Safety analysis of offshore decommissioning operation through Bayesian network

Decommissioning of offshore platforms is becoming increasingly popular. The removal of these heavy steel structures is characterised by high risks that may compromise personnel safety and loss of assets. The removal operation relies on dedicated barges and heavy lift vessels that may descent or capsize because of mechanical or structural failure. The knowledge of associated hazards is driven by experience and failure data are often obtained empirically through analogous operations, which further introduces uncertainty to the risk analysis. This paper proposes an integrated safety analysis approach for conducting a decommissioning risk analysis of offshore installations. The approach incorporates hierarchical Bayesian analysis (HBA) with Bayesian network (BN) to assess the accident causations leading to futile decommissioning operation. First, the overall system failure of a lifting vessel was reviewed with an emphasis on where safety issues arise. In addition, the failure data obtained from expert judgements were aggregated through statistical distribution based on HBA. The aggregated failure data are then used to conduct dynamic safety analysis using BN, to assess and evaluate the risks of offshore jacket removal operations. The accident model is illustrated with a case study from Brent Alpha decommissioning technical document to demonstrate the capability of incorporating HBA with BN to conduct a risk analysis

Safety analysis of plugging and abandonment of oil and gas wells in uncertain conditions with limited data

Well plugging and abandonment are necessitated to ensure safe closure of a non-producing offshore asset. Little or no condition monitoring is done after the abandonment operation, and data are often unavailable to analyze the risks of potential leakage. It is therefore essential to capture all inherent and evolving hazards associated with this activity before its implementation. The current probabilistic risk analysis approaches such as fault tree, event tree and bowtie though able to model potential leak scenarios; these approaches have limited capabilities to handle evolving well conditions and data unavailability. Many of the barriers of an abandoned well deteriorates over time and are dependent on external conditions, making it necessary to consider advanced approaches to model potential leakage risk. This paper presents a Bayesian network-based model for well plugging and abandonment. The proposed model able to handle evolving conditions of the barriers, their failure dependence and, also uncertainty in the data. The model uses advanced logic conditions such as Noisy-OR and leaky Noisy-OR to define the condition and data dependency. The proposed model is explained and tested on a case study from the Elgin platform's well plugging and abandonment failure

Hierarchical Bayesian model for failure analysis of offshore wells during decommissioning and abandonment processes

Risk analysis of offshore wells decommissioning, and abandonment processes is challenging due to limited life-cycle information of the well, and failure data of safety barriers in place. To this end, it is essential to capture and implement the variability associated with the sparse data for conducting risk analysis with considerable confidence level. The hierarchical Bayesian analysis provides a viable alternative to address the uncertainty of the data through aggregation for each causation. Bayesian network, through its robust computation engine, is used to define dependence of causations and uses Bayes' theorem to update the analysis as new information becomes available. In addition, the Bayesian network helps to represent complex dependencies among causations through appropriate relaxation strategy to minimize uncertainty in the data, link parameter of interest, and overall accident scenario modelling. This paper presents the integration of Hierarchical Bayesian model with a Bayesian network to conduct the risk analysis of well decommissioning and abandonment processes. The proposed methodology is illustrated using a well plugging and abandonment operational failure reported by the Department of Mineral Management Service (MMS). The results demonstrate the potential of the proposed approach as a robust means to study complex well decommissioning activities

Marine accident learning with fuzzy cognitive maps (MALFCMs) and Bayesian networks

Addressing safety is considered a priority starting from the design stage of any vessel until end-of-life. However, despite all safety measures developed, accidents are still occurring. This is a consequence of the complex nature of shipping accidents where too many factors are involved including human factors. Therefore, there is a need for a practical method, which can identify the importance weightings for each contributing factor involved in accidents. As a result, by identifying the importance weightings for each factor, risk assessments can be informed, and risk control options can be developed and implemented more effectively. To this end, Marine Accident Learning with Fuzzy Cognitive Maps (MALFCM) approach incorporated with Bayesian networks (BNs) is suggested and applied in this study. The MALFCM approach is based on the concept and principles of fuzzy cognitive maps (FCMs) to represent the interrelations amongst accident contributor factors. Thus, MALFCM allows identifying the importance weightings for each factor involved in an accident, which can serve as prior failure probabilities within BNs. Hence, in this study, a specific accident will be investigated with the proposed MALFCM approach