Prediction of methane adsorption in shale: Classical models and machine learning based models

Abstract

Shale gas contributes significantly to current global energy consumption, and an accurate estimation of geological gas-in-place (GIP) determines an optimal production plan. As the dominant form of storage, adsorbed gas in shale formation is of primary importance to be assessed. This paper summarizes adsorption models into traditional pressure/density dependent isothermal models, pressure and temperature unified model, and machine learning based models. Using a comprehensive experimental dataset, these models are applied to simulate shale gas adsorption under in-situ conditions. Results show that the modified Dubinin-Radushkevich (DR) model provides the optimal performance in traditional isothermal models. Pressure and temperature unified models make a breakthrough in isothermal conditions and can extrapolate the predictions beyond test ranges of temperature. Well-trained machine learning models not only break the limit of the isothermal condition and types of shale formation, but can also provide reasonable extrapolations beyond test ranges of temperature, total organic carbon (TOC), and moisture. Four popular machine learning algorithms are used, which include artificial neural network (ANN), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost). The XGBoost model is found to provide the best results for predicting shale gas adsorption, and it can be conveniently updated for broader applications with more available data. Overall, this paper demonstrates the capability of machine learning for prediction of shale gas adsorption, and the well-trained model can potentially be built into a large numerical frame to optimize production curves of shale gas.