Reservoir Production Prediction Based on Improved Graph Attention Network

The fractured-vuggy carbonate reservoir comprises various types of storage and seepage spaces, and is composed of multi-scale dissolution pores and fractures. The frequent changes to working systems make the characteristics of water breakthrough complex, and the production data nonlinear and non-stationary, resulting in great difficulty in real-time prediction. Traditional production forecasting methods only consider temporal correlations, neglecting the spatial correlations between production wells and local geological features. In this paper, adopts a modular design approach that comprehensively considers the spatiotemporal characteristics by abstracting each production well in the unit as a directed graph network node. We establish a graph attention network module based on the connectivity between wells to simulate fluid motion patterns and extract spatial features. To address the autocorrelation characteristics of the production sequences, we use a self-attention mechanism module to capture the temporal dependency relationships between production sequences. Finally, considering the fusion of spatiotemporal features, a gating mechanism is designed to adaptively aggregate spatiotemporal characteristics produced by the previous two modules, enabling dynamic production forecasting. We validate our proposed model using real-world production data from the Tarim Basin in China. Our experimental results demonstrate the superiority of the new model over existing production prediction models in fractured-vuggy carbonate reservoirs.