Rapid Data-Based Data Adequacy Procedure for Pipeline Integrity Assessment

• US 20090326865A1
• Filed: 06/30/2008
• Published: 12/31/2009
• Est. Priority Date: 06/30/2008
• Status: Active Grant

• Current Assignee: BP Corporation North America Incorporated (BP PLC), BP Exploration Operating Company Limited (BP PLC)
• Original Assignee: BP Corporation North America Incorporated (BP PLC)
• Inventors: Sprague, Kip P., Bailey, Richard S., Ziegel, Eric R.
• Granted Patent: US 9,182,081 B2
• Time in Patent Office: Days
• US Class Current: 702/179
• CPC Class Codes: F17D 5/00 Protection or supervision of installations arrangements for protecting foundations E02D31/00; protecting pipes from damage or internal or external wear F16L57/00, against corrosion and scale F16L58/00; investigation of the fluid-tightness of structures G01M3/00

Abstract

A method and system for evaluating the sample coverage of ultrasonic or radiography (UT/RT) measurements of pipeline wall thickness for statistical validity. A data library contains distributions of in-line inspection (ILI) measurements for other pipelines, calibrated to correspond to UT/RT measurements as needed. The data library for these ILI-measured pipelines also includes statistics generated from Monte Carlo simulation, by way of which various sample coverage levels sample the ILI measurements, for determining whether a measurement exceeds a given threshold or meets another premise related to determining the extreme wall loss measurement for the pipeline. A pipeline with sampled UT/RT measurements is used to identify one or more ILI-measured pipeline datasets that are most similar, and the statistics from those most similar pipeline datasets determine whether the sample coverage of the UT/RT measurements is sufficient to draw conclusions about the extreme value of wall loss in the sampled pipeline.

Claims

1. A method of evaluating the sufficiency of a number of measurements of the integrity of a pipeline, comprising the steps of:

receiving sampled measurement data of pipeline wall thickness loss for the pipeline, the measurement data obtained at a plurality of sample locations along the pipeline;

comparing a distribution of the sampled measurement data with distributions of in-line inspection measurements for a plurality of reference pipeline datasets stored in a data library to select one or more reference pipeline datasets having most similar distributions to the distribution of the sampled measurement data; and

retrieving, from the data library, at least a first statistic for the selected one or more reference pipelines, the first statistic indicating the sample coverage required to accept a first premise regarding an extreme value of wall thickness loss for the pipeline to a specified confidence level.

2. The method of claim 1, wherein the first premise is that the extreme value of wall thickness loss for the pipeline does not exceed a first specific percentage.

3. The method of claim 2, wherein a plurality of statistics are retrieved in the retrieving step;

and wherein a second statistic indicates the sample coverage required to accept a second premise regarding the extreme value of wall thickness for the pipeline to a specified confidence level, the second premise being that the extreme value of wall thickness loss for the pipeline does not exceed a second specific percentage.

4. The method of claim 1, wherein the first premise is that the highest sample measurement of wall thickness loss is within a specific percentage of the maximum wall thickness loss in the pipeline.

5. The method of claim 1, wherein the comparing step comprises:

identifying a maximum wall thickness loss measurement from the received sampled measurement data; and

selecting the plurality of reference pipeline datasets stored in the data library responsive to the identified maximum wall thickness loss of the pipeline.

6. The method of claim 1, wherein the comparing step comprises:

determining relative populations in the distribution of each of the plurality of reference pipeline datasets within a plurality of bins;

determining relative populations in the distribution of sampled measurement data within the plurality of bins;

calculating a figure of merit for each of the plurality of reference pipelines from differences between the populations in the bins of sampled measurement data and the populations in the bins for the reference pipeline; and

selecting one or more reference pipeline datasets responsive to the figure of merit.

7. The method of claim 1, further comprising:

comparing the sample coverage of the sampled measurement data for the pipeline to the required sample coverage indicated by the first statistic.

8. The method of claim 1, further comprising:

generating the data library from in-line inspection measurements for the plurality of reference pipeline datasets, the data library comprising, for each reference pipeline dataset;

a distribution of in-line inspection measurements for the reference pipeline datasets, and

one or more statistics comprising at least the first statistic.

9. The method of claim 8, wherein the step of generating the data library comprises, for each of the plurality of reference pipeline datasets:

retrieving in-line inspection measurement data for the reference pipeline dataset;

generating the distribution of in-line inspection measurements for the reference pipeline dataset;

storing the distribution in the data library in association with the reference pipeline dataset;

at a first sample coverage, randomly sampling the in-line inspection measurement data;

repeating the randomly sampling step for a plurality of repetitions at the first sample coverage;

determining a percentage of the plurality of repetitions that the first premise is satisfied by the random sample;

repeating the randomly sampling step, the repeating step, and the determining step for a plurality of sample coverages; and

storing, in the data library and in association with the reference pipeline dataset, sample coverage statistics corresponding to the percentages from the repeated determining step.

10. The method of claim 9, wherein the step of generating the data library further comprises:

calibrating the retrieved in-line inspection measurement data according to a calibration function between in-line inspection measurements and sampled measurement data.

11. The method of claim 9, wherein the step of generating the data library further comprises:

calibrating the distribution of in-line inspection measurements according to a calibration function between in-line inspection measurements and sampled measurement data.

