Research on Yield Prediction Model Driven by Mechanism and Data Fusion

Existing production forecasting methods often suffer from limited predictive accuracy due to their reliance on single-source data and the insufficient incorporation of physical principles. To address these challenges, this study proposes a mechanism-data fusion production forecasting model that integrates mechanistic model outputs with data-driven learning techniques. The proposed method first establishes a three-phase-separator mechanistic model to generate physics-informed simulation data. Then, a Global-Local Branch Prediction Model is designed to enhance both long-term trend estimation and local feature capture in a production time series. The mechanistic model data are incorporated as constraints into the prediction framework, effectively guiding the learning process and improving forecast accuracy. Experimental results on real-world oilfield data demonstrate that the proposed model outperforms state-of-the-art methods such as Autoformer and DLinear. Specifically, under the mechanism-based approach, the Global-Local Branching Prediction Model reduces MSE by 0.0100, MAE by 0.0501, and RSE by 1.40% compared to Autoformer and achieves improvements of 0.0080 in MSE, 0.0093 in MAE, and 0.48% in RSE over DLinear. The results confirm that integrating mechanistic constraints significantly enhances prediction performance, making the proposed model a robust and technologically superior solution for production forecasting in petroleum engineering.