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A DBN-based risk assessment model for prediction and diagnosis of offshore drilling incidents

机译：基于DBN的风险评估模型，用于预测和诊断海上钻井事故

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Drilling operations of offshore oil and gas fields are characterized by high technical complexity, high risks, and high costs, since they are always in harsh environments with complicated geological factors. Lost circulation or well "kick" is a typically hazardous event that may occur while drilling wells and it also may develop into a blowout accident without being well handled. It is necessary to identify and analyze the root causes of these events and their consequences, in order to prevent serious accidents from happening. In a drilling operation, the risk of blowout may change with time, depending on the operation stage, and such kind of dynamics should be captured in risk assessment. This paper presents an approach for determining the conditional probabilities of hazardous events and their consequences. The approach includes models that take into account the influence of degradation and (if applicable) new real-time information which represents the changing model parameters (such as state change of mud density). Such an approach is based on the Dynamic Bayesian Network (DBN) theory and then incorporates additional nodes to address the model uncertainties and parameter uncertainties. In addition, the effect of equipment degradation, which had been ignored in the existing researches, also is considered for modeling. Given that a hazardous event has occurred, this presented model can be used to predict the risk evolution, as well to reason its root causes during offshore drilling operation. A bowtie model is established to link the potential incident scenarios with the pressure regimes and formation load capacity, and then the model is translated into a DBN. DBN inference is adapted to perform prediction and diagnosis for dynamic risk assessment, and then a sensitivity analysis is carried out to find the relative importance of each root cause. A case study with focusing on lost circulation during three drilling scenarios is adapted to illustrate the feasibility of the proposed approach. (C) 2016 Elsevier B.V. All rights reserved.
机译：海上油气田的钻井作业技术复杂，风险高，成本高，因为它们总是处在具有复杂地质因素的恶劣环境中。井漏或井眼“跳动”是典型的危险事件，可能会在钻井时发生，并且如果不加以妥善处理，还可能发展为井喷事故。为了防止发生严重事故，有必要识别和分析这些事件的根本原因及其后果。在钻井作业中，井喷风险可能随时间变化，具体取决于作业阶段，因此应在风险评估中记录此类动态变化。本文提出了一种确定危险事件的条件概率及其后果的方法。该方法包括考虑退化影响的模型，以及（如适用）表示变化的模型参数（例如泥浆密度的状态变化）的新实时信息。这种方法基于动态贝叶斯网络（DBN）理论，然后合并其他节点以解决模型不确定性和参数不确定性。此外，还考虑了在现有研究中忽略的设备退化的影响，以进行建模。如果发生了危险事件，则可以使用该模型来预测风险演变，并在海上钻井作业中推断其根本原因。建立领结模型以将潜在的事件场景与压力状况和地层载荷能力联系起来，然后将该模型转换为DBN。 DBN推理适用于执行预测和诊断以进行动态风险评估，然后进行敏感性分析以找到每个根本原因的相对重要性。以三个钻井方案中的漏失为重点的案例研究被改编以说明该方法的可行性。 （C）2016 Elsevier B.V.保留所有权利。

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