Study on prediction of reservoir seepage law and optimization of numerical simulation based on deep learning

Accurate prediction of reservoir seepage law is very important for the efficiency and economic benefit of oilfield development. However, the traditional prediction method is limited by the complexity and nonlinear characteristics of the reservoir system, and it is difficult to meet the requirements of high precision and high efficiency. In this paper, a method of reservoir seepage law prediction and numerical simulation optimization based on deep learning is proposed, and the prediction accuracy and calculation efficiency are improved by combining data driving with physical constraints. In this paper, a deep learning model integrating temporal and spatial features is built. ConvLSTM combined with attention mechanism is adopted. Conv3D layer is used to extract reservoir spatial features, LSTM layer captures temporal dynamic characteristics, and attention mechanism is introduced to focus on key areas. The model takes geological parameters and historical production data as inputs, pressure field and water saturation field as outputs, and is optimized by the total loss function combined with data-driven loss and physical law loss. The experimental results show that the model is significantly superior to the traditional numerical simulation method and pure data-driven model in the prediction accuracy of seepage field. The mean absolute error (MAE) of pressure field is reduced by 33.3%, and the MAE of water saturation is reduced by 29.3%. At the same time, the calculation time of single time step is only 17% of the traditional method, which greatly improves the simulation efficiency. This study provides new ideas and methods for the intelligent development of reservoir engineering, and provides strong support for the efficient development and sustainable utilization of oil fields.