INCORPORATION OF HUMAN FACTORS IN RISK ANALYSIS OF OIL AND GAS PIPELINE USING BAYESIAN NETWORK

Abstract

Billions of barrels of oil and gas are consumed around the world daily and these oil and gas are being mainly transported and distributed through pipelines. The pipelines are demonstrably safe and are reliable systems to transport hydrocarbons, owing to the combination of good design, materials, and operating practices. However, if the pipeline fail, it is one of the most frustrating issues as its significant adverse would impact environment and public safety as well as severe economic loss. The objective of this study is to construct a cause and effect relationship framework of pipeline failure due to human factor using Bayesian Network (BN) approach. The potential human factors of the pipeline failure linked to corrosion were identified and categorized into three categories that are maintenance, monitoring, and operational errors. The predictive and diagnosis analyses of the Bayesian Network were performed to find the casual links which cause the failure in the system and make a prediction of the control measures to reduce the rate of the human mistakes. Results revealed that operational error showed a significant effect when the system operates beyond the limits of its design. In conclusion, Bayesian Networks appear to be a solution to build an effective oil and gas pipeline human error management model by providing information about the important human error that needs to be controlled. Thus, this framework may assist the decision maker to decide when and where to take preventive or mitigate measures in the risk management process of a pipeline.