A hybrid physics-informed machine learning framework for water cut prediction in waterflooding reservoirs

Jian Gai, Wenchao Jiang, Tianzhi Wang, Xu Su, Chi Dong, Erlong Yang, Bo Yang, Xu Lai,
A hybrid physics-informed machine learning framework for water cut prediction in waterflooding reservoirs,
Results in Engineering,
Volume 28,
2025,
107856,
ISSN 2590-1230,
https://doi.org/10.1016/j.rineng.2025.107856.
(https://www.sciencedirect.com/science/article/pii/S2590123025039088)
Abstract: The variation of water cut plays a crucial role in optimizing waterflooding development strategies and delaying production decline. This study proposes a novel approach that combines machine learning with a general water cut prediction model to improve forecasting accuracy and applicability. Based on the data from 140 waterflooding blocks in Daqing Oilfield, a general mathematical model is established to uniformly describe various water cut trends, including convex, sigmoidal, and concave shapes, to meet the development needs of different reservoir types. Furthermore,key features were selected through feature importance analysis and recursive feature elimination. Machine learning models, including Random Forest and LightGBM, were constructed and optimized, with a 70 %/15 %/15 % training/validation/test split and bootstrapping for rigorous evaluation. Finally, a hybrid model was developed by integrating the general water cut prediction model with the best-performing LightGBM model. The predictive performance of the hybrid model was evaluated on the DBGDD block over a historical period (1963–2021) and for a future forecast horizon (2021–2024). It significantly outperforms that of the individual models, achieving R² = 0.994 (95 % CI: 0.984–0.996) and RMSE = 2.46 (95 % CI: 2.25–2.68). The results indicate that the hybrid model not only improves prediction accuracy and stability but also demonstrates strong generalization ability and practical applicability, as further validated in two additional blocks (N2DG and X7DSP). This method provides a precise water cut prediction tool for waterflooding development, enhancing waterflooding efficiency and economic benefits, while offering valuable insights for the integration of physical models with machine learning in other engineering fields.
Keywords: Water cut prediction; Waterflooding development; Machine learning; Hybrid model; Reservoir engineering