Note: Descriptions are shown in the official language in which they were submitted.
CA 02414644 2002-12-18
Attorney docket no.: T9305507
Filename: 382927 v.1
Data Management of Pipeline Datasets
Field of the Invention
This invention relates generally to data management and particularly to
tracking relevancy of multiple pipeline datasets to form an integrated
dataset.
Background of the Invention
The petroleum pipeline infrastructure in North America, estimated at three
million miles of pipeline, was constructed over a period of eighty years. Many
of
the operating pipelines are now more than 50 years old. In recent years public
concern has arisen as a result of several high profile pipeline incidents that
have
had significant consequences, including the loss of life. As a result there is
increased emphasis on improving the management of pipeline integrity. This
increased emphasis has taken the form of laws, regulations, and industry
standards leading to improved pipeline company practices.
A key component of effective pipeline integrity management is the
integration of information about the condition of a pipeline so that site-
specific risk
analysis can be carried out to prioritize inspection and repair. As part of
the
process, data from multiple sources using multiple coordinate systems need to
be translated and correlated into a common frame of reference so that data
features can be aligned for observation of coincident events.
Unfortunately, in many cases pipeline operators are overwhelmed by this
data and cannot effectively access, integrate, or analyze data relationships,
thus
limiting the value of this data in decision-making processes. There is thus a
need
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to provide the pipeline industry with an effective and affordable way to meet
these regulatory and operating challenges, and in particular, a need to
effectively
manage the vast amounts of collected data relating to pipeline infrastructure,
in a
way that enables pipeline operators to maintain pipeline integrity.
If the underlying relevancy of the data is not managed and communicated, the
value of the data integration and any subsequent analysis of the combined data
set
is questionable.
Summary of the Invention
According to one aspect of the present invention, there is provided a
method of managing pipeline datasets comprising the steps of: establishing an
asset survey for a pipeline by correlating a first data set with a spatial
representation of the pipeline, the data set comprising a plurality of point
features
along a length of the pipeline, each point feature having a source type, a
source
name, an indication type, and a status associated therewith, the source type
having a relative priority; integrating a second data set with the asset
survey; and
setting the status field of each feature point in dependence upon at least one
of
its relative age, source and type.
According to another aspect of the present invention, there is provided
apparatus for managing pipeline datasets comprising: a centerline model for a
pipeline; a module for establishing an asset survey for a pipeline by
correlating a
first data set with a spatial representation of the pipeline, the data set
comprising a
plurality of point features along a length of the pipeline, each point feature
having a
source type, a source name, an indication type, and a status associated
therewith, the source type having a relative priority; a weld matching module
for
integrating a second data set with the asset survey; and a data management
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module for setting the status field of each feature point in dependence upon
at
least one of its relative age, source and type.
In accordance with an aspect of the present invention there is provided a
method of managing pipeline datasets comprising the steps of:
establishing, using a data processor, an asset survey for a pipeline by
correlating a first data set with a spatial representation of the pipeline,
the first
data set comprising a plurality of point features along the length of the
pipeline,
each point feature having a source, a name, a type and a status associated
therewith, the source having a relative priority;
integrating, using the data processor, a second data set with the asset
survey, and correlating the second data set with the spatial representation of
the
pipeline;
setting, using the data processor, the status field of each point feature in
dependence upon at least one of its relative age, source and type; and
displaying the spatial representation of the pipeline and selected
information from the asset survey and the second data set on a viewer.
In accordance with a further aspect of the present invention there is
provided an apparatus for managing pipeline datasets comprising:
a non-transitory computer readable medium having stored thereon a
dataset management program executable by a data processor, the dataset
management program comprising:
a centerline model for a pipeline;
a module for establishing an asset survey for a pipeline by
correlating a first data set with a spatial representation of the pipeline,
the data set
comprising a plurality of point features along a length of the pipeline, each
point
feature having a type, a name, and a status associated therewith, the type
having
a relative priority;
a weld matching module for integrating a second data set with the
asset survey and correlating the second data set with the spatial
representation of
the pipeline; and
a data management module for setting the status field of each point
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feature in dependence upon at least one of its relative age and type; and
a viewer configured to display the spatial representation of the pipeline and
selected information from the first and second data sets.
All "on pipe" point features are defined by their source type (Source), the
particular data set identified as the source name (Name), and the indication
type
(Type) in order to empower subsequent data management processes. An
example of a Source/Source Name combination would be ILI (in-line
inspection)/1998 MFL Run. This structure ensures the ability to trace a record
to
the originating data source.
