Note: Descriptions are shown in the official language in which they were submitted.
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METHOD, MACHINE, AND COMPUTER MEDIUM HAVING
COMPUTER PROGRAM TO DETECT AND EVALUATE STRUCTURAL
ANOMALIES IN CIRCUMFERENTIALLY WELDED PIPELINES
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims priority to and the benefit of U.S. Provisional
Patent Application
Serial No. 61/864,095, titled "System, Method and Computer Medium Having
Computer
Program to Detect and Evaluate Structural Anomalies in Circumferentially
Welded Pipelines"
filed on August 9, 2013. This application also is a continuation-in-part of co-
pending U.S.
Patent Application Serial No. 14/039,360 titled "System, Method and Computer
Medium Having
Computer Program to Determine Presence of Stress Corrosion Cracking in
Pipelines With
Pattern Recognition" filed on September 27, 2013, which claims priority to and
the benefit of
U.S. Provisional Patent Application Serial No. 61/706,575 (now expired) filed
on September 27,
2012. This application also is a continuation-in-part of co-pending U.S.
Patent Application
Serial No. 12/953,720 titled "Computer-Implemented Method to Screen for
Longitudinal-Seam
Anomalies" tiled on November 24, 2010, which is a continuation of then pending
U.S. Patent
Application Serial No. 12/270,432, a continuation of then pending U.S. Patent
Application Serial
No. 12/949,896, and a continuation of co-pending U.S. Patent Application
Serial No.
12/950,118. This
application also is a continuation-in-part of co-pending U.S. Patent
Application Serial No. 12/950,118 titled "System to Screen for Longitudinal-
Seam Anomalies"
filed on November 19, 2010, which is a continuation of then pending U.S.
Patent Application
Serial No. 12/270,432. This application also is a continuation-in-part of U.S.
Patent Application
Serial No. 12/949,896 (now U.S. Patent No. 8,140,273) titled "Program Product
to Screen for
Longitudinal-Seam Anomalies" filed on November 19, 2010, which is a divisional
of then
pending U.S. Patent Application Serial No. 12/270,432. This application also
is a continuation-
in-part of U.S. Patent Application Serial No. 12/270,432 (now U.S. Patent No.
7,899,628) titled
"System, Method and Program Product to Screen for Longitudinal-Seam Anomalies"
filed on
November 13, 2008, which claims priority to and the benefit of U.S.
Provisional Patent
Application Serial No. 61/008,822 (now expired) filed on December 21, 2007.
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CA 02854546 2016-06-21
BACKGROUND
1. Field of Invention
[0002] The present invention relates to the detection and evaluation of
anomalies such as
cracks and incomplete fusions in welds of pipelines, e.g., pipelines for the
transport of fluids
associated with energy or other resources. In particular, the invention
relates to methods,
machines, and computer media having computer programs which utilize signal
wave form and
pattern recognition to detect and locate anomalies such as with axial magnetic
flux leakage
technology.
2. Description of Related Art
[0003] Some pipelines with segments that have been joined by welding have
experienced in-
service failures due to anomalies such as incomplete fusion and cracks. Some
of the anomalies
that potentially pose threats to the integrity of a pipeline have been
difficult to detect and
distinguish from other anomalies with some current pipeline inspection tools
and methods.
[0004] Magnetic Flux Leakage (MFL) inspection is often a robust and
reliable inspection
technology, which can be used effectively to detect 'crack-like' defects in
the welds and other
locations within a pipeline wall.
[0005] Generally, MFL technology operates on the principle that where there is
metal loss in the
pipeline, a magnetic field leaks from the metal. To implement MFL inspection
technology in a
pipeline, an MFL tool such as an In-Line Inspection (ILI) tool is often
deployed into an interior
of the pipeline and induced to travel therethrough to evaluate the pipeline
wall. In some
instances, the MFL tool includes magnets and brushes arranged to create a
magnetic circuit with
the pipeline wall and to saturate the pipeline wall with magnetic flux.
Longitudinal or
circumferential field paths are induced depending on the needs of a particular
pipeline survey.
For example, strategically placed sensors on the MFL tool can detect signals
representative of
the leakage of the magnetic flux from the pipeline wall at locations around a
circumference of
the pipeline wall over a length of the pipeline. Because anomalies, such as
metal loss within the
pipeline wall, tend to change the MFL signals detected in proportion to the
size of the anomaly,
the MFL signals detected from the pipeline wall can be analyzed to determine
the size and
location of anomalies within the pipeline wall. An example of an advanced data
analysis
technique associated with MFL technology, and developed by Applicant, can be
seen in U.S.
