USE OF SURVIVAL MODELING METHODS WITH PIPELINE INSPECTION DATA FOR DETERMINING CAUSAL FACTORS FOR CORROSION UNDER INSULATION

• US 20130304438A1
• Filed: 05/09/2012
• Published: 11/14/2013
• Est. Priority Date: 05/09/2012
• Status: Abandoned Application

• 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, Bailey, Richard S.
• Application Number:
  US13/467,753
• Publication Number:
  US 20130304438A1
• Time in Patent Office: Days
• US Class Current: 703/6
• CPC Class Codes: G06F 2113/14 Pipes
• CPC Class Codes: G06F 2119/04 Ageing analysis or optimisation against ageing
• CPC Class Codes: G06F 30/00 Computer-aided design [CAD]

Abstract

Methods and systems for using survival modeling methods with pipeline inspection data to determine causal factors for corrosion under insulation comprise determining a first corrosion condition of a pipeline joint at a first time; determining a second corrosion condition of the pipeline joint at a second, subsequent time; determining joint attributes, pipeline attributes, and location attributes associated with the pipeline joint; and repeating the process for a plurality of pipeline joints in one or more pipelines. This information is fed into a multiple regression and survival analysis process that determines regression coefficients reflecting the estimated degrees to which various factors contribute to corrosion under insulation. The survival analysis also determines one or more survival models capable of predicting when a given pipeline joint is likely to transition from a first corrosion state to a different second corrosion state, given values for its various attributes.

Claims

1. A computer-implemented method of modeling predicted CUI transition lifetimes in pipeline joints, comprising:

for each pipeline joint in a plurality pipeline joints in one or more pipelines;

determining a first condition of the pipeline joint with respect to CUI at a first time;

determining a second condition of the pipeline joint with respect to CUI at a second time subsequent to the first time; and

determining a plurality of attributes associated with the pipeline joint; and

performing survival analysis modeling using the first condition, the second condition, and the plurality of attributes for the plurality of pipeline joints to derive one or more survival models reflecting one or more predicted lifetimes before a hypothetical input pipeline joint transitions from a first CUI condition to a second CUI condition.

2. The method of claim 1, wherein the plurality of attributes associated with the pipeline joint comprises:

joint attributes reflecting characteristics of the pipeline joint;

pipeline attributes reflecting characteristics of a pipeline or pipeline section in which the pipeline joint resides; and

location attributes reflecting characteristics of a geographical location in which the pipeline joint resides.

4. The method of claim 1, wherein the one or more predicted lifetimes of the hypothetical input pipeline joint predicted by the one or more survival models are based on:

joint attributes reflecting characteristics of the input pipeline joint;

pipeline attributes reflecting characteristics of a pipeline or pipeline section in which the input pipeline joint resides; and

location attributes reflecting characteristics of a geographical location in which the input pipeline joint resides.

5. The method of claim 1, wherein performing survival analysis modeling further comprises:

analyzing the second condition of one or more pipeline joints as right-censored data.

6. The method of claim 1, wherein performing survival analysis modeling further comprises:

analyzing the first condition of one or more pipeline joints as left-censored data.

7. The method of claim 1, further comprising:

performing multiple regression analysis with interval-valued response data using the first condition, the second condition, and the plurality of attributes for the plurality of pipeline joints to derive a regression coefficient associated with each attribute, wherein the regression coefficient reflects a degree to which initiation or advancement of CUI is estimated to be caused by a value of the attribute.

8. The method of claim 1, further comprising:

generating data reflecting one or more conditions and one or more attributes of an actual input pipeline joint as inputs to the one or more survival models to generate one or more predicted lifetimes before the actual input pipeline joint transitions from a first CUI condition to a different second CUI condition.

9. The method of claim 1, wherein the one or more survival models comprise a plurality of survival models reflecting predicted lifetimes before a hypothetical input pipeline joint transitions from one or more first CUI conditions to a plurality of different second CUI conditions.

10. The method of claim 9, further comprising:

generating data reflecting one or more conditions and one or more attributes associated with a plurality of actual input pipeline joints as inputs to the plurality of survival models to generate one or more predicted lifetimes before each actual input pipeline joint transitions from a first CUI condition to one or more different second CUI conditions.

