Estimating worst case corrosion in a pipeline

• US 7,941,282 B2
• Filed: 01/07/2009
• Issued: 05/10/2011
• Est. Priority Date: 08/01/2008
• 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), BP Exploration Operating Company Limited (BP PLC)
• Inventors: Sprague, Kip P., Ziegel, Eric R, Bailey, Richard S.
• Primary Examiner(s): Le; John H
• Application Number:
  US12/349,851
• Publication Number:
  US 20100030491A1
• Time in Patent Office: 853 Days
• Field of Search: 702/12, 702/34, 702/35, 702/170, 702/179, 736/02, 376/249, 716/4, 703/7, 703/9
• US Class Current: 702/34
• CPC Class Codes: F17D 1/00 Pipe-line systems conveying articles or materials through a pipe- line by means of a fluid carrier B65G51/00, B65G53/00; liquids B67D; special devices for transferring liquids from bulk storage containers into vehicles or boats or vice versa, e.g. loading or unloading vehicles or portable containers B67D7/00; conveying material which has been excavated by a dredger or soil shifter through a pipe-line E02F7/10; sewer pipe-line systems E03F3/00; thermal insulation of pipe-lines F16L59/00; central heating systems F24D
• 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
• CPC Class Codes: F17D 5/005 of gas pipelines, e.g. alarm
• CPC Class Codes: F17D 5/06 using electric or acoustic means
• CPC Class Codes: G01B 17/02 for measuring thickness
• CPC Class Codes: G01B 21/08 for measuring thickness
• CPC Class Codes: G01N 2291/2634 cylindrical from outside
• CPC Class Codes: G01N 29/043 in the interior, e.g. by shear waves
• CPC Class Codes: G01N 29/0672 by acoustic tomography medical tomography A61B8/13
• CPC Class Codes: G01N 29/4454 Signal recognition, e.g. specific values or portions, signal events, signatures

Abstract

A method and system for estimating the worst case corrosion in a pipeline for which pipeline wall thickness measurements are limited to sampled ultrasonic or radiography (UT/RT) measurements. A data library contains distributions of in-line inspection (IL) measurements for other pipelines, calibrated to correspond to UT/RT measurements as needed. These ILI datasets are randomly sampled multiple times, to obtain multiple sample sets from each ILI dataset. Candidate statistical distributions are evaluated for each sample set to determine which of the candidate statistical distributions most accurately estimates the worst case corrosion measured by ILI. A discriminant function is then derived from sample statistics and pipeline descriptors associated with the sample sets, along with the best candidate statistical distribution for that sample set. Sample statistics and pipeline descriptors for the pipeline with sampled UT/RT measurements are then applied to the discriminant function to determine the best one of the candidate statistical distributions for extreme value estimation, and the worst case corrosion is then determined using that best statistical distribution.

Claims

1. A method of estimating 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;

calculating sample statistics of the sampled measurement data;

applying the calculated sample statistics to a discriminant function, the discriminant function arranged to identify one of a plurality of candidate statistical distributions for accuracy in estimating an extreme value of pipeline wall thickness loss, each of the plurality of candidate statistical distributions based on wall thickness loss measurements of a corresponding one of a plurality of reference pipelines;

operating a computer system to evaluate an extreme value quantile of the identified candidate statistical distribution as fit to the sampled measurement data to obtain an estimate of an extreme value of pipeline wall thickness loss for the pipeline.

2. The method of claim 1, further comprising:

obtaining pipeline descriptors for the pipeline;

and wherein the applying step also applies the pipeline descriptors to the discriminant function.

3. The method of claim 1, wherein the discriminant function comprises a plurality of linear equations, each associated with a candidate statistical distribution.

4. The method of claim 1, further comprising:

deriving the discriminant function from Monte Carlo sampling of inline inspection measurement datasets for the plurality of reference pipelines.

5. The method of claim 4, wherein the deriving step comprises:

identifying an extreme value of pipeline wall thickness loss in each of the plurality of datasets;

for each of the plurality of datasets, at each of a plurality of sample sizes, randomly sampling each of the datasets one or more times, to derive a plurality of reference sample sets;

for each of the reference sample sets;

calculating one or more sample statistics for the sample set;

evaluating an extreme value quantile using each of the plurality of candidate statistical distributions as fit to the sample set, to obtain an estimate of an extreme value of pipeline wall thickness loss;

comparing the evaluated extreme value quantile from each of the plurality of candidate statistical distributions to the extreme value of pipeline wall thickness loss for the dataset from which the reference sample set is taken; and

then, for each of the datasets, identifying one of the candidate statistical distributions for which the evaluated extreme value quantile is closest to the extreme value of pipeline wall thickness loss for the dataset from which the reference sample set is taken; and

for each of the candidate statistical distributions, operating a computer system to derive a discriminant equation using the sample statistics of the reference sample sets for which the candidate statistical distribution is the identified candidate statistical distribution.

6. The method of claim 5, wherein the deriving step further comprises:

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

7. The method of claim 5, further comprising:

for each dataset, obtaining pipeline descriptors for its associated reference pipeline from which its inline inspection measurement data are acquired;

wherein the step of deriving a discriminant equation also uses the pipeline descriptors of the reference pipelines for the dataset from which the reference sample set is taken.

8. The method of claim 7, further comprising:

obtaining pipeline descriptors for the pipeline;

wherein the applying step also applies the pipeline descriptors to the discriminant function.