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Patent Nos. 7,899,628 titled "System, Method and Program Product to Screen for
Longitudinal-
Seam Anomalies" and 8,140,273 titled "Program Product to Screen for
Longitudinal-Seam
Anomalies."
[0006] In normal analysis processes utilizing Axial Flux MFL (A-MFL)
technology, on the other
hand detection processes have been primarily focused on the identification and
quantification of
volumetric metal loss anomalies along a pipeline. This technology utilizes the
amount of flux
leakage detected, the length of anomaly, and the width of anomaly (number of
channels) to
determine depth.
SUMMARY OF INVENTION
[0007] In view of
the foregoing, Applicant recognized that when a "metal-loss" sizing
algorithm is applied to narrow axial anomalies such as cracks in a
circumferential weld (girth
weld), for example, the resulting calculated depth can be considerably
shallower than the actual
depth. Because there are a limited number of channels affected by these
anomalies, the
calculated depth is low and most often below a minimum reporting threshold.
Additionally,
when the data indicative of these anomalies that are potentially threatening
to the structural
integrity of the pipeline is displayed, the data is often obscured by data
from adjacent portions of
the pipeline. These factors contribute to non-reported anomalies, which may
lead to pipeline
failures. Accordingly, Applicant also recognized a need for a new
identification machine,
process, and technology that overcomes the above-identified limitations, among
others.
Accordingly, embodiments of the present invention provide methods, machines,
and computer
media having computer programs to detect anomalies that are potentially
threatening to the
structural integrity of one or more pipelines or portions thereof such as
circumferential welds by
utilizing pattern recognition in pipeline data such as A-MFL data. A screening
process, for
example, does not affect or change how survey data is recorded such as in
survey tools; only how
it is analyzed after the collection of the survey data is completed.
Embodiments of the methods,
machines, and computer media having computer programs can be used to screen
for
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circumferential welds that are candidates for site excavation to confirm and
validate the analysis
of those welds.
[0008] Embodiments of the present invention also provide methods, machines
and computer
media having computer programs which utilize wave form analysis or pattern
recognition to
detect potentially threatening anomalies in circumferential welds. For
example, according to an
embodiment of the present invention, a pattern recognition protocol can use
axially scanned
images generated from A-MFL data to locate circumferential welds with an
elevated potential of
containing cracks or other potentially threatening anomalies.
[0009] An embodiment of a method of detecting and evaluating anomalies in
circumferential
welds of a longitudinally extending pipeline for the transport of fluids
associated with energy
therethrough includes (i) receiving, in a first process, magnetic flux leakage
data from one or
more pipeline inspection survey tools, the magnetic flux leakage data
including data being
associated with one or more circumferential welds of one or more longitudinal
pipelines, (ii)
displaying, in a second process, the magnetic flux leakage data on the one or
more displays as
one or more A-Scan images whereby each of a plurality of channels is being
represented by a
respective one of a plurality of individual lines and whereby deviations in
paths of the individual
lines represent an excess or absence of metal in the structure of the one or
more longitudinal
pipelines, (iii) identifying, in a third process, one or more weld regions in
the one or more A-
Scan images including the magnetic flux leakage data representing the one or
more
circumferential welds, (iv) analyzing, in a fourth process, the magnetic flux
leakage data within
the one or more weld regions of the one or more A-Scan images being displayed
on the one or
more displays by making a determination whether anomalous regions can be
detected within the
one or more weld regions that include one or more individual lines that
deviate from respective
paths in a manner dissimilar to a background array of lines, and (v)
generating, in a fifth process,
an output on a display to identify a location of any of the one or more
circumferential welds
corresponding to any of the one or more weld regions in which an anomalous
region was
detected in the fourth process.
[0010] An embodiment of a machine to detect anomalies in circumferential
welds in a
longitudinally extending pipeline associated with the transport of fluids
associated with energy or
other resources therethrough, for example, can include one or more displays,
one or more
processors in communication with the one or more displays and being adapted to
process data
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received from one or more pipeline inspection survey tools, and non-transitory
storage medium
or media having one or more computer programs stored thereon and readable by
the one or more
processors. The one or more computer programs can include a set of
instructions that, when
executed by the one or more processors, causes the one or more processors to
perform the
operations of: (i) receiving, in a first process, magnetic flux leakage data
from the one or more
pipeline inspection survey tools related to one or more circumferential welds
in one or more
longitudinal pipelines, (ii) displaying, in a second process, the magnetic
flux leakage data on the
one or more displays as one or more selected patterns of data representing
selected signal
characteristics of the one or more circumferential welds, (iii) analyzing, in
a third process, the
magnetic flux leakage data responsive to the selected signal characteristic
and one or more
predetermined patterns of the magnetic flux leakage data of the pipeline joint
being displayed on
the one or more displays, the one or more predetermined patterns of the
magnetic flux leakage
data being indicators of one or more anomalies potentially threatening the
structural integrity of
the pipeline, and (iv) generating, in a fourth process, an output identifying
a location and
characterization of the one or more anomalies identified.