In order to effectively manage the integrity of a pipeline it is imperative
that
the data be effectively managed such that overall relevancy of the individual
data
set is clearly communicated to the users of the data. Although individual
elements
such as date, spatial accuracy and measurement accuracy are all important
parameters, they are not always available to, nor understood by, the users or
stakeholders. Consequently, the need exists for an overarching framework to
clearly communicate the relevancy of the data set as well as define associated
rules as to how that data set will impact other data sets or processes.
The data field "status" was developed and a framework of processes and
algorithms were defined to provide structure and flexibility to the management
of
the assignment of a record's "status". The field "status" has five distinct
states;
active, inactive, archived, super inactive and super active. Active is
representative of the latest and most accurate condition of the pipeline to
your
knowledge. Inactive indicates that the record has either been superseded by
better data or the record is from new data and has not yet been validated as
accurate. Super inactive indicates the record has been superseded to the
extent
that it cannot subsequently be made active through normal data management
processes. Archived indicates the record is associated with a piece of pipe
that
was removed from service (i.e. cut out of the pipeline). Where as super active
indicates that the record is an absolute fact and that irrespective of
whatever
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other data is subsequently gathered for that section of pipeline, the super
active
record will always been seen as active or representative of the current
condition
of the pipeline.
Key determinants in a records status are the chronology and source of the
data set for a particular section of pipe. An application of this is that the
ILI for
the extent of pipe that is exposed during an excavation will be automatically
turned super inactive. The ILI data has been superseded by the excavation data
because the excavation data is based on the empirical inspection of the pipe
surface as opposed to the inferential interpretation of the ILI data. This
process
can be applied one step further in the assignment of Super Active status to
the
NDE records. In this case if the defects found in the excavation were coated
with
a highly reliable coating (liquid urethane epoxy applied under controlled
conditions) they would accurately represent the condition of the pipe
irrespective
subsequent ILI data.
Similarly, when a new ILI run is imported, its point indication data will
assume the status of inactive until it can be validated as being an accurate
representation of the pipe condition. Beyond that point each type of
indication
(e.g. weld, metal loss, geometry etc) can be individually assigned a status
based
on different rule sets for different linear extents of the ILI. The result is
not only
timely status management, but also an opportunity for the integrity engineer
to
share his interpretation expertise to the corporate enterprise though the
highly
granular application of status assignment.
Brief Description of the Drawings
The present invention will be further understood from the following detailed
description, with reference to the accompanying drawings, in which:
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Fig. 1 illustrates in a functional block diagram data flow into and out of a
pipeline data processor programmed to manage multiple pipeline datasets in
accordance with an embodiment of the present invention;
Fig. 2 illustrates in a flowchart of an inline inspection (IL!) dataset
integration process performed by the pipeline data processor of Fig. 1;
Figs. 3a and 3b illustrate in functional block diagrams representing
pipeline data and the management thereof in accordance with an embodiment of
the present invention;
Fig.4 illustrates in a flowchart a method of managing relevancy of data
during the NDE dataset integration process of Figs. 3a and 3b; and
Fig. 5 illustrates in a flowchart of a method of managing relevancy of data
during the process of the ILI dataset integration process of Figs. 3a and 3b.
Detailed Description of Embodiments of the Invention
One of the regulatory requirements for an integrity management program
is for a pipeline operator to integrate pipeline information from diverse data
sources so that a comprehensive analysis can be made of the threats to a
pipeline's integrity. These data sources include:
Pipe property: This data is associated with the specifications and properties
of
the pipe, e.g. pipe thickness, coating data. The
properties are generally
consistent within each pipe joint. Consequently, these properties typically
transition at the welds that join the joints together.
CA 02414644 2002-12-18
Above-ground surveys: This data includes all the above ground surveys
associated with Direct Assessment (DA), such as Cathodic Protection (CP),
Direct Current Voltage Gradient (DCVG), Pipeline Current Mapper (PCM), as well
as other environmental parameters such as land use and topography.
In-Line Inspections (IL!): This data is collected from the measurements taken
by
inspection vehicles that travel along the interior of a pipeline. Such
measurements are known as ILI surveys, and are typically performed while the
pipeline is in operation; product flow is typically used to propel the
inspection
vehicle, although self-propelled models can also be used. The data collected
by
the ILI survey includes pipeline features such as valves, welds, and branch
connections, and defects such as metal loss, dents, and cracks.
The data collected by the ILI survey is spatially organized by referencing
the location of each measurement point in terms of a linear distance along the
pipe to a selected start position in the pipe, e.g. the start of the ILI run.
The linear
distance can be determined by an odometer on the inspection vehicle, which
tracks the distance travelled by the vehicle as it rolls along the inside the
pipeline.
Excavation: For purposes of this document excavation data is termed non-
destructive examination (NDE) data. This data is generated from measurements
taken during the course of an excavation or dig that exposes the pipe surface.
The measurements can be spatially referenced to a control point, typically a
girth
weld that was exposed in the excavation.
