1. Assess Pipeline Susceptibility
Stress Corrosion Cracking:
A Predictive Model to
Assess Pipeline Susceptibility
Janice Jett
The University of Texas
at Dallas
GIS Master’s Project Defense
April 30, 2007

2. Introduction
According to the Pipeline and Hazardous Materials Safety Administration, in 2003, there were over 2.3 million miles of pipelines in the U.S.

3. Introduction: Pipeline Corrosion
In the United States there are millions of people living and working near
pipelines carrying hazardous substances.
Pipelines are subjected to:
Environmental Abuse
External Damage
Coating Disbondment
Inherent Mill Defects
Soil Movements/Instability
Third Party Damage
As pipelines age, their risk of catastrophic failure increases and peoples
lives may be put at risk.
The focus of this project is on Stress Corrosion Cracking (SCC).
Stress Corrosion Cracking is a very specific and serious form of
environmental corrosion that can cause pipeline failure.

4. Introduction: Pipeline Protection
Two types of pipeline protection currently used
External coating
Cathodic protection
External coating systems place a barrier between the soil environment and the pipe
surface.
Cathodic protection is a method of preventing the
corrosion of metals by passing an electric current
through an electrolyte to the metal surface.
The flow of electricity opposes the flow of electrons,
thus protecting the metal.
Unfortunately, external coatings and cathodic protection systems deteriorate
over time and may not be maintained properly.
CATHODIC PROTECTION

5. Introduction: Pipeline Inspection
Corrosion within a pipeline is found with In-Line Inspection (ILI) tools,
also known as smart or intelligent pigs.
Pigs travel throughout the length of a pipeline driven by product flow.
A smart pig has the capability of identifying metal loss, mechanical defects
and corrosion in the pipe wall.
ILI tools are 3.0 to 5.5 m (10 to 18 ft) in length.
Many Pipelines are not “piggable”.
Valves that do not open fully
Tight-radius bends
Multiple wall thickness
Different pipe diameters

6. Stress Corrosion Cracking
Project Objective: General
Use GIS to design a Stress Corrosion Cracking prediction model, based on
pigable lines, so that SCC can be predicted on pipelines that are not “piggable”. + +
Stress Corrosion Cracking =

7. Large-Diameter, High-Pressure Transmission Gas Pipeline
Background on SCC
What conditions causes Stress Corrosion Cracking to form?
Coating Conditions and Cathodic Protection Levels
Operating and Residual Stresses
Terrain Conditions - such as soil types, drainage and topography
What are the characteristics of Stress Corrosion Cracking ?
Small cracks develop on the outside surface of the buried pipeline.
Initially not visible to the eye.
Can exist on pipelines for many years without causing problems.
Eventually the pipeline will fail and will either leak or rupture.
SCC Colony
Large-Diameter, High-Pressure Transmission Gas Pipeline
Source: CEPA (1996).
Taken By R.J. Eiber

8. Background on SCC
What are the different types of Stress Corrosion Cracking?
Near-Neutral pH SCC
High pH SCC
What are the differences between High pH and Near-Neutral pH SCC crack growth?
High pH SCC
Intergranular cracking
Cracks grow around or between the grains in the steel.
Source: CEPA (1996).
Near-Neutral pH SCC
Transgranular cracking
Cracks follow a path across or through the grains.
The side walls of the cracks corrode.
Cracks appear much wider than high pH SCC cracks.
Source: CEPA (1996).

9. Project Objective: Specific
Identify and rank areas along the pipeline system that are the
most likely to have SCC based on various factors known to
contribute to SCC.
The age of the pipeline is greater than 10 years
The coating type is not Fusion Bonded Epoxy (FBE)
Operating Temperature
Operating temperature exceeds 38ºC (100º F) - High ph SCC
Cathodic Protection (CP)
-600 to -750 mV (Cu/CuSO4) copper/copper sulfate - High pH SCC
-760 to -790 mV (Cu/CuSO4) copper/copper sulfate – Near Neutral pH SCC
The operating stress exceeds 60% of specified minimum yield strength (SMYS)
The segment is less than 32 km (20mi.) downstream from a compressor station. pH
Range of 9 to 11 - High pH SCC
Range of 6 to 8 – Near Neutral pH SCC
Terrain conditions (soil type, drainage and topography).