[0011] An
embodiment of a method of detecting and evaluating anomalies in
circumferential
welds of a longitudinally extending pipeline for the transport of fluids
associated with energy
therethrough, for example, can include (i) receiving, in a first process,
magnetic flux leakage data
from the one or more pipeline inspection survey tools, the magnetic flux
leakage data being
associated with one or more circumferential welds of one or more longitudinal
pipelines, (ii)
displaying, in a second process, the magnetic flux leakage data on the one or
more displays as
one or more selected patterns of data representing selected signal
characteristics of the one or
more circumferential welds, (iii) analyzing, in a third process, the magnetic
flux leakage data of
the one or more circumferential welds being displayed on the one or more
displays by making a
determination whether the displayed patterns of data are consistent with one
or more
predetermined patterns of magnetic flux leakage data, the one or more
predetermined patterns of
the magnetic flux leakage data being indicators of one or more anomalies
within the one or more
circumferential welds that are potentially threatening the structural
integrity of the pipeline, (iv)
generating, in a fourth process, an output identifying a location and
characterization of any of the
one or of more circumferential welds for which the displayed patterns of data
was determined to
be consistent with the one or more predetermined patterns in the third
process, and (v) validating,
CA 02854546 2014-06-17
in a fifth process, the output by excavating one or more of the
circumferential welds identified in
the output and inspecting the one or more of the circumferential welds
excavated to confirm the
presence of one or more anomalies within the one or more or the
circumferential welds
excavated that are potentially threatening the structural integrity of the
pipeline.
[0012] An embodiment of a non-transitory storage medium or media having one
or more
computer programs stored thereon and readable by one or more processors, may
include, for
example, one or more computer programs including a set of instructions that,
when executed by
the one or more processors, causes the one or more processors to perform the
operations of (i)
receiving, in a first process, magnetic flux leakage data from the one or more
pipeline inspection
survey tools, the magnetic flux leakage data being associated with one or more
circumferential
welds of one or more longitudinal pipelines, (ii) displaying, in a second
process, the magnetic
flux leakage data on the one or more displays as one or more selected patterns
of data
representing selected signal characteristics of the one or more
circumferential welds, (iii)
analyzing, in a third process, the magnetic flux leakage data of the one or
more circumferential
welds being displayed on the one or more displays by making a determination
whether or not the
displayed patterns of data are consistent with one or more predetermined
patterns of magnetic
flux leakage data, the one or more predetermined patterns of the magnetic flux
leakage data
being indicators of anomalies within the one or more circumferential welds
that are potentially
threatening the structural integrity of the pipeline, and (iv) generating, in
a fourth process, an
output identifying a location and characterization of any of the one or of
more circumferential
welds for which the displayed patterns of data was determined to be consistent
with the one or
more predetermined patterns of in the third process.
[0013] Embodiments of methods, machines, and computer media having computer
programs,
for example, can include an identification process developed through
utilization of A-MFL
inspection technology taken to a new level of sophistication with a
disciplined methodical
evaluation of data and data signals. Particularly, for example, embodiments of
the present
invention can include supplemental screening processes applied to pipeline
survey data, which
utilize an A-MFL method and wave form analysis or pattern recognition to
identify anomalies in
circumferential welds potentially threatening the structural integrity of the
pipeline. The
screening process of embodiments of the present invention does not need to
affect or change how
6
the survey data is recorded in the ILI survey tools or other pipeline
inspection tools if that is not
desired; only how it is analyzed after the collection of survey data is
completed.
[0014] Embodiments of methods, machines, and computer media having computer
programs
of the present invention, can include confirmation and validation of the
process applicability in
each case. The confirmation, for example, minimally can include several
validation excavations
utilizing "highest level" Non-Destructive Evaluation (''NDE") methods and, in
some cases, can
require removal of appropriate samples for destructive metallurgical
evaluation in a laboratory.