Operational: This data typically relates to the product or environment inside
the
pipe.
According to an embodiment of the invention, there is provided a spatial
integration method for spatially integrating datasets from different data
sources,
to produce a single integrated dataset that can be used, for example, by a
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pipeline operator to maintain the integrity of his pipeline. Unlike
conventional GIS
systems that simply overlay datasets for viewing purposes, the spatial
integration
method of this embodiment uses elements or fit points within each different
dataset to spatially align the datasets not only relative to each other, but
also to a
"real world" location. Furthermore, the spatial integration method is provided
with
steps that spatially integrate multiple ILI datasets, by matching their
respective
welds. This enables new ILI datasets to be readily integrated with existing
ILI
datasets for a particular pipeline section.
Referring to Fig. 1, a data processor 12 is programmed with a pipeline
dataset management program that can integrate multiple pipeline datasets and
display information selected from one or more integrated datasets. The dataset
management program comprises a weld-matching module 13 for spatially
integrating ILI runs, to produce an integrated below-ground dataset ("asset
survey"), and a centreline fitting program 14 to spatially integrate the asset
survey against a 3-D pipeline model 16 and above-ground survey datasets 18.
Selected information from the spatially integrated above-ground and below-
ground datasets are transmittable to a viewer 20 for viewing.
In-Line Inspection (ILI) Surveys
The ILI tool records the location of the features it detects by associating a
linear measure along the pipeline to each of these features. The linear
measure
or odometer distance is determined by counting the revolutions of onboard
odometer wheels that are rolling along the internal surface of the pipeline as
the
tool travels along the pipeline. Circumferential welds used to connect the
joints
of pipe represent a commonly identifiable feature occurring on a more or less
regular interval along the pipeline. Multiple ILI runs are integrated with
each other
through a weld matching process that takes into consideration that there is
not
necessarily a one to one match given the potential for misidentification of
welds
as well as the possibility the weld tally could have been modified due to pipe
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replacements or cut-outs. This process of integrating the ILI runs through
alignment of the welds minimizes the relative error between the multiple ILI
runs
or data sets by re-zeroing the odometer error at each set of matched welds.
Given the number of welds within an ILI run of 100 km (at least one weld
occurs every 40 to 80 feet depending on the pipe manufacturing process), it is
evident that the ILI weld tally is voluminous. Therefore, the spatial
integration
module 13 of the dataset management program is provided to automate the
spatial integration aspect of the ILI dataset integration process whereas the
centreline fitting program 14 accurately positions the ILI indications in
space
("real world" coordinates). Referring to Fig. 2, the ILI dataset integration
process
begins first by obtaining a tabulation of all ILI indications (shown as
process block
22 in Fig. 2). As the terminology and data organization may vary from ILI run
to
ILI run, such terminology and data organization are formatted so that they are
consistent and can be understood by the dataset management program (shown
as process block 24 in Fig. 2).
The formatted ILI dataset 10 is then imported into the dataset
management program (shown as process block 26 in Fig. 2). At this stage, an
operator will manually specify a name for the ILI dataset, its start point in
the
pipeline, and its run date, if such information is not already provided (shown
as
process block 42 in Fig. 2).
Referring to process block 28 in Fig. 2, if the ILI dataset 10 is the first
ILI
dataset to be imported into the dataset management program, it is spatially
integrated with the 3D pipeline model by executing the centreline fitting
module in
the program (shown as process block 40 in Fig. 2). If ILI datasets are already
present, then this ILI dataset 10 is spatially integrated with the other ILI
datasets
by executing the weld matching module in the program (discussed in detail
below); this is shown as process blocks 30, 32 in Fig. 2.
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The process of automatically spatially integrating multiple ILI datasets
involves weld matching multiple ILI datasets to a single selected ILI dataset
to
produce a single integrated dataset. This single ILI dataset is known as the
"master" and defines the joint lengths and weld positions of the integrated
dataset. The data from the other ILI dataset is then spatially slaved to the
master
dataset by executing the weld matching module 13 of the dataset management
program. The resulting integrated data set in known as an "asset survey". Into
this same asset survey is integrated the excavation data set.
Once the weld-matching process has been completed, a report of the
unmatched welds can be generated (process block 34 in Fig. 2) and a user can
analyse this report.
Spatially Normalizing Asset survey (integrated ILI results) and NDE Datasets
There are two processes through which the NDE data and Asset survey
are spatially integrated. The first involves the manual matching of welds in
the
NDE to welds in the Asset survey. The weld positions relative to each other
(pipe
joint lengths) will be dictated by the NDE data similar to the impact the
master ILI
has on the slave ILI's. Correspondingly, the linear position of points in the
Asset
survey that are between the matched weld pairs are repositioned by
interpolation
between the new relatives welds position.
