Vol. 9 4, 2019 p 444-451


Article name, authors, abstract and keyword


Evaluation of an empirical model to predict maximum pitting corrosion rate in wet sour crude transmission pipelines

Thibault Villette a, Abderrazak Traidia a, Sankara Papavinasam b, Abdelmounam M. El-Sherik a

a Saudi Aramco, PO Box 5000, Dhahran, 31311, Saudi Arabia
b CorrMagnet Consulting Inc., 6 Castlemore Street, Ottawa, Ontario, K2G6K8, Canada

DOI: 10.28999/2541-9595-2019-9-4-444-451

Abstract: A previously published empirical model for the prediction of internal pitting corrosion rate (PCR) was assessed to predict maximum PCR in wet sour crude Saudi Arabian transmission pipelines. Although, the original model did not capture actual PCRs, it succeeded to properly rank the selected pipelines by order of increasing PCRs. Two reasons were identified as the main source of discrepancy, namely, the corrosion mitigation availability and the flow-induced localized corrosion. The original empirical model was modified by introducing two correction factors. These correction factors were elucidated through numerical optimization and were based on delineating the contributions of each correction factor. Introduction of the correction factors significantly increased the agreement between predictions and field measurements.

Keywords: pitting corrosion, corrosion rate, empirical model, wet sour oil, corrosion inhibitor, flow-induced localized corrosion.

The authors gratefully acknowledge The Saudi Arabian Oil Company (Saudi Aramco) for funding and allowing publication of the present work.

For citation:
Villette T., Traidia A., Papavinasam S., El-Sherik A. M. Evaluation of an empirical model to predict maximum pitting corrosion rate in wet sour crude transmission pipelines. Nauka i tehnologii truboprovodnogo transporta nefti i nefteproduktovScience & Technologies: Oil and Oil Products Pipeline Transportation. 2019;9(4):444451.

[1] Waard C. D., Lotz U. Prediction of CO2 corrosion of carbon steel. Proc. NACE Corrosion/93 conf.; paper no. 69. Houston (TX): NACE; 1993.
[2] Srinivasan S., Kane R. D. Prediction of corrosivity of CO2/ H2S production environments. Proc. NACE Corrosion/96 conf.; paper no. 11. Houston (TX): NACE; 1996.
[3] Bonis M., Crolet J. L. Basics of the prediction of the risks of CO2 corrosion in oil and gas wells. Proc. NACE Corrosion/89conf.; paper no. 466. Houston (TX): NACE; 1989.
[4] Nesic S., Nordsveen M., Nyborg R., Stangeland A. A mechanistic model for CO2 corrosion with protective iron carbonate films. Proc. NACE Corrosion/2001 conf.; paper no. 01040. Houston (TX): NACE; 2001.
[5] Anderko A., McKenzie P., Young R. D. Computation of rates of general corrosion using electrochemical and thermodynamic models. Corrosion. 2001;57(3):202213.
[6] Pots B. F. M. Mechanistic models for the prediction of CO2 corrosion rates under multiphase flow conditions. Proc. NACE Corrosion/95 conf.; paper no. 137. Houston (TX): NACE; 1995.
[7] Sharland S. A review of the theoretical modeling of crevice and pitting corrosion. Corrosion Science. 1987;27(3):289323.
[8] Papavinasam S., Doiron A., Revie R. W., Sizov V. A model to predict internal pitting corrosion of oil and gas pipelines. Proc. NACE Corrosion/2007 conf.; paper no. 07658. Houston (TX): NACE; 1995.
[9] Demoz A., Papavinasam S., Omotoso O., Michaelian K., Rewie R. W. Effect of field operational variables on internal pitting corrosion of oil and gas pipelines. Corrosion. 2009;65(11): 741747.
[10] Papavinasam S., Doiron A., Panneerselvam T., Revie R. W. Effect of hydrocarbons on the internal corrosion of oil and gas pipelines. Corrosion. 2007;63(7):704712.
[11] Papavinasam S., Doiron A., Revie R. W. Effect of surface layers on the initiation of internal pitting corrosion in oil and gas pipeline. Corrosion. 2009;65(10):663673.
[12] Landry X., Runstedtler A., Papavinasam S., Place T. D. Computational fluid dynamics study of solids deposition in heavy oil transmission pipeline. Corrosion. 2012;68(10):904912.
[13] Hewitt G. F. Flow Regimes. In: Hetsroni G., editor. Handbook of Multiphase Systems. NY: Hemisphere Publishing Corporation, McGraw-Hill Book Company; 1982.
[14] Sooknah R., Papavinasam S., Revie R. W. Validation of a predictive model for microbiologically influenced corrosion. Proc. NACE Corrosion/2008 conf.; paper no. 08503. Houston (TX): NACE; 2008.
[15] Macdonald D. D., Engelhardt G. R. Predictive modeling of corrosion. In: Richardson J. A., et al. (eds.). Shreirs Corrosion. 2010. Vol. 2. P. 16301679. Amsterdam: Elsevier; 2010.
[16] Velasquez J. C., Caleyo F., Valor A., Hallen J. M. Predictive model for pitting corrosion in buried oil & gas pipelines. Corrosion. 2009;65(5):332342.
[17] Kapusta S. D., Pots B. F. M., Rippon I. J. The application of corrosion prediction models to the design and operations of pipelines. Proc. NACE Corrosion/2004 conf.; paper no. 04633. Houston (TX): NACE; 2004.
[18] Schmitt H. G., Bakalli M. Flow assisted corrosion. In: Richardson J. A., et al. (eds.). Shreirs Corrosion. 2010. Vol. 2. P. 954987. Amsterdam: Elsevier; 2010.
[19] Selection of pipeline flow and internal corrosion models: NACE Technical Report 21410. Houston (TX): NACE; 2015.