12. An evaluation system for evaluating measurements of pipeline wall thicknesses, comprising:

a memory resource for storing a data library;

one or more central processing units for executing program instructions; and

program memory, coupled to the central processing unit, for storing a computer program including program instructions that, when executed by the one or more central processing units, is capable of causing the computer system to perform a sequence of operations for evaluating the sufficiency of a number of measurements of the integrity of a pipeline, the sequence of operations comprising;

receiving sampled measurement data of pipeline wall thickness loss for the pipeline, the measurement data obtained at a plurality of sample locations along the pipeline;

comparing a distribution of the sampled measurement data with distributions of in-line inspection measurements for a plurality of reference pipeline datasets stored in a data library to select one or more reference pipeline datasets having most similar distributions to the distribution of the sampled measurement data;

retrieving, from the data library, at least a first statistic for the selected one or more reference pipeline datasets, the first statistic indicating the sample coverage required to accept a first premise regarding an extreme value of wall thickness loss for the pipeline to a specified confidence level; and

comparing the sample coverage of the sampled measurement data for the pipeline to the required sample coverage indicated by the first statistic.

13. The evaluation system of claim 12, further comprising:

a network interface for presenting and receiving communication signals to a network accessible to human users;

wherein the memory resource is accessible to the central processing units via the network interface.

14. The evaluation system of claim 12, wherein the operation of receiving sampled measurement data comprises:

accessing the memory resource.

15. The evaluation system of claim 12, wherein the first premise is that the extreme value of wall thickness loss for the pipeline does not exceed a first specific percentage.

16. The evaluation system of claim 15, wherein a plurality of statistics are retrieved in the retrieving operation;

and wherein a second statistic indicates the sample coverage required to accept a second premise regarding the extreme value of wall thickness for the pipeline to a specified confidence level, the second premise being that the extreme value of wall thickness loss for the pipeline does not exceed a second specific percentage.

17. The evaluation system of claim 12, wherein the first premise is that the highest sample measurement of wall thickness loss is within a specific percentage of the maximum wall thickness loss in the pipeline.

18. The evaluation system of claim 12, wherein the comparing operation comprises:

identifying a maximum wall thickness loss measurement from the received sampled measurement data; and

selecting the plurality of reference pipeline datasets stored in the data library responsive to the identified maximum wall thickness loss of the pipeline.

19. The evaluation system of claim 12, wherein the comparing operation comprises:

determining relative populations in the distribution of each of the plurality of reference pipeline datasets within a plurality of bins;

determining relative populations in the distribution of sampled measurement data within the plurality of bins;

calculating a figure of merit for each of the plurality of reference pipeline datasets from differences between the populations in the bins of sampled measurement data and the populations in the bins for the reference pipeline dataset; and

selecting one or more reference pipeline datasets responsive to the figure of merit.

20. A computer-readable medium storing a computer program that, when executed on a computer system, causes the computer system to perform a sequence of operations for evaluating the sufficiency of a number of measurements of the integrity of a pipeline, the sequence of operations comprising:

receiving sampled measurement data of pipeline wall thickness loss for the pipeline, the measurement data obtained at a plurality of sample locations along the pipeline;

comparing a distribution of the sampled measurement data with distributions of in-line inspection measurements for a plurality of reference pipeline datasets stored in a data library to select one or more reference pipeline datasets having most similar distributions to the distribution of the sampled measurement data; and

retrieving, from the data library, at least a first statistic for the selected one or more reference pipeline datasets, the first statistic indicating the sample coverage required to accept a first premise regarding an extreme value of wall thickness loss for the pipeline to a specified confidence level.

21. The computer-readable medium of claim 20, wherein the first premise is that the extreme value of wall thickness loss for the pipeline does not exceed a first specific percentage.

22. The computer-readable medium of claim 21, wherein a plurality of statistics are retrieved in the retrieving operation;

and wherein a second statistic indicates the sample coverage required to accept a second premise regarding the extreme value of wall thickness for the pipeline to a specified confidence level, the second premise being that the extreme value of wall thickness loss for the pipeline does not exceed a second specific percentage.

23. The computer-readable medium of claim 20, wherein the first premise is that the highest sample measurement of wall thickness loss is within a specific percentage of the maximum wall thickness loss in the pipeline.

24. The computer-readable medium of claim 20, wherein the comparing operation comprises:

identifying a maximum wall thickness loss measurement from the received sampled measurement data; and

selecting the plurality of reference pipeline datasets stored in the data library responsive to the identified maximum wall thickness loss of the pipeline.

25. The computer-readable medium of claim 20, wherein the comparing step comprises:

determining relative populations in the distribution of each of the plurality of reference pipeline datasets within a plurality of bins;

determining relative populations in the distribution of sampled measurement data within the plurality of bins;

calculating a figure of merit for each of the plurality of reference pipeline datasets from differences between the populations in the bins of sampled measurement data and the populations in the bins for the reference pipeline dataset; and

selecting one or more reference pipeline datasets responsive to the figure of merit.

26. The computer-readable medium of claim 20, wherein the sequence of operations further comprises:

comparing the sample coverage of the sampled measurement data for the pipeline to the required sample coverage indicated by the first statistic.

27. The computer-readable medium of claim 20, wherein the sequence of operations further comprises:

generating a data library, for each of the plurality of reference pipeline datasets, by;

retrieving in-line inspection measurement data for the reference pipeline dataset;

generating the distribution of in-line inspection measurements for the reference pipeline dataset;

storing the distribution in the data library in association with the reference pipeline dataset;

at a first sample coverage, randomly sampling the in-line inspection measurement data;

repeating the randomly sampling step for a plurality of repetitions at the first sample coverage;

determining a percentage of the plurality of repetitions that the first premise is satisfied by the random sample;

repeating the randomly sampling step, the repeating step, and the determining step for a plurality of sample coverages; and

storing, in the data library and in association with the reference pipeline dataset, sample coverage statistics corresponding to the percentages from the repeated determining step.

28. The computer-readable medium of claim 27, wherein the operation of generating the data library further comprises:

calibrating the retrieved in-line inspection measurement data according to a calibration function between in-line inspection measurements and sampled measurement data.