Machine learning–driven prediction of profile control effect in oil reservoir based on relative production difference

Traditional prediction methods have limited accuracy, and there are varying degrees of deviation between predicted oil production and actual oil production after implementation of the measures. In order to solve this problem, this study introduces a prediction method of profile control effect based on relative production difference (PRD). Initially, Long Short-Term Memory (LSTM) network, Gated Recurrent Unit (GRU) network, and Transformer neural network were used to predict the benchmark production of water flooding. Subsequently, the study identified factors influencing profile control effect as input features and the relative difference between benchmark production of water flooding and post-treatment actual production as the target output. Then three machine learning models were constructed for prediction of profile control effect, namely Adaptive Boosting (AdaBoost), Extreme Gradient Boosting (XGBoost), and Random Forest (RF). Hyperparameters of all models were optimized using the Optuna framework during training process. Finally, the study applied the optimal profile control effect prediction model to predict the profile control effect in S reservoir. The results shows that LSTM performs the best in predicting the benchmark production of water flooding, with R 2 values of 0.9874 and 0.9508 for the training and test sets, respectively. RF performs the best in predicting the profile control effect, with R 2 values of 0.9988 and 0.9995 for the testing and cross validation sets, respectively. In oilfield applications, the test set R 2 of RF exceeds 0.9, which proves that the predicted production after profile control is highly consistent with the actual production, and the model has strong generalization ability.