Spatial-Temporal Graph-Level Feature Embedding for Shale Gas Production Forecasting with Well Interference

Deep-learning (DL) models have been used for production forecasting in subsurface engineering applications, but it is often assumed that each well operates independently. Graph convolutional networks (GCNs) can incorporate data from neighboring wells. However, existing spatial-temporal (ST) GCN (ST-GCN) methods are mainly used for autoregressive tasks and face limitations in predicting newly developed wells with no prior history. In this study, we introduce an ST-graph-level feature embedding (GFE) (ST-GFE) method that fully utilizes temporal neighbor interactions for newly developed wells. It enhances forecasting by integrating a non-autoregressive encoder-decoder structure and aggregating the historical data from neighboring wells into a single feature vector. This aggregated vector, merging local and contextual information, contains richer information about the studied region. We evaluate ST-GFE using a data set of 6,605 Montney shale gas wells, incorporating formation properties, fracture parameters, and production history. ST-GFE significantly improves prediction accuracy for newly developed wells compared with the purely temporal models, such as recurrent neural network–based and transformer models. ST-GFE adapts to production changes in adjacent wells, including shut-in and infill drilling activities. Additionally, the ST-GFE model treats each well and its surrounding wells as a graph, enabling batch training and significantly reducing memory usage compared with transductive GCN approaches. Furthermore, the model dynamically updates its forecasts with real-time production data, enhancing precision and relevance. The xperimental results confirm that ST-GFE effectively leverages spatio-temporal dynamics and interactions between adjacent wells, broadening its applicability to various drilling and production scenarios.