Application of data mining and machine learning techniques for cause analysis and severity prediction of loss of containment incidents in offshore oil and gas facilities
Open AccessOffshore crude oil and natural gas operations are characterized by high-risk scenarios, particularly those involving loss of containment (LOC) incidents that result in significant hydrocarbon releases, which underscores the necessity for improved predictive and mitigation strategies to elevate safety standards and prevent severe personnel and environmental impacts. An exhaustive analysis conducted to determine the causes and predict the severity of LOC events using data mining and machine learning methodologies revealed that equipment failures prominently emerged as the leading cause of LOC incidents across all categories, emphasizing the critical importance of predictive maintenance and reliable equipment integrity management. Nine predictive models were evaluated, and CatBoost exhibited the best performance with 95% accuracy. Leveraging the superior accuracy and efficiency of CatBoost, the top five features identified were trained to develop a predictive model for LOC severity. This model was transitioned into an easily accessible web application using the Streamlit Python library, offering real-time predictions of LOC severity classifications and their respective probabilities. The research hypotheses were validated through statistical testing, enhancing the robustness and reliability of the developed model. The study culminates with an elaborate discussion of the implications of the findings, emphasizing the model’s potential to aid operators with proactive decision-making, process safety management, and risk mitigation.
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