarly, the
internal memory of an AutoFFS pipeline pig can be examined rapidly in minutes
instead
of weeks or months. The pipeline can be put back to service with confidence or
the
remediation effort can start immediately with the areas that were determined
to be unfit
for service. In addition, FEA can also be utilized to augment and/or verify
the AutoFFS
data as an additional safety measure.
Exporting AutoFFS Data to an FEA Engine
With the advent of desktop computers and design/drafting software, FEA is in
wide use today. It is typically utilized during the design phase to analyze as-
designed
structures. It should be understood that FEA engines operate on physical
structures
(something) under static or dynamic loading, not features alone, as features
alone do not
exist in nature. For example, a corrosion pit does not exist on its own. A
corrosion pit
exists as a feature on a physical structure, such a pipeline. Typically, the
geometry of a
feature is expressed as percentage of the physical structure geometry. For
example, a
10% pit depth is a meaningless expression without knowing the wall thickness
of the
material, the physical structure. Therefore, a 10% pit on a 0.095" wall
thickness coiled
tubing has a depth of 0.0095" and on a 1.000" wall thickness riser auxiliary
line has a
depth of 0.100". AutoFFS (and NDI), typically relay to the operator
information regarding
the severity (presence) of a feature (imperfection, defect) in a format such
as shown in
FIG. 2A, FIG. 2C and FIG. 19.
However, FEA Engines cannot operate on data, such as shown in FIG. 2C and
FIG. 19. FEA Engines can only operate on a structure, such as shown in FIG. 3A
through FIG. 3C, and evaluate the localized stresses of the structure under
specific
loading, as shown in FIG. 3D. It should be noted that 1 D-NDI data are
insufficient for
FEA as 1 D-NDI processing eliminates most of the material features
information, as
discussed earlier.
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On occasion, it is desirable to analyze the as-is material with FEA to obtain,
for
example, deflection, strains, stresses, natural frequencies and similar data.
Converting
manually the AutoFFS signals to a structure requires a number of
multidiscipline experts
and it is time consuming. Therefore, it is desirable to provide a program that
can convert
automatically the AutoFFS material features to a geometrical structure for use
by a
commercially available FEA engine. It should be understood that such
conversion would
depend on the particular AutoFFS capabilities and the particular FEA engine
geometry
file specifications. A more general AutoFFS conversion would translate the
AutoFFS
data to a drawing for use by a commercially available drafting program, such
as
AutoCAD. Other commercially available programs would then export the drawing
data to
an FEA engine.
Having a physical description of the MUA 9 (structure) alone is insufficient
information for FEA, as the loads involved are also required. Typically, the
MUA 9 is
analyzed under a regiment of anticipated loads that reflect the opinion of
experts. A
unique feature of AutoFFS is the data acquisition system 35 and sensors 36 and
37. As
discussed earlier, computer 20 may also monitor, through the data acquisition
system
35, parameters that are related to the assessment or utilization of the MUA 9
and/or
parameters to facilitate FFS and/or remaining useful life estimation. Such
parameters
may include, but not be limited to, the MUA 9 pump pressure, external
pressure, such as
the wellhead pressure, temperature, flow rate, tension, weight, load
distribution, fluid
volume and pump rate and the like. Preferably, these parameters are measured
or
acquired through sensors and/or transducers mounted throughout the MUA 9
deployment area 169, such as a rig or on the MUA 9, such as a vibration
monitor. For
ease of understanding, these various sensors and transducers are designated
with the
numeral 37. Therefore, and in addition to the physical description of the MUA
9,
AutoFFS would also acquire and export information regarding the actual
deployment
condition parameters 173 and the actual loads 174, including actual and the
unanticipated loads the MUA 9 endures resulting in a as-is and as-used FEA.
It should be understood that not all AutoFFS features can be converted to a
geometrical structure for use by an FEA engine, such as fatigue. Instead, such
features
affect the remaining useful life of the material. It should be further
understood that setting
the FEA boundaries and accepting, interpreting and understanding the overall
FEA
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CA 02632490 2008-05-22
process data and results is beyond the anticipate capabilities of the onsite
AutoFFS
operator, and therefore, this task is assigned to a remote expert 172 or group
of experts.
It may be seen from the preceding description that a novel Autonomous fitness
for continuing service assessment 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 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.
61