BRIEF DESCRIPTION OF DRAWINGS
[0015] Some of the features and benefits of the present invention having
been stated, others
will become apparent as the description proceeds when taken in conjunction
with the
accompanying drawings, in which:
[0016] FIG. 1 is a flow chart illustrating one exemplary method to determine
locations having an
elevated potential of containing threatening anomalies in one or more
circumferential pipeline
welds according to an embodiment of the present invention;
[0017] FIG. 2 is a graphical view of an Axial-Scan (A-Scan) computer
display showing
magnetic flux leakage data indicative of a level-one pipeline anomaly in a
circumferentially
welded seam whereby the x-axis corresponds to a longitudinal position along
the pipeline and the
y-axis corresponds to both a circumferential or radial position about the
pipeline and to an
amplitude of a magnetic flux leakage signals according an embodiment of the
invention;
[0018] FIG. 3 is a graphical view of an A-Scan computer display showing the
magnetic flux
leakage data depicted in FIG. 2 displayed in a manner indicative of a level-
two pipeline anomaly
according to an embodiment of the present invention;
[0019] FIG. 4 is a graphical view of an A-Scan computer display showing the
magnetic flux
leakage data depicted in FIGS. 2 and 3 displayed in a manner indicative of a
level-three pipeline
anomaly, whereby each channel within the anomalous area is isolated and shaded
for greater
visibility according to an embodiment of the present invention;
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[0020] FIG. 5 is a graphical view of an A-Scan computer display showing the
magnetic flux
leakage data depicted in FIG. 4 whereby having only every third channel being
displayed to
clarify the image of FIG. 3 according to an embodiment of the present
invention;
[0021] FIG. 6 is a graphical view of an A-Scan computer display showing the
magnetic flux
leakage data depicted in FIG. 4 having only every tenth channel being
displayed to clarify the
image of FIG. 4 according to an embodiment of the present invention;
[0022] FIG. 7 is a flow chart illustrating another exemplary method of
determining locations
having an elevated potential of containing threatening anomalies in one or
more circumferential
pipeline welds according to an embodiment of the present invention; and
[0023] FIG. 8 is a schematic view of a machine to detect and locate
anomalies in
circumferential pipeline welds having an elevated potential of threatening the
structural integrity
of the pipeline according to an embodiment of the present invention.
DETAILED DESCRIPTION OF INVENTION
[0024] The present invention will now be described more fully hereinafter
with reference to
the accompanying drawings in which embodiments of the invention are shown.
This invention
may, however, be embodied in many different forms and should not be construed
as limited to
the illustrated embodiments set forth herein; rather, these embodiments are
provided so that this
disclosure will be thorough and complete, and will fully convey the scope of
the invention to
those skilled in the art. Like numbers refer to like elements throughout.
[0025] An embodiment of the present invention, for example, can include a
supplemental
screening process applied to survey data utilizing display software, such as,
for example, the
ROSEN ROSOFT for Pipelines display software manufactured by ROSEN Swiss AG of
Stans,
Switzerland (such as, for example, version 6.60), as understood by those
skilled in the art, to
detect and locate anomalies in circumferential pipeline welds having an
elevated potential of
threatening the structural integrity of the pipeline. The ROSEN ROSOFT display
software may
be employed to display data collected by ROSEN USA, a pipeline inspection
service provider
that identifies one of their circumferential field tools as "Corrosion
Detection Pig" (CDP).
Although embodiments of the present invention are described in conjunction a
single software
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provider package, other forms of display software can be utilized as well as
will be understood
by those skilled in the art.
[00261 An embodiment of a method 100 of the present invention, for example,
such as shown
in FIG. 1, can begin by collecting A-MFL data from a pipeline (step 102) using
a survey tool,
such as, for example, an inline inspection ("IL!") tool as understood by those
skilled in the art.
The A-MFL data, for example, can be primarily influenced by anomaly air gaps,
which are a
function of anomaly length and depth, steel properties, and hoop stress. Once
the A-MFL data
has been collected using the survey tool at step 102, as understood by those
skilled in the art, it
can be transmitted to one or more computers having one or more processors
(see, for example,
processor 306 depicted in FIG. 8), as understood by those skilled in the art,
for analysis. Such a
transmission can be achieved via any number of wired or wireless
communications techniques.
At step 104, the processor then causes the A-MFL data to be displayed on a
display, such as one
or more electronic displays, display region, or the like, as understood by
those skilled in the art,
as one or more patterns of data representing pipeline conditions at
identifiable circumferential
weld locations. The pipeline conditions displayed are based on -MFL signal
characteristics
detected by the survey tool. As indicated in greater detail below, these
signal characteristics can
be displayed as, in some embodiments, line traces on an A-Scan image so that
each channel is
being represented by one or more individual lines.
