Accurate calculation of adsorbed shale gas content is critical for gas reserve evaluation and development. However, gas adsorption and desorption experiments are expensive and time-consuming, while physics-based models and empirical correlations are unable to accurately capture the adsorption characteristics for different shales. Langmuir adsorption is one of the most commonly used model for calculating the adsorbed gas content in shale gas reservoirs. However, most existing correlations for the Langmuir pressure and Langmuir volume in the model are oversimplified based on limited experimental data points. Thus they are not representative of key geological parameters and are far from accurate for prediction in many cases. We developed a variety of machine learning models that are multivariable controlled to quantify shale gas adsorption.

The data-driven method subdivides into two procedures: data compilation and machine learning regression. Over 700 data entries, composed of reservoir temperature (T, °C), total organic carbon (TOC, wt%), vitrinite reflectance (Ro,%), Langmuir pressure, and Langmuir volume are compiled from shale gas plays mainly in USA, Canada, and China. Data have been consistently curated, then machine learning approaches, including multiple linear regression (MLR), support vector machine (SVM), random forest (RF) and artificial neural network (ANN), have been built, trained and tested by partitioning the data into 75%:25%. For SVM, RF and NN models, 1000 simulations were run and averaged for performance comparison.

MLR identifies non-negligible parameters and general trends for shale gas adsorption. Nonetheless, the correlation coefficients from MLR are far from satisfactory. For Langmuir pressure, RF models fit best to the data entries and the other models follow the order of SVM > ANN > MLR. Particularly, RF models show the highest performance stability with the averaged R-squared value of 0.84 and the maximum of 0.87, indicating a very strong relationship constructed for these 213 data entries. For 485 Langmuir volume data entries, RF models also perform best while the other three regression methods are comparable. It should be noted that altering machine learning model structure and parameters could significantly affect the regression results.

Robust and universal machine learning models for estimating adsorbed shale gas content with high confidence level are established, which not only provide more accurate estimation and broader parameter adaptation than physics-based and empirical models, but also circumvent the high-cost and time-consuming deficiency of experimental measurements. These machine learning models can be used to estimate adsorbed gas content for shale plays with limited experimental measurements. Moreover, they can be incorporated into reservoir simulators to improve the simulation performance.

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