The second element a weld in the NDE data set driving the creation of a fit
point on the centreline model as its position is now explicitly known.
Correspondingly that weld which is now part of the Asset survey is matched to
the newly created fit point. As a result the Asset survey is refit between the
new
fit points and the nearest upstream and downstream fit points; the Asset
survey is
stretched or shrunk such that the NDE welds matches with new and
corresponding fit point in the centreline model. A corresponding record is
created
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of the new fit point match and the magnitude of the modification required to
the
Asset survey in order to align the points.
Assigning Relevancy to ILI and Excavation Records
Referring to Figs. 3a and 3b there are illustrated in functional block
diagrams representing pipeline data and the management thereof in accordance
with an embodiment of the present invention.
Fig. 3a shows a pipeline model 50 including data from an in-line inspection
(ILI) 52 and excavation (otherwise referred to as "NDE") data 54. The pipeline
model is updated with the excavation data to form a new pipeline model 56,
while
a corresponding section of the ILI data is corrected with regard to physical
reference points and given a status of inactive 58. Pipeline data uses
physical
reference points to correlate one data set to another data set. So for example
ILI
data 52 includes weld positions 62, 64, 66, and 68 and valve locations 70 and
72.
When a section of the pipeline is excavated, physical data is collected as
represented by excavation data 54, which may reveal somewhat different
locations for welds 62 and 64, as represented by weld locations 62' and 64'.
In
order to achieve a pipeline model that accurately reflects the physical state
of the
pipeline being monitored, the section of the pipeline model corresponding to
the
excavation data 54 is effectively replaced by the excavation data to form a
composite pipeline model 56. And the excavation data supersedes the ILI data
58, which is given a super inactive status.
Fig.3b provides an example of how further changes to the physical
pipeline are reflected in the pipeline model 50. Using the same ILI and
excavation data as Fig. 3a, as a result of the excavation a section 82 of the
pipeline is deemed needing replacement. As a result of the replacement section
the pipeline model 56 is updated with physical data for the new section, while
CA 02414644 2002-12-18
both ILI and excavation data corresponding to the cut out section 82 are
archived. Clearly from this simple example, managing data associated with a
pipeline is non-trivial, while the consequences of inaccurately modeling the
pipeline can be enormous.
Referring to Fig.4 illustrated in a flowchart a method of managing
relevancy of data during the NDE dataset integration process of Figs. 3a and
3b.
The method 100 begins with a NDE record as presented by 102. This data is
merged into the pipeline's asset survey as represented by a process block 104.
A first decision block 106 queries whether this record is within the extent of
a
subsequent excavation. If NO, a second decision block 108 asks if the record
is
a coating defect. If YES to the first decision block 106, a status = (super)
inactive
is stored for this record, as represented by 110.
A NO to the second decision block 108 leads to a third decision block 112
that queries whether the record is a pipe defect. A YES to the second decision
block leads to a fourth decision block 114, which queries if the record is
within the
extent of a subsequent recoat. A NO to the third decision block 112 leads to a
store data block 116, status = active.
A YES to the third decision block 112 leads to a fifth decision block 118
querying whether the defect is an "as found" or "as left" defect. "As left"
leads to
a store data status = active 120. "As found" leads to a store data status =
inactive 122. A NO to the fourth decision block 114 leads to a store data
status =
active 124. A YES leads to a store data status = archived 126.
Referring to Fig. 5 illustrates in a flowchart a method of managing
relevancy of data during the process of the ILI dataset integration process of
Figs. 3a and 3b. The method 150 begins with an ILI data record 152. The record
is merged into the asset survey, as represented by a process block 154. The
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method then queries whether this is the first ILI run to be imported against
this
line, as represented by decision block 156. A YES leads to a store data block
158 status = active, while a NO leads to a store data block 160 status =
inactive.
Then the method asks whether rule indicates the record is active, as
represented by a second decision block 162, based on a rule set using record
type and location along the line to define active/inactive records as
represented
by 164. A YES leads to a store data block 164 status = active, which leads to
a
third decision block 166 querying whether this record is within the extent of
a
subsequent excavation, to which a NO leads to a store data block 168 status =
active. While a NO to the second decision block 162 leads to a store data
block
170 status = inactive. This is followed by a fourth decision block 172
querying
whether this record is within the extent of a subsequent excavation, to which
a
NO leads to a store data block 184 status = inactive. A YES to the third
decision
block 166 or the fourth decision block 172 leads to a store data block 176
status
= (super) inactive. This is followed by a fifth decision block 178 querying
whether
rule indicates the record is active based upon input as represented by 180.
Regardless the decision block 178 leads a store data block 182 status =
(super)
inactive.
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