10. Project Methodology: Decision Trees Model FEB- Fusion Bond Epoxy
SMYS- Specified Minimum Yield Strength
CP– Cathodic Protection

11. Table 1 Polyethylene Tape Coated
Project Methodology:
Decision Trees Model
Table 1 Polyethylene Tape Coated
Table 2 Asphalt Coated
Canadian Energy Pipeline Association (CEPA)

12. Literature Review
Public Inquiry Concerning Stress Corrosion Cracking on Canadian
Oil and Gas Pipelines - National Energy Board, 1996
The purpose of this study:
Address factors that cause near-neutral pH SCC, the predominate type
experienced in Canada - high pH SCC is briefly addressed.
Result:
Determines factors that cause SCC
Defines the different characteristics of high pH and near-neutral pH SCC in
pipelines
Identifies 7 combinations of soil texture, drainage, and topography that were
associated with SCC detected in Tape Coated pipelines.
Identifies 4 combinations of soil texture, drainage, and topography that were
associated with SCC detected in the Asphalt Coated pipelines
There was no mention of GIS in this study; however, it provided substantial insight into what SCC is, how it is formed and what factors cause it to occur.

13. Literature Review
Development of SCC Susceptibility Model Using Decision
Tree Approach - NACE International CORROSION 2005
The purpose of this study:
Introduces a data mining methodology, and decision tree approach, for
identification of the correlation between the presence of SCC and
environmental loading conditions.
Result:
There is no SCC occurring when coating condition is excellent. The SCC
susceptibility is high when coating conditions and drainages are poor, and
CP is low. While for good coating conditions the required CP for SCC
presence is high.
When the depth of cover of soil is less than 1.5m and drainages and coating
condition are fair the SCC susceptibility is high.
The SCC probability is strongly associated with corrosion feature linearity.
There was also no mention of GIS in this study; however, this paper gave substantial useful information on the decision tree method

14. Literature Review
Development of SCC Susceptibility Model Using Decision Tree
Approach - continued

15. Data Sources Pipeline Features
I have been given access to major oil and gas company’s data for use in this
project. Not all data used in this study is accurate due to confidentiality agreements.
Soil Data
Geospatial Data Gateway
Raster Imagery
Raster imagery used in this project was downloaded from The Geospatial Data
Gateway.
High Consequence Data (HCA)
PHMSA National Pipeline Mapping System
Background Layers
ArcGIS 9 ESRI Data and Maps 2004
Media Kit

16. Software and Data Sources
ESRI Software – 9.1
ArcMap
ArcCatalog
Eagle Information Mapping
Data Calibrator
SmartDraw
Suite Edition
Database:
PODS - Oracle
ArcSDE
Procedural Language:
SQL – PLSQL Developer
Microsoft:
Access
Excel
PowerPoint
Word

17. Project Study Area

18. Project Methodology: Overview
Review background information
of specific pipelines.
Age
Wall Thickness
Wall Diameter
Grade
Coating Type
Operating Stress
Operating Temperature
Direction of Flow
Compressor Station Location
In-Line Inspection
CP Reading
HCA Area
Gather Terrain information along the system.
Soil surveys
Soil Type
Drainage Ph
Topography
Correlate pipeline data with the
terrain conditions.
Use Decision Tree Method to cross-reference
and rank pipeline data with the “significant
terrain conditions” known to promote SCC.
Risk Rank Class 1 - 4
Validation model using know ILI data and
known SCC locations.
Calibrate Data
Close Interval Survey (CIS)
In-Line Inspection (ILI)

19. Project Methodology: Preprocessing
Review Pipeline Data: Merge Data
External Coating
Pipe segment
Age
Wall Thickness
Wall Diameter
Grade
Coating Type
Operating Stress
Operating Temperature
Direction of Flow
Coating Type

20. Project Methodology: Preprocessing
Data Calibration
In-Line Inspection and Close Interval Survey data was calibrated using accurate
valves, casings and vent locations, and verified with aerial photography

21. Project Methodology: Preprocessing
Gather terrain information along the pipeline
SSURGO Soil data
Spatial Data
Soil lines: vector polygon format
Mapping scale: 1:24,000
Attribute Data
MS Access (.mdb) database
Approx. 50 tables
One-to-many relationships
Preprocessing
Export data to Excel.
Made edits in Excel.
Export data as DBF 4 (dBASE IV)
Join tables with the shapefiles using “MUKEY” field.

22. Project Methodology: Preprocessing
Correlate pipeline data with terrain conditions
Used the Intersect tool to combine pipeline data with SSURGO Soil data.

