Application of supervised machine learning to predict the enhanced gas recovery by CO2 injection in shale gas reservoirs

Abstract

The technique of Enhanced Gas Recovery by CO₂ injection (CO₂-EGR) into shale reservoirs has brought increasing attention in the recent decade. CO₂-EGR is a complex geophysical process that is controlled by several parameters of shale properties and engineering design. Nevertheless, more challenges arise when simulating and predicting CO₂/CH₄ displacement within the complex pore systems of shales. Therefore, the petroleum industry is in need of developing a cost-effective tool/approach to evaluate the potential of applying CO₂ injection to shale reservoirs. In recent years, machine learning applications have gained enormous interest due to their high-speed performance in handling complex data and efficiently solving practical problems. Thus, this work proposes a solution by developing a supervised machine learning (ML) based model to preliminary evaluate CO₂-EGR efficiency. Data used for this work was drawn across a wide range of simulation sensitivity studies and experimental investigations. In this work, linear regression and artificial neural networks (ANNs) implementations were considered for predicting the incremental enhanced CH₄. Based on the model performance in training and validation sets, our accuracy comparison showed that (ANNs) algorithms gave 15% higher accuracy in predicting the enhanced CH₄ compared to the linear regression model. To ensure the model is more generalizable, the size of hidden layers of ANNs was adjusted to improve the generalization ability of ANNs model. Among ANNs models presented, ANNs of 100 hidden layer size gave the best predictive performance with the coefficient of determination (R²) of 0.78 compared to the linear regression model with R² of 0.68. Our developed ML-based model presents a powerful, reliable and cost-effective tool which can accurately predict the incremental enhanced CH₄ by CO₂ injection in shale gas reservoirs.