Ensemble learning-based prediction model for oil and gas reservoir value in Mahu Sag

The Mahu oilfield, located in the northwestern part of the Junggar Basin in Xinjiang, is one of the largest conglomerate oilfields in the world, with reserves exceeding 1 billion tons. However, poor reservoir properties and strong heterogeneity present significant challenges to the efficient development of oil and gas resources. The key to efficient oil and gas development lies in accurately identifying reservoirs with industrial production value, those with higher productivity and relatively lower development costs. To address the complexity of oil and gas reservoir evaluation in the Mahu Sag of the Junggar Basin, this study proposed an oil and gas reservoir value (OGRV) prediction model based on ensemble learning. The study began with an in-depth analysis of the geological characteristics and exploration status of the Mahu Sag. Then, an ensemble model integrating random forest (RF), long short-term memory (LSTM), and convolutional neural network (CNN) was constructed to improve the accuracy and generalization ability of reservoir evaluation. During implementation, key feature parameters were extracted through systematic preprocessing and feature engineering. With expert knowledge, additional augmented features such as hydrocarbon humidity ratio, hydrocarbon balance ratio, and hydrocarbon characteristic ratio were incorporated. In addition, the sliding window technique was introduced to track the trend of features with depth variations, and the category information of similar wells was used as prior knowledge to enhance the model’s prediction performance. By leveraging the strengths of different models, a precise and robust reservoir evaluation algorithm was developed. It effectively identified reservoirs with industrial value in the Mahu Sag. The model yielded an F1-score of 0.847 0, accuracy of 0.772 5, and area under the receiver operating characteristic (ROC) curve (AUC) of 0.781 0. The study also investigated model interpretability in depth to help geoscientists better understand the model’s decision-making mechanisms and support more informed decision-making in oil and gas exploration and development.