Implementation of a Combined Fuzzy Controller Model to Enhance Risk Assessment in Oil and Gas Construction Projects

Abstract

The aim of this research is to enhance Oil and Gas (O&G) construction risk assessment using Fuzzy-based Failure Model Effect Analysis (FMEA) through the lens of O&G project managers in the U.S. A mixed-method approach was adopted for data collection, analysis, and processing, including semi-structured interviews with project managers to identify the key risks facing O&G construction projects; a Fuzzy-based FMEA to quantitatively analyse the level of significance of O&G risks; surveys to rank the assessment dimensions of the developed model and their components; and open-ended surveys to validate and verify the assessment model and its outputs, further expanding on the root causes of significant risks based on the assessment outputs, and to propose mitigation strategies for these risks. The research identified 41 risk factors classified under six categories, namely: management, technical and quality, financial and economic, health, safety, environmental, legal, and stakeholders’ risks. In addition, the risk assessment revealed that non-compliance with PPE regulations emerged as the most significant risk factor across all categories of O&G risks. This study offers valuable insights by assisting practitioners in better understanding the significant O&G risks that need to be addressed to ensure the successful execution and completion of O&G projects.

Keywords

• Oil and gas
• control system
• fuzzy set theory
• decision process
• construction industry
• risk assessment