WB-SatNet: Water-Balance-Guided, Production-History-Conditioned Reconstruction of Water-Saturation Fields

Full-field water saturation is central to waterflood surveillance but cannot be observed continuously across a reservoir, whereas well histories provide sparse dynamic evidence. We formulate target-time saturation reconstruction as a mapping from static geology, well locations, scheduled controls, simulated multi-well production responses, and development time to a two-dimensional saturation field. The water-balance-guided saturation network (WB-SatNet) combines a U-Net spatial pathway, a fixed-order gated recurrent unit (GRU) history encoder, explicit time conditioning, and a closed-boundary water-storage consistency term. Experiments used 400 geological realizations, 800 simulation runs, six target times, realization-wise train/validation/test splitting, and three random seeds. On the held-out test set, WB-SatNet achieved a mean absolute error (MAE) of 0.01175, a coefficient of determination (R2) of 0.98145, a structural similarity index measure (SSIM) of 0.99177, a flooded-area intersection over union (IoU) of 0.95523, and a global storage-consistency error of 0.00944. Its mean MAE was 6.31% lower than that of TCN-U-Net, the strongest temporal convolutional network baseline. Target-time, component-ablation, flooded-area, storage-consistency, history-window, noise, and operating-regime analyses support a monitoring-oriented interpretation. These results indicate that WB-SatNet provides an effective framework for production-history-conditioned water-saturation reconstruction and waterflood state monitoring in the investigated setting.