Computational Technology Resources - CSETS

Keywords: oil and gas pipelines, condition prediction, maintenance planning.

Abstract

Although pipelines are considered the safest method of transporting oil and gas, they are still subject to deterioration and degradation. It is, therefore, important that pipelines be effectively monitored to optimize their operation and to reduce their failure to within acceptable safe limits. Numerous models have been developed recently to predict pipeline conditions. Nevertheless, most of these models either concentrate on one of the failure types, such as corrosion, to assess the condition of pipelines, or are subjective. The subjectivity in expert opinion models is an imprecise process and may significantly affect the models output. This paper presents the development of innovative asset management tools for sustainable oil and gas pipelines. The factors that affect the performance of oil and gas pipelines were identified and studied. Hence, two models were developed to assess and predict the condition of such pipelines considering these factors. The two models were developed using regression analysis and artificial neural network (ANN) techniques based on historical inspection data collected from three existing offshore oil and gas pipelines. It was found that the performance of both techniques were close to each other, however, the ANN technique provided better results with an average percent validity above 97% when applied to the validation data set. Consequently, another two models were developed to optimise the rehabilitation of oil and gas pipelines using the life cycle cost analysis concept. The models assist in selecting the optimum rehabilitation/repair alternatives for pipelines based on both their condition during their service life and their equivalent uniform annual cost. Finally, a user-friendly automated tool was developed to implement the aforementioned models on the existing pipelines. The developed models in this study can be utilized to generate innovative deterioration models, which accelerate the decision making process and enable decision makers to manage, plan, and bid on their projects.

purchase the full-text of this chapter (price £20)

go to the previous chapter
go to the next chapter
return to the table of contents
return to the book description
purchase this book (price £95 +P&P)

	Computational & Technology Resources an online resource for computational, engineering & technology publications
	not logged in - login
Front Page Browse CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Computational Science, Engineering & Technology Series ISSN 1759-3158 CSETS: 38 COMPUTATIONAL TECHNIQUES FOR CIVIL AND STRUCTURAL ENGINEERING Edited by: J. Kruis, Y. Tsompanakis and B.H.V. Topping Chapter 9 Innovative Asset Management Tools for Sustainable Oil and Gas Pipelines M.S. El-Abbasy¹, T. Zayed¹ and A. Senouci² ¹Building, Civil and Environmental Engineering, Concordia University, Montreal, Canada ²Construction Management, University of Houston, United States of America doi:10.4203/csets.38.9 purchase the full-text of this chapter Full Bibliographic Reference for this chapter M.S. El-Abbasy, T. Zayed, A. Senouci, "Innovative Asset Management Tools for Sustainable Oil and Gas Pipelines", in J. Kruis, Y. Tsompanakis and B.H.V. Topping, (Editors), "Computational Techniques for Civil and Structural Engineering", Saxe-Coburg Publications, Stirlingshire, UK, Chapter 9, pp 199-231, 2015. doi:10.4203/csets.38.9 Keywords: oil and gas pipelines, condition prediction, maintenance planning. Abstract Although pipelines are considered the safest method of transporting oil and gas, they are still subject to deterioration and degradation. It is, therefore, important that pipelines be effectively monitored to optimize their operation and to reduce their failure to within acceptable safe limits. Numerous models have been developed recently to predict pipeline conditions. Nevertheless, most of these models either concentrate on one of the failure types, such as corrosion, to assess the condition of pipelines, or are subjective. The subjectivity in expert opinion models is an imprecise process and may significantly affect the models output. This paper presents the development of innovative asset management tools for sustainable oil and gas pipelines. The factors that affect the performance of oil and gas pipelines were identified and studied. Hence, two models were developed to assess and predict the condition of such pipelines considering these factors. The two models were developed using regression analysis and artificial neural network (ANN) techniques based on historical inspection data collected from three existing offshore oil and gas pipelines. It was found that the performance of both techniques were close to each other, however, the ANN technique provided better results with an average percent validity above 97% when applied to the validation data set. Consequently, another two models were developed to optimise the rehabilitation of oil and gas pipelines using the life cycle cost analysis concept. The models assist in selecting the optimum rehabilitation/repair alternatives for pipelines based on both their condition during their service life and their equivalent uniform annual cost. Finally, a user-friendly automated tool was developed to implement the aforementioned models on the existing pipelines. The developed models in this study can be utilized to generate innovative deterioration models, which accelerate the decision making process and enable decision makers to manage, plan, and bid on their projects. purchase the full-text of this chapter (price £20) go to the previous chapter go to the next chapter return to the table of contents return to the book description purchase this book (price £95 +P&P)
Back to top	©Civil-Comp Limited 2023 - terms & conditions