Research on the prediction model of gas well reserves using machine learning technology

Current evaluation of production performance data from over 100 active wells in the Songliao Gas Field predominantly relies on manual-based analytical techniques, an approach that proves labor-intensive and operationally inefficient. In terms of well control dynamic reserves and productivity evaluation, the prediction results of wells with long production time and more monitoring data are relatively reliable, but the calculation results of gas wells with short production time or few monitoring data are only for reference. To overcome these operational challenges, this study introduces a machine learning-driven framework for dynamic reserve prediction in gas well systems. The methodology employs ensemble feature selection techniques to systematically determine key controlling parameters governing reservoir performance characteristics. Then, three machine learning methods are used to establish dynamic reserve prediction models. The optimal model is identified through a comparative analysis of the fitting effects of these three models. Implementation of the proposed methodology within the Xushen-1 gas-bearing structure demonstrated through comparative analysis that the Support Vector Machine (SVM) algorithm achieves statistically superior fitting accuracy (R 2 >0.92) compared to conventional reservoir simulation approaches under equivalent geological constraints. The model has strong generalization ability and can efficiently predict dynamic reserves, which can be widely used in the field.