A condition assessment model for oil and gas pipelines using integrated simulation and analytic network process

Even though they are safe and economical transportation means of gas and oil products around the world, pipelines can be subject to failure and degradation generating hazardous consequences and irreparable environmental damages. Therefore, gas and oil pipelines need to be effectively monitored and assessed for optimal and safe operation. Many models have been developed in the last decade to predict pipeline failures and conditions. However, most of these models used corrosion features as the sole factor to assess the condition of pipelines. Therefore, the objective of this paper was to develop a condition assessment model of oil and gas pipelines that considers several factors besides corrosion. The proposed model, which uses both analytic network process and Monte Carlo simulation, considers the uncertainty of the factors affecting pipeline condition and the interdependency relationships between them. The performance of the model was tested on an existing offshore gas pipeline in Qatar and was found to be satisfactory. The model will help pipeline operators to assess the condition of oil and gas pipelines and hence prioritise their inspections and rehabilitation requirements.

[1]  Thomas L. Saaty How to Make a Decision: The Analytic Hierarchy Process , 1994 .

[2]  Heng Li,et al.  Contractor selection using the analytic network process , 2004 .

[3]  S. Kumar,et al.  Neuro-fuzzy approaches for pipeline condition assessment , 2007 .

[4]  Pieter H. A. J. M. van Gelder,et al.  Decision Analysis Framework for Risk Management of Crude Oil Pipeline System , 2011, Adv. Decis. Sci..

[5]  Prasanta Kumar Dey,et al.  A risk‐based model for inspection and maintenance of cross‐country petroleum pipeline , 2001 .

[6]  M. Ahammed,et al.  Probabilistic estimation of remaining life of a pipeline in the presence of active corrosion defects , 1998 .

[7]  Peng Zhang,et al.  Long-Distance Oil/Gas Pipeline Failure Rate Prediction Based on Fuzzy Neural Network Model , 2009, 2009 WRI World Congress on Computer Science and Information Engineering.

[8]  Emad Elwakil,et al.  A model for predicting failure of oil pipelines , 2014 .

[9]  Massimo Bertolini,et al.  Oil pipeline spill cause analysis , 2006 .

[10]  Jian-Hua Li,et al.  Predicting corrosion remaining life of underground pipelines with a mechanically-based probabilistic model , 2009 .

[11]  Chiara Bersani,et al.  Accident Occurrance Evaluation in the Pipeline Transport of Dangerous Goods , 2010 .

[12]  Matthias Ehrgott,et al.  Multiple criteria decision analysis: state of the art surveys , 2005 .

[13]  Thomas L. Saaty,et al.  Decision making with dependence and feedback : the analytic network process : the organization and prioritization of complexity , 1996 .

[14]  Maneesh Singh,et al.  A methodology for risk-based inspection planning of oil and gas pipes based on fuzzy logic framework , 2009 .

[15]  Francisco Caleyo,et al.  Probabilistic Condition Assessment of Corroding Pipelines in Mexico , 2003 .

[16]  T. L. Saaty,et al.  Decision making with dependence and feedback , 2001 .

[17]  Anne Jerneck,et al.  Structuring sustainability science , 2011 .

[18]  W. R. Byrd,et al.  A success guide for pipeline integrity management , 2004 .

[19]  Thomas L. Saaty,et al.  How to Make a Decision: The Analytic Hierarchy Process , 1990 .

[20]  Joeseph Sarkis,et al.  A methodological framework for evaluating environmentally conscious manufacturing programs , 1999 .

[21]  W. Kent Muhlbauer,et al.  Pipeline Risk Management Manual , 1992 .

[22]  Norhazilan Md Noor,et al.  The Forecasting Residual Life of Corroding Pipeline based on Semi-Probabilistic Method , 2010 .

[23]  Alex W. Dawotola,et al.  Risk Assessment of Petroleum Pipelines using a com- bined Analytical Hierarchy Process - Fault Tree Analysis (AHP-FTA) , 2009 .

[24]  Tarek Zayed,et al.  Condition Rating Model for Underground Infrastructure Sustainable Water Mains , 2006 .

[25]  Tarek Zayed,et al.  Fuzzy-Based Model for Predicting Failure of Oil Pipelines , 2014 .

[26]  Norhazilan Md Noor,et al.  Deterministic prediction of corroding pipeline remaining strength in marine environment using DNV RP–F101 (Part A) , 2011 .

[27]  Tarek Zayed,et al.  Infrastructure Management : Integrated AHP/ANN Model to Evaluate Municipal Water Mains' Performance , 2008 .

[28]  Mahesh D. Pandey,et al.  Probabilistic Neural Network for Reliability Assessment of Oil and Gas Pipelines , 2002 .

[29]  R. Goodland,et al.  Oil and Gas Pipelines Social and Environmental Impact Assessment: State of the Art , 2006 .