[0027] At step 106,
the A-MFL data is analyzed based upon its respective signal
characteristics. In embodiments, an advanced data analysis technique known as
Kinder Morgan's
KMAP process is performed by the processor. Portions of the KMAP process are
defined in
Protocol PI 3, "KMAP Screening for Long Seam Anomaly Evaluation, ROSEN
Software
Package," and generally is employed in conjunction with a body scan and a
longitudinal weld
scan for evaluating the integrity of longitudinal welds in the pipeline. Based
on the results of the
KMAP Protocol, as understood by those skilled in the art, embodiments of
machines, methods,
and computer media having computer programs can additionally analyze the A-MFL
data
associated with one or more circumferential welds in the pipeline to determine
if patterns
indicative of potentially elevated risks of threatening anomalies are found to
be contained in the
data. One or more exemplary embodiments of these patterns are illustrated in
FIGS. 2-6. At step
108, for example, an output is generated including a list of potentially
threatening anomalies in
the pipeline. Such an output can typically be a summary
9
display or spreadsheet, associated screen captures and suggested excavation
locations. The
process 100 ends after step 108, although in other embodiments, such as in the
method 200
described below with reference to FIG. 7, the method can be iteratively
refined after excavating
and evaluating one or more pipeline joints as recommended in the output
generated in step 108.
[0028] FIG. 2 illustrates an example of an A-Scan computer display 150
showing the A-MFL
data from one pipeline location having a circumferential weld known to contain
a crack-like
anomaly that is potentially threatening to the pipeline being depicted. The x-
axis in FIG. 2
represents a longitudinal location along the pipeline, and the y-axis
represents a circumferential
location about a full 360 degree circumference of the pipeline wall. Each
channel of the A-MFL
data collected is represented as an individual line extending in a generally
horizontal direction
from an upstream location (generally to the left in FIG. 2) to a downstream
location (generally to
the right in FIG. 2), for example.
[0029] A weld region can be identified in FIG. 2 in locations where data
representative of a
circumferential weld is being displayed. The weld region in FIG. 2 includes
the centrally located,
generally vertical feature formed from fluctuations in each of the lines
representing the
individual channels. At the longitudinal location of the circumferential weld,
the lines generally
deviate upwardly from the general horizontal direction. The upward deviation
is an indication of
the excess metal that would be expected at a weld location as compared to the
metal content of
the surrounding upstream and downstream locations in the pipeline structure.
The upward
deviation of most of the lines for example, can be indicative of signals that
may be characterized
as "low frequency" as the upward deviation is gradual at the central weld
location.
[0030] A suspected anomalous region in the weld region of FIG. 2 can be
identified as
indicated by an oval circumscribing the anomaly in FIG. 2. In some of the
channels representing
the anomaly, the upward deviation appears slightly to the right, or on an
upstream side of the
circumferential weld. These channels or individual lines deviate from
respective paths in a
manner dissimilar to a background array of lines. From the view depicted in
FIG. 2, no
meaningful assessment can be made for the potential anomaly that would
indicate whether the
anomaly represents a defect in the weld that could threaten the structural
integrity of the pipeline.
Thus, the view in FIG. 2 can be used in some embodiments of a method to
characterize the
anomaly as a level-one anomaly, which merits further analysis by clarification
of the image and
further analysis of the data.
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100311 FIG. 3 illustrates the suspected anomalous area being identified in
the circumferential
weld and being enlarged in comparison to the view depicted in FIG. 2 such that
only
approximately one third of the circumference (120 degrees) of the pipeline
wall is depicted. This
view is sufficient to encompass the data representing the anomaly and
indicates that some of the
lines deviate downwardly within the region. This downward deviation is an
indication of an
anomalous gap in the metallic structure of the pipeline. The lines appear to
slope gradually
downwardly in a manner consistent with the lines representing the low
frequency A-MFL signals
of the upwardly sloping lines indicating excess metal in the circumferential
weld. This view is
also adequate for an analyst to determine that the location represented is
worthy of further
investigation by excavating the pipeline and visually inspecting the pipeline,
or otherwise
evaluating the structural integrity of the pipeline by NDE methods and, in
some cases, may
require removal of appropriate samples for destructive metallurgical
evaluation in a laboratory.
Thus, the view in FIG. 3 can be used in some embodiments of a method to
characterize the
anomaly as a level-two anomaly, which merits further analysis by excavation
and inspection.
100321 FIG. 4 illustrates one or more suspected anomalous areas identified
in the
circumferential weld being enlarged in comparison to the view depicted in FIG.
3 such that a
lesser extent of the longitudinal length of the pipeline is represented in the
display. Each channel
within the magnetic anomalous area is isolated and displayed in a
distinguishing shade 152 with
respect to the surrounding channels. In this view of FIG. 4, it is apparent to
those skilled in the
art that high frequency signals are present within the larger, low frequency
array of signals
represented. These relatively high-frequency signals can be indicators of
crack-like defects in the
circumferential weld that potentially threaten the structural integrity of the
pipeline. The exact
range of frequencies that indicate crack-like defects can depend on many
variables including
variables associated with the construction of the pipeline, the type of
inspection performed, and
other factors as will be understood by those skilled in the art. A relative
analysis of the high-
frequency signals with respect to surrounding low-frequency signals accounts
for variations in
many of these variables. The views depicted in FIGS. 2 and 3 obscure the
presence of these high-
frequency signals, which are prominently displayed in FIG. 4. Thus, the view
in FIG. 4 can be
used in some embodiments of a method to characterize the anomaly as a level-
three anomaly.