3. The method of claim 3, wherein one or more of the location attributes are derived from GIS data.

11. A system configured to model predicted CUI transition lifetimes in pipeline joints, the system comprising:

a processing system comprising one or more processors; and

a memory system comprising one or more computer-readable media, wherein the computer-readable media have instructions stored thereon that, when executed by the processing system, cause the processing system to perform operations comprising;

for each pipeline joint in a plurality pipeline joints in one or more pipelines;

determining a first condition of the pipeline joint with respect to CUI at a first time;

determining a second condition of the pipeline joint with respect to CUI at a second time subsequent to the first time; and

determining a plurality of attributes associated with the pipeline joint; and

performing survival analysis modeling using the first condition, the second condition, and the plurality of attributes for the plurality of pipeline joints to derive one or more survival models reflecting one or more predicted lifetimes before a hypothetical input pipeline joint transitions from a first CUI condition to a second CUI condition.

12. The system of claim 11, wherein the plurality of attributes associated with the pipeline joint comprises:

joint attributes reflecting characteristics of the pipeline joint;

pipeline attributes reflecting characteristics of a pipeline or pipeline section in which the pipeline joint resides; and

location attributes reflecting characteristics of a geographical location in which the pipeline joint resides.

14. The system of claim 11, wherein the one or more predicted lifetimes of the hypothetical input pipeline joint predicted by the one or more survival models are based on:

joint attributes reflecting characteristics of the input pipeline joint;

pipeline attributes reflecting characteristics of a pipeline or pipeline section in which the input pipeline joint resides; and

location attributes reflecting characteristics of a geographical location in which the input pipeline joint resides.

15. The system of claim 11, wherein performing survival analysis modeling further comprises:

analyzing the second condition of one or more pipeline joints as right-censored data.

16. The system of claim 11, wherein performing survival analysis modeling further comprises:

analyzing the first condition of one or more pipeline joints as left-censored data.

17. The system of claim 11, the operations further comprising:

performing multiple regression analysis with interval-valued response data using the first condition, the second condition, and the plurality of attributes for the plurality of pipeline joints to derive a regression coefficient associated with each attribute, wherein the regression coefficient reflects a degree to which initiation or advancement of CUI is estimated to be caused by a value of the attribute.

18. The system of claim 11, the operations further comprising:

generating data reflecting one or more conditions and one or more attributes of an actual input pipeline joint as inputs to the one or more survival models to generate one or more predicted lifetimes before the actual input pipeline joint transitions from a first CUI condition to a different second CUI condition.

19. The system of claim 11, wherein the one or more survival models comprise a plurality of survival models reflecting predicted lifetimes before a hypothetical input pipeline joint transitions from one or more first CUI conditions to a plurality of different second CUI conditions.

20. The system of claim 19, the operations further comprising:

generating data reflecting one or more conditions and one or more attributes associated with a plurality of actual input pipeline joints as inputs to the plurality of survival models to generate one or more predicted lifetimes before each actual input pipeline joint transitions from a first CUI condition to one or more different second CUI conditions.

13. The system of claim 13, wherein one or more of the location attributes are derived from GIS data.

21. A method of modeling predicted CUI transition intervals in pipeline joints, comprising:

for each pipeline joint in a plurality pipeline joints in one or more pipelines;

inspecting the pipeline joint at a first time to determine a first condition of the pipeline joint with respect to CUI;

inspecting the pipeline joint at a second time subsequent to the first time to determine a second condition of the pipeline joint with respect to CUI; and

determining a plurality of attributes associated with the pipeline joint, the plurality of attributes comprising;

one or more joint attributes selected from among the set of joint configuration attributes, joint orientation attributes, joint shape attributes, joint support attributes, and joint insulation attributes;

one or more pipeline attributes selected from among the set of pipeline shape attributes, adjacent pipeline attributes, pipeline service attributes, pipeline wall thickness attributes, pipeline insulation attributes, pipeline length attributes, joint position attributes, pipeline material strength attributes, pipeline group configuration attributes, and pipeline production date attributes; and

one or more location attributes selected from among the set of ground attributes, wind attributes, proximity to man-made phenomena attributes, and proximity to natural phenomena attributes; and

generating a computer-implemented mathematical model based on inputs comprising the plurality of attributes, wherein the computer-implemented mathematical model comprises one or more survival functions capable of predicting one or more expected time intervals before a hypothetical input pipeline joint transitions from a first CUI condition to a second CUI condition based on attributes associated with the hypothetical input pipeline joint.

22. The method of claim 21, wherein one or more of the location attributes are derived from GIS data.

23. The method of claim 21, wherein generating the computer-implemented mathematical model further comprises:

analyzing the second condition of one or more pipeline joints as right-censored data.

