Physics-Guided Spatiotemporal Injection–Production Integrated Deep Learning Model for Water-Cut Prediction in Waterflooding Reservoirs
Year: 2026
Type: article
Abstract: Accurate water-cut prediction in mature waterflooding reservoirs is essential for identifying remaining oil distribution, warning of water breakthrough, and optimizing injection–production strategies. However, conventional numerical simulation is computationally intensive, while purely data-driven models often lack physical interpretability and struggle to capture the complex spatiotemporal dynamics of high-water-cut reservoirs. To address this issue, this study proposes a physics-guided spatiotemporal injection–production integrated network (PSTI-Net) for multistep water-cut forecasting. The network distills dynamic physical knowledge from a simulator into a lightweight student model. Three branches are built: a three-dimensional convolutional neural network (3D CNN) extracts spatial heterogeneity from saturation fields; time-of-flight (TOF) constraints capture fluid arrival dynamics near the wellbore; an injection–production module computes correlation-weighted injection rates using geometric distance and time-lagged correlation. The fused features enter a cascaded gated recurrent unit and long short-term memory (GRU-LSTM) for multistep forecasting. Tested on 40 years of production data from 402 wells in a Xinjiang oilfield, the model achieves an R2 of 0.865, outperforming numerical simulation (R2 = 0.754) and baselines like LSTM and informer, with an approximately sixfold speed advantage. Ablation studies show that temporal features contribute most, while spatial and injection–production modules enhance stability. The method captures sudden water breakthroughs and high-permeability channeling reliably. By distilling simulator physics, PSTI-Net provides fast, physically consistent water-cut forecasts without solving flow equations, bridging reliability and efficiency for early warning, optimization, and rapid field-scale decisions.
DOI: 10.1115/1.4072605
Authors: Yu Wang, Hu Jia, Lihui Deng
Language: English
Subfield: Ocean Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7203834231