Level-three anomalies can be defined to include those anomalies consistent
with crack like
defects that potentially threaten the structural integrity of a longitudinal
pipeline. In-line-
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inspection vendors do not generally identify these potentially threatening
defects in pipeline
welds, but the discipline of the KMAP process has been further developed to
include processes
to identify, display and clarify these indicators as noted and described
herein.
[0033] In FIG. 5, the display of FIG. 4 is clarified further by displaying
only every third
channel while prominently displaying the high-frequency signal in a
distinguishing shade 152. In
FIG. 6, the display is further clarified by removing additional background
clutter and only
displaying every tenth channel and further zooming in on the high-frequency
signal such that
only about a quarter of the circumference (90 degrees) of the pipeline wall is
depicted. The view
in FIG. 6 is considered an optimum or desired clarification providing clear
delineation of crack-
like signals in the A-MFL data.
[0034] FIG. 7 illustrates an embodiment of a procedure 200 describes a
supplemental
screening process applied to ROSEN CDP survey data using the ROSEN ROSOFT for
Pipelines
or GE-PH "MFL" survey using its PipeImage Display Software, as will be
understood by those
skilled in the art, to identify pipeline locations with circumferential welds
with an elevated
potential of containing crack-like defects or other anomalies that can
threaten the integrity of the
pipeline. Aspects of this procedure 200, for example, can be called a "Wave
form analysis or
pattern recognition" process and may, in some instances be intended to
directly detect crack-like
anomalies in circumferential welds, and in other instances may be intended to
detect the
environmental and operating conditions that could lead to the development of
crack-like
anomalies. The result of the wave form analysis or pattern recognition
process, for example, can
be a ranking, e.g., subjective, the anomalies based on relative signal
characteristics.
[0035] An embodiment of this wave form analysis or pattern recognition
process or protocol,
for example, identifies circumferential welds with an elevated risk of
containing or developing
crack-like anomalies. The application or use of this process, in some
embodiments, can be
sensitive to the pipeline steel properties, the coating condition of the
pipeline, the operating
environment of the pipeline, the capabilities of the specific pipeline
inspection tool, survey tool
or ILI tool employed, and the pipeline operating conditions under which the
survey was
conducted. Thus, when applying the pattern recognition protocol to other
conditions, each
variable may be considered and adjusted based on the specific application.
[0036] The process 200 begins with the collection of A-MFL data (step 202)
from a pipeline
including circumferential welds. The data is collected from one or more
pipeline inspection
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tools, and in some embodiments, this step may be performed by an ILI vendor
providing a more
general assessment of pipeline conditions. For example, in some embodiments,
the A-MFL data
includes data received from the ROSEN CDP pipeline inspection tools.
[0037] Next, A-MFL data is received by one or more processors (step 204)
from the one or
more pipeline inspection tools. The processor identifies segments of the A-MFL
data that contain
representations of circumferential welds and displays images representing
these segments of data
(step 206) on one or more displays, such as, for example an LCD computer
screen. The A-MFL
data is displayed as one or more selected patterns of data representing
selected characteristics of
a signal detected by the pipeline inspection tool from a wall of the pipeline.
In some
embodiments, the selected patterns of data include A-Scan images whereby
adjacent lines
representing signal characteristics representative of a degree of metal
present at a location in the
pipeline wall, as in the A-scan images of FIGS. 2-6 discussed above. In some
embodiments, the
selected patterns can include representations of pipeline conditions over a
full 360 degree
circumference of the pipeline as in the A-scan image of FIG. 2. In other
embodiments, the
selected patterns of data include contrasting shades, smooth waveforms,
erratic/non-erratic
patterns, or symmetrical patterns for representing the selected signal
characteristics.