9. 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 estimating 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;

calculating sample statistics of the sampled measurement data;

retrieving a discriminant function from the data library;

applying the sample statistics to the discriminant function, the discriminant function arranged to identify one of a plurality of candidate statistical distributions for accuracy in estimating an extreme value of pipeline wall thickness loss, each of the plurality of candidate statistical distributions based on wall thickness loss measurements of a corresponding one of a plurality of reference pipelines; and

evaluating an extreme value quantile of the identified candidate statistical distribution as fit to the sampled measurement data to obtain an estimate of an extreme value of pipeline wall thickness loss for the pipeline.

10. The evaluation system of claim 9, further comprising:

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

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

11. The evaluation system of claim 9, wherein the operation of receiving sampled measurement data comprises:

accessing the memory resource.

12. The evaluation system of claim 9, wherein the sequence of operations further comprises:

obtaining pipeline descriptors for the pipeline;

and wherein the pipeline descriptors are also applied to the discriminant function in the applying operation.

13. The evaluation system of claim 9, wherein the discriminant function comprises a plurality of linear equations, each associated with a candidate statistical distribution.

14. The evaluation system of claim 9, wherein the sequence of operations further comprises:

retrieving, from the data library, inline inspection measurement datasets for the plurality of reference pipelines; and

deriving the discriminant function from Monte Carlo sampling of the inline inspection measurement datasets.

15. The evaluation system of claim 14, wherein the operation of deriving the discriminant function comprises:

identifying an extreme value of pipeline wall thickness loss in each of the plurality of datasets;

for each of the plurality of datasets, at each of a plurality of sample sizes, randomly sampling each of the datasets one or more times, to derive a plurality of reference sample sets;

for each of the reference sample sets;

calculating one or more sample statistics for the sample set;

evaluating an extreme value quantile using each of the plurality of candidate statistical distributions as fit to the sample set, to obtain an estimate of an extreme value of pipeline wall thickness loss;

comparing the evaluated extreme value quantile from each of the plurality of candidate statistical distributions to the extreme value of pipeline wall thickness loss for the dataset from which the reference sample set is taken; and

then, for each of the datasets, identifying one of the candidate statistical distributions for which the evaluated extreme value quantile is closest to the extreme value of pipeline wall thickness loss for the dataset from which the reference sample set is taken; and

for each of the candidate statistical distributions, deriving a discriminant equation using the sample statistics of the reference sample sets for which the candidate statistical distribution is the identified candidate statistical distribution.

16. The evaluation system of claim 15, wherein the operation of deriving the discriminant function further comprises:

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

17. The evaluation system of claim 15, wherein the sequence of operations further comprises:

for each dataset, retrieving pipeline descriptors for its associated reference pipeline from which its inline inspection measurement data are acquired;

and wherein the operation of deriving a discriminant equation also uses the pipeline descriptors of the reference pipelines for the dataset from which the reference sample set is taken.

18. The evaluation system of claim 17, wherein the sequence of operations further comprises:

obtaining pipeline descriptors for the pipeline;

and wherein the applying operation also applies the pipeline descriptors to the discriminant function.

19. 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 estimating 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;

calculating sample statistics of the sampled measurement data;

applying the sample statistics to a discriminant function, the discriminant function arranged to identify one of a plurality of candidate statistical distributions for accuracy in estimating an extreme value of pipeline wall thickness loss, each of the plurality of candidate statistical distributions based on wall thickness loss measurements of a corresponding one of a plurality of reference pipelines; and

evaluating an extreme value quantile of the identified candidate statistical distribution as fit to the sampled measurement data to obtain an estimate of an extreme value of pipeline wall thickness loss for the pipeline.

20. The computer-readable medium of claim 19, wherein the sequence of operations further comprises:

obtaining pipeline descriptors for the pipeline;

and wherein the operation of applying the sample statistics to a discriminant function also applies the pipeline descriptors to the discriminant function.

21. The computer-readable medium of claim 19, wherein the discriminant function comprises a plurality of linear equations, each associated with a candidate statistical distribution.

22. The computer-readable medium of claim 19, wherein the sequence of operations further comprises:

deriving the discriminant function from inline inspection measurement datasets for the plurality of reference pipelines by;

identifying an extreme value of pipeline wall thickness loss in each of the plurality of datasets;

for each of the plurality of datasets, at each of a plurality of sample sizes, randomly sampling each of the datasets one or more times, to derive a plurality of reference sample sets;

for each of the reference sample sets;

calculating one or more sample statistics for the sample set;

evaluating an extreme value quantile using each of the plurality of candidate statistical distributions as fit to the sample set, to obtain an estimate of an extreme value of pipeline wall thickness loss;

comparing the evaluated extreme value quantile from each of the plurality of candidate statistical distributions to the extreme value of pipeline wall thickness loss for the dataset from which the reference sample set is taken; and

then, for each of the datasets, identifying one of the candidate statistical distributions for which the evaluated extreme value quantile is closest to the extreme value of pipeline wall thickness loss for the dataset from which the reference sample set is taken; and

for each of the candidate statistical distributions, deriving a discriminant equation using the sample statistics of the reference sample sets for which the candidate statistical distribution is the identified candidate statistical distribution.

23. The computer-readable medium of claim 22, wherein the operation of deriving the discriminant function further comprises:

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

24. The computer-readable medium of claim 22, wherein the sequence of operations further comprises:

for each dataset, obtaining pipeline descriptors for its associated reference pipeline from which its inline inspection measurement data are acquired; and

obtaining pipeline descriptors for the pipeline;

wherein the step of deriving a discriminant equation also uses the pipeline descriptors of the reference pipelines for the dataset from which the reference sample set is taken;

and wherein the applying step also applies the pipeline descriptors to the discriminant function.