On the prediction of methane adsorption in shale using grey wolf optimizer support vector machine approach

With the advancement of technology, gas shales have become one of the most prominent energy sources all over the world. Therefore, estimating the amount of adsorbed gas in shale resources is necessary for the technical and economic foresight of the production operations. This paper presents a novel machine learning method called grey wolf optimizer support vector machine (GWO-SVM) to predict adsorbed gas. For this purpose, a data set containing temperature, pressure, total organic carbon (TOC), and humidity has been collected from several sources, and the GWO-SVM model was created based on it. The results show that this model has R-squared and root mean square error equal to 0.982 and 0.08, respectively. Also, the results ensure that the proposed model gives an excellent prediction of the amount of adsorbed gas compared to previously proposed models. Besides, according to the sensitivity analysis, among the input parameters, humidity has the highest effect on gas adsorption.(c) 2021 Southwest Petroleum University. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

This study introduces a novel machine learning method (GWO-SVM) to predict adsorbed gas in shale resources. The results show that the model performs excellently and humidity has the greatest impact on gas adsorption.