24. The method of claim 21, wherein generating the computer-implemented mathematical model further comprises:

analyzing the first condition of one or more pipeline joints as left-censored data.

25. The method of claim 21, wherein generating the computer-implemented mathematical model further comprises:

performing multiple regression analysis with interval-valued response data using the first condition, the second condition, and the plurality of attributes for the plurality of pipeline joints to derive a regression coefficient associated with each attribute, wherein the regression coefficient reflects a degree to which initiation or advancement of CUI is estimated to be caused by a value of the attribute.

26. The method of claim 21, further comprising:

generating data reflecting one or more conditions and one or more attributes of an actual input pipeline joint as inputs to the computer-implemented mathematical model to generate one or more expected time intervals before the actual input pipeline joint transitions from a first CUI condition to a different second CUI condition.

27. The method of claim 21, wherein the computer-implemented mathematical model comprises a plurality of survival functions capable of predicting one or more expected time intervals before a hypothetical input pipeline joint transitions from one or more first CUI conditions to a plurality of different second CUI conditions.

28. The method of claim 27, further comprising:

generating data reflecting one or more conditions and one or more attributes associated with a plurality of actual input pipeline joints as inputs to the computer-implemented mathematical model to generate one or more expected time intervals before each actual input pipeline joint transitions from a first CUI condition to one or more different second CUI conditions.

29. A system configured to model predicted CUI transition intervals in pipeline joints, the system comprising:

a processing system comprising one or more processors; and

a memory system comprising one or more computer-readable media, wherein the computer-readable media have instructions stored thereon that, when executed by the processing system, cause the processing system to perform operations comprising;

for each pipeline joint in a plurality pipeline joints in one or more pipelines;

determining a first condition of the pipeline joint with respect to CUI at a first time;

determining a second condition of the pipeline joint with respect to CUI at a second time subsequent to the first time; and

determining a plurality of attributes associated with the pipeline joint, the plurality of attributes comprising;

one or more joint attributes selected from among the set of joint configuration attributes, joint orientation attributes, joint shape attributes, joint support attributes, and joint insulation attributes;

one or more pipeline attributes selected from among the set of pipeline shape attributes, adjacent pipeline attributes, pipeline service attributes, pipeline wall thickness attributes, pipeline insulation attributes, pipeline length attributes, joint position attributes, pipeline material strength attributes, pipeline group configuration attributes, and pipeline production date attributes; and

one or more location attributes selected from among the set of ground attributes, wind attributes, proximity to man-made phenomena attributes, and proximity to natural phenomena attributes; and

generating a computer-implemented mathematical model based on inputs comprising the plurality of attributes, wherein the computer-implemented mathematical model comprises one or more survival functions capable of predicting one or more expected time intervals before a hypothetical input pipeline joint transitions from a first CUI condition to a second CUI condition based on attributes associated with the hypothetical input pipeline joint.

30. The system of claim 29, wherein one or more of the location attributes are derived from GIS data.

31. The system of claim 29, wherein generating the computer-implemented mathematical model further comprises:

analyzing the second condition of one or more pipeline joints as right-censored data.

32. The system of claim 29, wherein generating the computer-implemented mathematical model further comprises:

analyzing the first condition of one or more pipeline joints as left-censored data.

33. The system of claim 29, wherein generating the computer-implemented mathematical model further comprises:

performing multiple regression analysis with interval-valued response data using the first condition, the second condition, and the plurality of attributes for the plurality of pipeline joints to derive a regression coefficient associated with each attribute, wherein the regression coefficient reflects a degree to which initiation or advancement of CUI is estimated to be caused by a value of the attribute.

34. The system of claim 29, the operations further comprising:

generating data reflecting one or more conditions and one or more attributes of an actual input pipeline joint as inputs to the computer-implemented mathematical model to generate one or more expected time intervals before the actual input pipeline joint transitions from a first CUI condition to a different second CUI condition.

35. The system of claim 29, wherein the computer-implemented mathematical model comprises a plurality of survival functions capable of predicting one or more expected time intervals before a hypothetical input pipeline joint transitions from one or more first CUI conditions to a plurality of different second CUI conditions.

36. The system of claim 35, the operations further comprising:

generating data reflecting one or more conditions and one or more attributes associated with a plurality of actual input pipeline joints as inputs to the computer-implemented mathematical model to generate one or more expected time intervals before each actual input pipeline joint transitions from a first CUI condition to one or more different second CUI conditions.