23. Analysis Summary: Low Chance of SCC Analysis
Create a buffer that is within 32 km (20mi.) downstream from compressor
stations.
Clip Pipeline and HCA Area to buffer.
Select by Attribute - Pipeline Clip

24. Analysis Summary Class 1 – Not within HCA Class 3 – Within HCA Class 3

25. Project Methodology: Decision Trees Model FEB- Fusion Bond Epoxy
SMYS- Specified Minimum Yield Strength
CP– Cathodic Protection

26. Table 1 Polyethylene Tape Coated
Project Methodology:
Decision Trees Model
Table 1 Polyethylene Tape Coated
Table 2 Asphalt Coated
Canadian Energy Pipeline Association (CEPA)

27. Analysis Summary: High Chance of SCC Rank
Based on Decision Tree, there is no Near Neutral SCC associated with
Asphalt Coating in the study area.
SCC associated with Polyethylene
Tape Coating
Class 4

28. Results and Discussion
Model Validation
Corrosion has been previously found in the same area.

29. Results and Discussion
Model Validation
No Corrosion has previously been found in this area

30. Results and Discussion
Model Validation
Corrosion has been previously found in the same area.

31. Results and Discussion
Model Validation
Historical Rupture of the Pipeline.

32. Results and Discussion
Model Validation
Existing ILI data and known corrosion locations.

33. Results and Discussion
Model Validation
Existing ILI data and known corrosion locations.

34. Conclusions: Assessments
The SCC Model predicted corrosion in the same general area as historical corrosion.
Data from the ILI runs do not coincide with model prediction.
There are many factors that cause Stress Corrosion Cracking, it is virtually
impossible to determine if SCC will occur by looking at each individual
factor separately.
The Decision Tree Method incorporated with GIS gives you the capability
of combining all known SCC causing factors together with HCA data to determine
if SCC could occur and if it is a possible threat people and or the community.

35. Conclusions: Contributions
New contributions to Geographic Information Science:
Developed a new method for predicting potential Stress Corrosion Cracking
within pipelines
Designed a SCC decision tree model that can be used and modified within the
GIS environment to identify areas of pipeline susceptible to SCC
Encountered challenges and identified solutions to assist future researchers

36. Conclusions: Further Research
Secondary SCC Risk Assessment
The secondary risk assessment would need to be conducted on all operational
pipelines within the company.
This would consist of performing a thorough data analysis to identify specific high
SCC risk locations along the pipeline for exploratory excavations.
Excavation, Inspection and Repairs
Physical inspection of the pipe would be needed in order to determine
is SCC exists.
If SCC exists, replacement of the pipe and coating would be needed.

37. Bibliography
Michael Baker Jr., Inc. January 2005 Integrity Management Program Stress
Corrosion Cracking Study FINAL REPORT 2005
NACE Standard RP Stress Corrosion Cracking (SCC) Direct
Assessment Methodology. NACE International.
NACE External Stress Corrosion Cracking of Underground Pipelines. NACE
International. Publication October.
NEB Stress Corrosion Cracking on Canadian Oil and Gas Pipelines.
Report of the Inquiry. National Energy Board. MH December.
Office of Pipeline Safety, Washington, D.C. (website).
Pipeline and Hazardous Materials Safety Administration (website)
Pubellier, Cindy A GIS for 3D Pipeline Management
ESRI International User Conference. Paper No. 1105
Uhlig’s Corrosion Handbook, 2nd Edition, Revie,R.W. editor, John
Wiley and Sons, New York, 2000.

38. Bibliography ANSI/ASME B31.8S 2001, “Managing System Integrity of
Gas Pipelines” (New York, NY: ASME).
Beavers, J.A. and W.V. Harper Stress Corrosion Cracking Prediction Model.
NACE International CORROSION Paper
CEPA CEPA Stress Corrosion Cracking Database: First Trending Report.
Submitted to Canadian National Energy Board. January 1998.
Feil,W. and Gao, M. and Gu, B and Kania R Development of SCC Susceptibility Model Using Decision Tree Approach. NACE International CORROSION 2005.
Paper
Hall, R.J. and M.C. McMahon Stress Corrosion Cracking Study. General
Physics Corporation for U.S. Department of Transportation, Research and
Special Programs Administration, Office of Pipeline Safety. Report No. DTRS56-
96-C May.
J.R. Quinlan, “Induction of Decision Trees,” Machine Learning,
Vol.1, pp , 1986.

39. Questions
and
Answers