摘要：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 R2 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 integrating more property distribution maps generally increases model accuracy, considerable improvement (from 0.736 to 0.816) is achieved by solely incorporating porosity maps. ? 2025 The Authors

ISSN号：2949-9097

卷、期、页：卷139

发表日期：2025-07-01

影响因子：0.000000

期刊分区（SCI为中科院分区）：二区

收录情况：SCI（科学引文索引印刷版）,EI（工程索引）,SCIE（科学引文索引网络版）

发表期刊名称：Gas Science and Engineering

参与作者：王牧明,张霞林,王海,陈胜男

第一作者：惠钢

论文类型：期刊论文

论文概要：王牧明,张霞林,王海,惠钢,陈胜男，Integrating geological model via A multimodal machine learning approach in shale gas production forecast，Gas Science and Engineering，2025，卷139

论文题目：Integrating geological model via A multimodal machine learning approach in shale gas production forecast