Dynamic risk assessment methodology of operation process for deepwater oil and gas equipment

Deepwater oil and gas equipment is a complex and dynamic system that requires thorough risk assessment during its operation. Existing methods such as fault tree analysis, failure mode analysis, bow-tie analysis, and Markov models have been used to investigate risks related to deepwater equipment installation, fatigue, leaks, and blowouts. However, these approaches often overlook the impact of dynamic changes in oil well operations on the risk factors associated with deepwater equipment accidents. Therefore, there is a need for a comprehensive method that can assess the real-time dynamic safety of deepwater equipment under various failure modes. To address this gap, this work proposes a novel risk assessment method for deepwater equipment that incorporates preventive maintenance strategies and dynamic operating conditions. A dynamic Bayesian networks (DBNs) structure is established for the failure model of deepwater equipment. The fault modes of the subsystems and the dynamic and multi-state characteristics of the faults are considered, and the dynamic fault probabilities are calculated. The degree of failure consequences and the characteristic values of different failure modes are evaluated using the matter element theory. To validate the effectiveness of the proposed method, a study is conducted on the operation process of a subsea blowout preventer. The results demonstrate that dynamic risk assessment for deepwater equipment can be accurately and effectively performed using this method. In conclusion, a risk assessment framework is introduced in the research, which accounts for the preventive maintenance strategy and dynamic operating conditions of deepwater equipment. By combining DBNs and the matter element theory, a comprehensive approach is provided to evaluate the dynamic safety of deepwater equipment under different failure modes. The study on subsea blowout preventers serves as a practical demonstration of the efficacy of the proposed method.

Automated summary

This study proposes a novel risk assessment method for deepwater equipment that incorporates preventive maintenance strategies and dynamic operating conditions. By establishing a dynamic Bayesian network structure, the failure model and dynamic fault probabilities of deepwater equipment are evaluated. The matter element theory is used to assess the failure consequences and characteristic values of different failure modes. The study on subsea blowout preventers demonstrates the efficacy of the proposed method in accurately and effectively performing dynamic risk assessment for deepwater equipment.