Abstract This research presents a hybrid model to predict oil production and to provide a dynamic risk profile of the production system. The introduced predictive approach combines a multilayer perceptron (MLP)-artificial neural network (ANN) model with a hybrid connectionist strategy (BN-DBN), which comprises a Bayesian network (BN) model and a dynamic Bayesian network (DBN) model. The proposed hybrid methodology (MLP-BN-DBN) is designed to find the correlations between the input and output data to forecast the desired oil production rate. The MLP model captures the variabilities in the fluid and rock properties, model's uncertainties, and the effects of pressure maintenance on the production process. The BN model uses the 3 σ mathematical rule to promptly signal the arrival of any production rate change and captures the pressure maintenance impact using the early warning source indexes. The DBN model provides a dynamic risk profile of the production system using the observed evidence and reservoir production hyperbolic decline concept. The proposed methodology offers the field operators better opportunity to obtain real time estimate of the likelihood of any impending production loss at any time during production operations. The model exhibits a high capability of oil production prediction with the minimum, average, and maximum percentage errors of 0.01%, 6.57%, and 15.28%, respectively. The developed hybrid model serves as a risk monitoring system. The model is cost-effective and eases the computational burden of history matching processes and bridges the gaps in the existing systems for oilfield development dynamic risk forecast and production predictions. Hence, the proposed methodology offers a multipurpose tool for dynamic risk assessment and for proper reservoir production optimization.

中文翻译：

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油藏生产和相关动态风险的混合智能模型

摘要 本研究提出了一种混合模型来预测石油产量并提供生产系统的动态风险概况。引入的预测方法将多层感知器 (MLP)-人工神经网络 (ANN) 模型与混合连接策略 (BN-DBN) 相结合，其中包括贝叶斯网络 (BN) 模型和动态贝叶斯网络 (DBN) 模型。提议的混合方法 (MLP-BN-DBN) 旨在找到输入和输出数据之间的相关性，以预测所需的石油生产率。MLP 模型捕捉流体和岩石特性的变化、模型的不确定性以及压力维持对生产过程的影响。BN 模型使用 3 σ 数学规则及时发出任何生产率变化的信号，并使用预警源指标捕获压力维持影响。DBN 模型使用观察到的证据和油藏产量双曲线下降概念提供生产系统的动态风险概况。所提出的方法为现场操作员提供了更好的机会，以在生产操作期间的任何时间获得任何即将发生的生产损失的可能性的实时估计。该模型具有较高的产油预测能力，最小、平均和最大百分比误差分别为0.01%、6.57%和15.28%。开发的混合模型用作风险监控系统。该模型具有成本效益，减轻了历史匹配过程的计算负担，并弥补了现有油田开发动态风险预测和生产预测系统的差距。因此，所提出的方法为动态风险评估和适当的油藏生产优化提供了一种多用途工具。