Shale oil production time series forecasting for multi-fractured horizontal wells with optimized artificial neural networks integrating multi-source data

作者：Jie Zhan, Jun Jia, Xifeng Ding, Zhenzihao Zhang, Jiaxiang Cheng, Yike Li, Xianlin Ma, Jiaen Lin, Zhangxin Chen · 发表于：Physics of Fluids · 年份：2025 · DOI：10.1063/5.0260766 · 被引用次数：3 · 研究领域：Reservoir Engineering and Simulation Methods、Oil and Gas Production Techniques、Advanced Data Processing Techniques

Time series forecasting is crucial for guiding capital investment, production enhancement, and optimization in the oil and gas industry. However, conventional data-driven approaches for the production prediction fail to meet the industry's criteria. This paper develops a hybrid model combining bidirectional long short-term memory (Bi-LSTM) or bidirectional gated recurrent unit (Bi-GRU) with multi-layer perceptron (MLP) and self-attention (SA), termed Bi-LSTM/GRU-MLP-SA, to predict shale oil production rates. The SHapley Additive exPlanations (SHAP) method is applied to enhance the model's interpretability. The proposed model architecture consists of five key components: input layers, Bi-LSTM/GRU layers, MLP layers, SA layers, and output layers. The Bi-LSTM/GRU captures temporal dependencies from time-series data, while the MLP captures relevant information from non-sequential data. The SA mechanism allows the model to focus on the most salient parts of the data. Compared to traditional single-technique models like standalone Bi-LSTM/GRU, Bi-LSTM/GRU with SA (Bi-LSTM/GRU-SA), and Bi-LSTM/GRU combined with MLP (Bi-LSTM/GRU-MLP), our Bi-LSTM/GRU-MLP-SA model demonstrates superior performance. Specifically, the Bi-GRU-MLP-SA variant achieved an average root mean square error (RMSE) of 0.2763, a mean absolute error (MAE) of 0.2192, and a mean absolute percentage error (MAPE) of 0.0490, indicating a higher accuracy and stability. In summary, the Bi-GRU-MLP-SA model is the most effe...