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

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.

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.

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

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

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 =

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

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).

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).

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

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

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.

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

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

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 http://soildatamart.nrcs.usda.gov/ Raster Imagery Raster imagery used in this project was downloaded from The Geospatial Data Gateway. http://datagateway.nrcs.usda.gov/NextPage.asp?HitTab=1 High Consequence Data (HCA) PHMSA National Pipeline Mapping System http://www.npms.phmsa.dot.gov/ Background Layers ArcGIS 9 ESRI Data and Maps 2004 Media Kit

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

Project Study Area

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)

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

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

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.

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

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

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

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

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

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

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

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

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

Results and Discussion Model Validation Historical Rupture of the Pipeline.

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

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

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.

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

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.

Bibliography Michael Baker Jr., Inc. January 2005 Integrity Management Program Stress Corrosion Cracking Study FINAL REPORT 2005 NACE Standard RP 0204. 2004. Stress Corrosion Cracking (SCC) Direct Assessment Methodology. NACE International. NACE. 2003. External Stress Corrosion Cracking of Underground Pipelines. NACE International. Publication 35103. October. NEB. 1996. Stress Corrosion Cracking on Canadian Oil and Gas Pipelines. Report of the Inquiry. National Energy Board. MH-2-95. December. Office of Pipeline Safety, Washington, D.C. (website). Pipeline and Hazardous Materials Safety Administration (website) Pubellier, Cindy. 2003. 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.

Bibliography ANSI/ASME B31.8S 2001, “Managing System Integrity of Gas Pipelines” (New York, NY: ASME). Beavers, J.A. and W.V. Harper. 2004. Stress Corrosion Cracking Prediction Model. NACE International CORROSION 2004. Paper 04189. CEPA. 1998. 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. 2005 Development of SCC Susceptibility Model Using Decision Tree Approach. NACE International CORROSION 2005. Paper 05479. Hall, R.J. and M.C. McMahon. 1999. 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-0002-004. May. J.R. Quinlan, “Induction of Decision Trees,” Machine Learning, Vol.1, pp.81- 106, 1986.

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