DEVELOPMENT OF ROOT CAUSE FAILURE ANALYSIS FRAMEWORK FOR EQUIPMENT FAILURE ANALYSIS IN OIL AND GAS INDUSTRY

Equipment failure is one of the major issues in oil and gas industries which affect plant operations. These failures could not be eliminated, nevertheless, can be controlled or minimized by using failure analysis approaches. Root Cause Failure Analysis (RCFA) is widely used approach in oil and gas industries to identify potential causes of such failures. This study highlighted that RCFA performed in oil and gas industries are sometimes found ineffective due to various reasons generally related to competency of RCFA team, collection of sufficient/relevant data, data analysis process and root cause identification. Literature identified various approaches of RCFA used in oil and gas industries; however, no standard format is used, leading to the need for formulation of RCFA framework for effective failure investigation. Hence, the main objective of this study is to propose a comprehensive RCFA framework for equipment failure analysis in oil & gas industry. Based on prevailing flaws, literature studies, identified critical factors, survey findings and industrial inputs, the study proposed a comprehensive RCFA framework composed of four key phases namely, failure reporting and investigation, data collection, root cause identification and solution phase. The proposed framework was validated by industrial experts practicing RCFA in oil and gas industry. Inputs received from the RCFA experts indicated that the proposed RCFA framework is practical to be used in oil and gas industry, provide critical steps to accomplish RCFA investigation successfully, offers guidance for collection of sufficient failure data from various sources and focuses on identifying root causes of the failure. A case study of Super-Clause unit-Sulphur Recovery Unit (SRU) in oil and gas industry was also presented to demonstrate the applications of the proposed framework. The proposed framework will enhance the effectiveness of RCFA investigation, improve the process of identifying root causes of failure and add value to control similar failures recurrence.