[0038] Once displayed, the A-MFL data is analyzed (step 210). The analysis,
for example,
can include a determination of whether the displayed pattern is consistent
with predetermined
patterns of level-one anomalies (step 212). To make this determination, an
analyst may visually
inspect the displayed image and assess the similarities and differences
between the displayed
image and the target pattern, or the pattern can be electronically compared
with side-by-side or
overlays of known patterns on one or more displays as understood by those
skilled in the art. For
some embodiments of the present invention, analysts, operators or implementers
performing an
embodiment of a pattern recognition scan, as understood by those skilled in
the art, desirably
have some familiarity with pipelines, pipeline construction techniques, ILI
tools and tool data
and preferably have a working knowledge of pipeline inspection tools. In other
embodiments,
one or more processors makes the determination, and an analyst further can
confirm the
determination, if desired, by viewing displayed patterns of data. Generally,
the predetermined
patterns of level-one anomalies may include features distinguishable from
adjacent features such
as the features encircled in the A-scan image of FIG. 2. If no level-one
anomalies can be
identified in a displayed image, the process 200 returns to step 206, and
another segment of data
13
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representing a different circumferential weld or another portion of the same
circumferential weld
is displayed. If a level-one anomaly is identified, the process 200 proceeds
to clarify the
displayed image (step 214). In some embodiments, the clarification of step 214
incudes enlarging
or zooming in on the image of the level-one anomaly identified with software
tools associated
with the display.
[0039] Once the image is clarified, the analysis includes a determination
of whether the
displayed pattern is consistent with predetermined patterns of level-two
anomalies (step 216).
Again, if no level-two anomalies can be identified in the displayed image as
clarified in step 214,
the process 200 returns to step 206. If a level-two anomaly is identified, the
process 200 proceeds
to further clarify the displayed image (step 218). The clarification of step
218 incudes further
enlarging and distinguishing relevant portions of the displayed image, such as
the channels
identified by a distinct and distinguishing shade 152 FIG. 4. In some
embodiments, level-two
anomalies include those warranting field investigations for inspection or
repair. The analysis
continues to a determination of whether the displayed pattern is consistent
with predetermined
patterns of level-three anomalies (step 220). Again, if no level-three
anomalies can be identified
in the displayed image as clarified in step 218, the process 200 returns to
step 206. If a level-
three anomaly is identified, the process 200 proceeds to further clarify the
displayed image (step
222). The clarification of step 218 incudes further enlarging the relevant
portions of the image
and removing representations of data deemed to be irrelevant. The resulting
images from step
222 may include the A-scan images of FIGS. 5 and 6.
[0040] Because different pipelines, for example, may require unique
software or program
product display values due to differences in magnetic characteristics,
diameter, wall thickness,
grade, and tool speed during the actual inspection, for example. Thus, step
210, in some
embodiments, includes applying one or more pipeline variable characteristics
to the magnetic
flux leakage data being displayed on the one or more displays, The display
software utilized as
part of embodiments of the present invention, for example, can have an icon,
button or other user
interface on the display that, when clicked or operated, automatically sets
the display values for
displaying a particular portion of the circumference of the pipeline.
[0041] Once each of the images selected for analysis is assessed and the
appropriate images
have been characterized as level-one, 2 or 3, an excavation validation report
is generated (step
224) identifying locations of circumferential welds containing anomalies
potentially threatening
14
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=
the structural integrity of the pipeline. The excavation and validation report
may include
indications of the levels assigned to the images, and may include the images
themselves.
[0042] An embodiment of the wave form analysis or pattern recognition
process or protocol,
as applied to this complex anomaly discrimination, should include and may
require confirmation
and validation of process applicability in each case. The confirmation can
minimally include
several validation excavations utilizing "highest level" NDE methods and, in
some cases, may
require removal of appropriate samples for destructive metallurgical
evaluation in a laboratory.
A site excavation is requested or performed (step 226) for at least one of the
locations identified
in the excavation and validation report.
[0043] Based on the results of any or several validation excavations
performed, the
predetermined patterns of data indicative of level-one, 2 or 3 anomalies may
be refined and the
process 200 returns to step 202 where additional circumferential welds may be
evaluated. In all
cases the results of field investigations can be reported to appropriate
pipeline management
personnel and can be compared to the assessment.
[0044] FIG. 8 illustrates an embodiment of a machine 300 to detect and
characterize
anomalies in circumferential welds 302A and 302B of a longitudinally extending
pipeline 302
positioned to transport fluids associated with energy or other resources
therethrough. For
example, an embodiment of a machine can include one or more displays 304, one
or more
processors 306 in communication with one or more pipeline inspection survey
tools 308, and
non-transitory storage media 310 having one or more computer programs stored
thereon and
readable by the one or more processors 306. The one or more computer programs
can include a
set of instructions that, when executed by the one or more processors 306,
causes the one or
more processors 306 to perform the operations of: (i) receiving, in a first
process, MFL data from
the one or more pipeline inspection survey tools 308 related to the one or
more circumferential
welds, weld 302A for example, of one or more longitudinal pipelines 302, (ii)
displaying, in a
second process, the MFL data on the one or more displays 304 as one or more
selected patterns
of data representing selected signal characteristics of the circumferential
weld 302A,
(iii) analyzing, in a third process, the MFL data responsive to the selected
signal characteristics
and one or more predetermined patterns of the MEI, data of the circumferential
weld 302A being
displayed on the one or more displays 304, the one or more predetermined
patterns of the MFL
data being indicators of anomalies in the circumferential weld 302A
potentially threatening the
CA 02854546 2014-06-17
structural integrity of the pipeline 302, and (iv) generating, in a fourth
process, an output
identifying a location and characterization of the anomalies identified.
