This study presents a dynamic risk modeling strategy for a hydrocarbon sub-surface production system under a gas lift mechanism. A data-driven probabilistic methodology is employed to conduct a risk analysis. The integrated approach comprises a multilayer perceptron (MLP) – artificial neural network (ANN) model and a Bayesian network (BN) technique. The MLP-ANN model performs the production forecast, and the BN model analyzes dynamic risks (the production response) and evaluates the impact of the sand face pressure on risks. The introduced model offers an effective strategy to avoid production failure and to monitor dynamic risks. The dynamic risk analysis yields predictive outcomes at any production time in the well’s production life. It offers field operators an early warning system based on the Bayesian model with prognostic capabilities. The proposed strategy effectively manages production risks and assists in production decision-making, especially in complex production systems.

这项研究提出了在气举机制下烃地下生产系统的动态风险建模策略。采用数据驱动的概率方法进行风险分析。集成方法包括多层感知器（MLP）–人工神经网络（ANN）模型和贝叶斯网络（BN）技术。MLP-ANN模型执行产量预测，而BN模型分析动态风险（生产响应）并评估砂面压力对风险的影响。引入的模型提供了一种有效的策略，可以避免生产失败并监视动态风险。动态风险分析可在油井生产寿命中的任何生产时间产生可预测的结果。它为现场操作员提供了基于贝叶斯模型的具有预警功能的预警系统。