Integrating geological model via A multimodal machine learning approach in shale gas production forecast

作者：Muming Wang, Xialin Zhang, Hai Wang, Gang Hui, Shengnan Chen · 发表于：Gas Science and Engineering · 年份：2025 · DOI：10.1016/j.jgsce.2025.205617 · 被引用次数：12 · 研究领域：Reservoir Engineering and Simulation Methods、Hydraulic Fracturing and Reservoir Analysis、Hydrocarbon exploration and reservoir analysis

Machine learning (ML) has achieved great success in production prediction for unconventional shale gas reservoirs. However, these methods mostly rely on the discrete data collected from the wells, such as drilling, completion, and production data. In this study, a multimodal ML approach is proposed to incorporate not only the aforementioned tabular data but also the geological property distribution maps surrounding the production wells. More specifically, a visual parameterization method was applied to preprocess the unstructured data from a 3D geological model to account for the geology properties near the horizontal wells. A comprehensive architecture for a multimodal model was then developed, assimilating a convolutional neural network (CNN) module, an artificial neural network (ANN) module, and a fusion module. The CNN module was established to process and extract high-level information from the visual dataset, while the ANN module was devised to learn from traditional tabular datasets. A fusion module combined and interacted with the data from both modalities. Results have shown that the proposed multimodal model achieved the highest testing R 2 of 0.828 by integrating the formation maps with tabular datasets, compared to 0.736 from ANN. This is owing to the fact that two wells with similar porosity values measured at well sites could penetrate formations with different qualities along their thousand meters of lateral length. Visual feature analysis indicates that while ...