Prediction of Failure Rate in Long Distance Oil and Gas Pipelines Using Soft Computing Techniques

Abstract

Long distance oil and gas pipelines are the major transporters of crude oil and other petroleum products which are highly expensive and earning millions of dollars’ income. Considered that they are the secure way of transporting those products pipelines fail leaving catastrophic consequences behind. The aim of this study was to build a fuzzy-based model to forecast the type of failure in the future utilizing the historical data of the pipeline records. This paper presents the fuzzy risk analysis method proposed which is the IS appraisal, the LIF evaluation, and the risk analysis. Fuzzy model showed that all inputs and factors have significant influence on the output results. Results obtained using this model exhibits more accurate prediction compared to other methods.