This book systematically examines performance evaluation, reliability modeling, and risk analysis for offshore well control equipment, integrating multidisciplinary approaches from petroleum, safety, mechanical, and artificial intelligence engineering. It employs reliability modeling, dynamic Bayesian networks, and machine learning to assess key devices such as subsea safety systems and blowout preventers, with emphasis on lifecycle and risk evaluation. The study addresses practical challenges including imperfect testing, degradation, and time-varying failures, and analyzes equipment behavior under special operational conditions. Providing theoretical tools and research insights, this work supports enhanced safety in deep-sea oil and gas operations. It is intended for undergraduates, graduates, and researchers interested in offshore drilling equipment and risk assessment technologies.
This book systematically examines performance evaluation, reliability modeling, and risk analysis for offshore well control equipment, integrating multidisciplinary approaches from petroleum, safety, mechanical, and artificial intelligence engineering. It employs reliability modeling, dynamic Bayesian networks, and machine learning to assess key devices such as subsea safety systems and blowout preventers, with emphasis on lifecycle and risk evaluation. The study addresses practical challenges including imperfect testing, degradation, and time-varying failures, and analyzes equipment behavior under special operational conditions. Providing theoretical tools and research insights, this work supports enhanced safety in deep-sea oil and gas operations. It is intended for undergraduates, graduates, and researchers interested in offshore drilling equipment and risk assessment technologies.
Shengnan Wu
Well Control Equipment Safety Barriers Reliability Modelling Risk Assessment Performance Assessment Life Prediction Fatigue Failure Testing & Maintenance Strategies Drilling Operations Ocean Engineering Managed Pressure Drilling - MPD