[0045] The non-transitory storage media 310, for example, also can serve to
store report data
to generate excavation validation report data. Also, the non-transitory
storage media 310 can be
updated with confirmation data including whether confirmation of the presence
of anomalies at
an excavated site occurred thereby to further assess additional A-MFL data
associated with the
machine 300.
[0046] In some embodiments, a user 316 such as an analyst can view the one
or more displays
304 to assess and evaluate the one or more selected patterns of data displayed
thereon to confirm
a determination made by the processor 306. In other embodiments, the user 316
can facilitate the
determination by providing an input to the one or more processors 306 based on
an assessment
made by viewing the one or more displays 304.
[0047] It is to be understood by those skilled in the art that the
invention is not limited to the
exact details of construction, operation, exact materials, or embodiments
shown and described, as
modifications and equivalents will be apparent to one skilled in the art. For
example, although
discussed as steps in a computerized process, steps of the present invention
may also be
accomplished manually. In addition, although aspects of the present invention
have been
described with respect to a computer, a computer device, a computer machine,
or processor
executing program product or software that directs the functions of
embodiments of the present
invention, it should be understood by those skilled in the art that the
present invention can be
implemented as a program product for use with various types of data processing
machines as
well. Programs defining the functions of embodiments of the present invention,
for example, can
be delivered to a data processing machine via a variety of signal-bearing
media, which include,
without limitation, non-rewritable storage media (e.g., CD-ROM, DVD-ROM, or
BluRay),
rewritable storage media (e.g., floppy disks, hard drive disks, CD-R,
rewritable ROM media, or
rewritable BluRay), and communication media, such as digital and analog
networks. It should be
understood, therefore, that such signal-bearing media, when carrying or
embodying computer
readable instructions that direct the functions of embodiments of the present
invention, represent
alternative embodiments of the present invention.
[0048] This application claims priority to and the benefit of U.S.
Provisional Patent
Application Serial No. 61/864,095, titled "System, Method and Computer Medium
Having
16
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Computer Program to Detect and Evaluate Structural Anomalies in
Circumferentially Welded
Pipelines" filed on August 9, 2013. This application also is a continuation-in-
part of co-pending
U.S. Patent Application Serial No. 14/039,360 titled "System, Method and
Computer Medium
Having Computer Program to Determine Presence of Stress Corrosion Cracking in
Pipelines
With Pattern Recognition" tiled on September 27, 2013, which claims priority
to and the benefit
of U.S. Provisional Patent Application Serial No. 61/706,575 (now expired)
filed on September
27, 2012. This application also is a continuation-in-part of co-pending U.S.
Patent Application
Serial No. 12/953,720 titled "Computer-Implemented Method to Screen for
Longitudinal-Seam
Anomalies" filed on November 24, 2010, which is a continuation of then pending
U.S. Patent
Application Serial No. 12/270,432, a continuation of then pending U.S. Patent
Application Serial
No. 12/949,896, and a continuation of co-pending U.S. Patent Application
Serial No.
12/950,118. This
application also is a continuation-in-part of co-pending U.S. Patent
Application Serial No. 12/950,118 titled "System to Screen for Longitudinal-
Seam Anomalies"
filed on November 19, 2010, which is a continuation of then pending U.S.
Patent Application
Serial No. 12/270,432. This application also is a continuation-in-part of U.S.
Patent Application
Serial No. 12/949,896 (now U.S. Patent No. 8,140,273) titled "Program Product
to Screen for
Longitudinal-Seam Anomalies" filed on November 19, 2010, which is a divisional
of then
pending U.S. Patent Application Serial No. 12/270,432. This application also
is a continuation-
in-part of U.S. Patent Application Serial No. 12/270,432 (now U.S. Patent No.
7,899,628) titled
"System, Method and Program Product to Screen for Longitudinal-Seam Anomalies"
filed on
November 13, 2008, which claims priority to and the benefit of U.S.
Provisional Patent
Application Serial No. 61/008,822 (now expired) filed on December 21, 2007.
[0049] In the
drawings and specification, there have been disclosed illustrative embodiments
of the invention and, although specific terms are employed, they are used in a
generic and
descriptive sense only and not for the purpose of limitation. Accordingly, the
invention is
therefore to be limited only by the scope of the appended claims.
17
