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

• DOI: 10.1016/j.petlm.2021.12.002
• Publication Date: 2021-12-13T17:18:31Z
• Authors: Rahmad Syah; Mohammad Hossein ...; Reza Daneshfar; Hossein Dehdar; Bahram Soltani So...
• Keywords: Shale Gas

With the advancement of technology, gas shales have become one most prominent energy sources all over world. Therefore, estimating amount adsorbed in shale resources is necessary for technical and economic foresight production operations. This paper presents a novel machine learning method called grey wolf optimizer support vector (GWO-SVM) to predict gas. For this purpose, data set containing temperature, pressure, total organic carbon (TOC), humidity has been collected from several sources, GWO-SVM model was created based on it. The results show that R-squared root mean square error equal 0.982 0.08, respectively. Also, ensure proposed gives an excellent prediction compared previously models. Besides, according sensitivity analysis, among input parameters, highest effect adsorption.