A Novel Data-Driven Method to Estimate Methane Adsorption Isotherm on Coals Using the Gradient Boosting Decision Tree: A Case Study in the Qinshui Basin, China

Ning Wang,Jiyuan Zhang,Jiaosheng Yang,Xianmin Zhang,Qiujia Hu,Qihong Feng

doi:10.3390/en13205369

Abstract

The accurate determination of methane adsorption isotherms in coals is crucial for both the evaluation of underground coalbed methane (CBM) reserves and design of development strategies for enhancing CBM recovery. However, the experimental measurement of high-pressure methane adsorption isotherms is extremely tedious and time-consuming. This paper proposed the use of an ensemble machine learning (ML) method, namely the gradient boosting decision tree (GBDT), in order to accurately estimate methane adsorption isotherms based on coal properties in the Qinshui basin, China. The GBDT method was trained to correlate the adsorption amount with coal properties (ash, fixed carbon, moisture, vitrinite, and vitrinite reflectance) and experimental conditions (pressure, equilibrium moisture, and temperature). The results show that the estimated adsorption amounts agree well with the experimental ones, which prove the accuracy and robustness of the GBDT method. A comparison of the GBDT with two commonly used ML methods, namely the artificial neural network (ANN) and support vector machine (SVM), confirms the superiority of GBDT in terms of generalization capability and robustness. Furthermore, relative importance scanning and univariate analysis based on the constructed GBDT model were conducted, which showed that the fixed carbon and ash contents are primary factors that significantly affect the adsorption isotherms for the coal samples in this study.

Highlights

As an unconventional hydrocarbon resource, coalbed methane (CBM) has been unlocked for commercial development in the USA, China, Australia, Canada, and India [1]
This paper proposed the use of the gradient boosting decision trees (GBDT) [20,21] in order to accurately estimate adsorption isotherms that are based on coal properties and experimental condition for coal samples acquired from the Qinshui basin
This paper proposed the use of a machine learning algorithm, namely GBDT, in order to estimate

Summary

Introduction

As an unconventional hydrocarbon resource, coalbed methane (CBM) has been unlocked for commercial development in the USA, China, Australia, Canada, and India [1]. Experimental methods that were commonly used for measuring high-pressure methane adsorption isotherms have included the manometric, the gravimetric, and the volumetric methods [7]. These methods differ in the means by which the adsorption amount is determined, they all require indispensable procedures that typically include sample preparation, adsorption equilibrium, and data deduction. Such tedious experimental procedures are time-consuming, but they may result in varying sources of uncertainties. 1), commercial developments of CBMofresources have been ongoing since more than twothan decades

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Results

Conclusion