Precise forecasting of the initial productivity rates of infill wells is essential for the effective exploitation of offshore reservoirs characterized by high water-cut. However, conventional reservoir simulation and basic machine learning models often suffer from high computational complexity and low interpretability. This research introduces a hybrid data-driven framework that combines ensemble feature engineering with a random forest model optimized through the crayfish optimization algorithm. The primary controlling factors were identified through a majority voting mechanism involving five feature selection algorithms. Subsequently, the COA was utilized to optimize the parameters of the random forest algorithm to improve its predictive robustness. The proposed EFE-COA-RF model achieves a testing MAE of 6.831 and an R2 of 0.954, outperforming standard machine learning models and other optimization-based variants. The complete training process requires approximately 10.8 min, whereas the prediction time for the testing set is approximately 0.03 s. These results demonstrate that the proposed framework provides an accurate, interpretable, and efficient tool for rapid productivity evaluation in mature offshore oilfields.
Xia et al. (Sat,) studied this question.