A methodology for predicting production dynamics of horizontal wells in bottom water reservoirs by integrating data-driven approaches with percolation mechanisms

The production dynamics of horizontal wells in bottom-water sandstone reservoirs are influenced by multiple factors, including reservoir properties and production regimes, posing significant challenges for prediction. Traditional empirical formulas and numerical simulation methods have numerous limitations. In recent years, with the advancement of intelligent oilfield processes, machine learning approaches have been increasingly applied to oilfield production processes. Compared to traditional methods, machine learning offers comprehensiveness and efficiency but lacks physical interpretability and robustness, thereby restricting its credibility and applicability in practical engineering scenarios. To address this issue, firstly, taking a block in the Bohai Oilfield as an example, this paper establishes a seepage mechanism model for horizontal wells in bottom-water sandstone reservoirs considering time-varying permeability. Secondly, improvements are made to the traditional Long ShortTerm Memory (LSTM) neural network structure to construct a neural network framework that supports multi-layer inputs of both static and dynamic data, enhancing the model’s adaptability to various geological conditions, with adjustable weights for static and dynamic parameters. Lastly, a dataset generated from the seepage mechanism model is used, and through dataset fusion, a hybrid prediction model combining data-driven and seepage physics mechanisms for horizontal well production dynamics is established. This data-physics fusion mechanism enhances the interpretability and robustness of the model. Test results indicate that the multi-layer input neural network framework for static and dynamic parameters achieves slightly higher prediction accuracy compared to traditional LSTM networks, while the hybrid model integrating data-driven and seepage mechanism shows a significantly improved prediction accuracy over the multi-layer input neural network framework. Field application also verifies the high efficiency and practicality of this hybrid model in predicting the production dynamics of horizontal wells, providing strong support for optimizing oilfield development plans and establishing production regimes.