Francois Ayello, PhD DNV Research & Innovation

1. DEVELOPMENT OF A PROBABILISTIC MODEL FOR STRESS CORROSION CRACKING OF UNDERGROUND PIPELINES USING BAYESIAN NETWORKS: A CONCEPTS. Jain, F. Ayello, J. A. Beavers, N. Sridhar Francois Ayello, PhD DNV Research & Innovation

2. Introduction MARV stands for Multi-Analytic Risk Visualization MARV is a methodology . Although MARV runs from a computer and is accessed from a touch screen, MARV is not a software! MARV utilise different analytic methods in order to assess the probability of failure. A demonstration of MARV was created for onshore pipelines, and threats such as: internal corrosion, SCC, third party damage are quantified. More importantly, uncertainty in the calculations are quantified too. This uncertainty is driving the data collection, when the uncertainty is acceptable, expensive data gathering can stop.

3. Improving models Combine different sets of data and show the simulation results in real time.

4. Improving models See the relationships between models. Inputs and outputs of models are clearly shown on a graphical user interface. No assumptions are hidden. Bayesian model for internal corrosion

5. Improving models Results are never a single number, but a likelihood. While gathering data reduce the uncertainty in the likelihood, a good risk management program reduce the likelihood itself. Likelihood of pipeline thickness evolution over 30 years

6. Why SCC? Near Beardmore, Ontario, Natural gas leak Source: NEB Michigan Kalamazoo oil and sand spill Source : National geographic Integrity Management Programs should include assessment of threat due to SCC

7. Why SCC? • SCC goes undetected till actual failure occurs • SCCDA is not sufficient • Need for a model for risk assessment of different pipeline segments due to SCC • The model should • Predict the probability of failure at a future time • Consider the uncertainty in parameters • Assess the important parameters along different pipeline segments • Should have a feedback mechanism

8. Goals Prioritize! • Prioritize data gathering • Prioritize mitigation

9. What must be prioritized? Prioritization between data gathering and mitigation, is based on what we know of the system. % Probability of failure

10. We must understand the system! We must understand the relationship between factors… Three conditions necessary for SCC (from NEB, Canada report)

11. Mechanism of High pH SCC 1 - Coating disbondment

12. Mechanism of High pH SCC 2 - Electrolyte formation (Wet season) CP High pH solution forms in the wet seasons due to oxygen reduction in the presence of CP (negative potential), carbonate and bicarbonate solution forms

13. Mechanism of High pH SCC 2 - Evaporation (Dry Season) CP Pipe’s high temperature drives evaporation / solution concentration. Cracks initiate and grow in dry seasons when CP is ineffective

14. Layout for the SCC model Carbonate /Bicarbonate concentration Coating Disbondment • Inputs • CP history • Soil type • Terrain • Coating type and history • Pressure • Temperature • Location w.r.t dent, welds, bends • Dimension of dents, welds, bends • Age of coating • Material properties • Surface prep history • Water-table movement • Seasonal variations Potential/ Current Crack initiation and Growth Failure Stress Estimation

15. Failure Probability Carbonate /Bicarbonate concentration Coating Disbondment Potential/ Current Crack initiation and Growth Failure Stress Estimation

16. Failure Probability Thickness Crack Length Crack Depth P (critical) SCC Failure Diameter Operating pressure Fracture toughness

17. Electrolyte concentration Carbonate /Bicarbonate concentration Coating Disbondment Potential/ Current Crack initiation and Growth Failure Stress Estimation

18. Electrolyte concentration Soil Type Topography River or Ground Water Drainage Water Table Variation Soil Chemistry CO2 (soil) Water CO3 / HCO3 concentration Anodic Dissolution Rate Water ingress Coating Disbondment pH Seasonal variations Soil Conductivity Shielding Coating Effective Potential in dry condition Effective Potential Applied CP Mill Scale Evaporative effect Temperature

19. Stress estimation Carbonate /Bicarbonate concentration Coating Disbondment Potential/ Current Crack initiation and Growth Failure Stress Estimation

20. Stress estimation T vs. T when Pipe was Built Soil Settlement Secondary Stress Axial Stress Land Slide Pressure Distance from Compressor Effective Maximum stress Residual Stress Circumferential Stress Dents Welds Compressive Stress Miter & Wrinkle Bends Other Construction Practices Surface Prep

21. Coating disbondment Carbonate /Bicarbonate concentration Coating Disbondment Potential/ Current Crack initiation and Growth Failure Stress Estimation

22. Coating disbondment Seasonal Variations Age of the Coating Soil Stress Topography Coating Disbondment Drainage Weld Position External forces % of swelling clay Soil Type Coating Surface Prep. Coating Type

23. Overall model

24. Model Inputs • The model inputs were probability distributions which could be deduced from the literature data, analytical models, and/or field data

25. Analysis: Effect of distance from the compressor

26. CGR and Crack dimensions Ratio of the depth of the cracks to thickness of the pipe for regions very close to the compressor. Normalized crack growth rate around a compressor.

27. SCC Probability of failure Relative probability of failure as a function of distance from the compressor at different times.

28. Empirical observation of probability Failure history as a function of distance from the compressor (NACE international report 2003)

29. CONCLUSION • A model for High pH SCC was presented that takes into account • The uncertainty in the parameters • Changes in space (location) and time • Good qualitative agreement between empirical and simulation data • Current model is built purely on literature data. • Model is not static, using the model improves the model.

30. Questions