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On the prediction of methane adsorption in shale using grey wolf optimizer support vector machine approach 被引量:1
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作者 Rahmad Syah Mohammad Hossein Towfighi Naeem +2 位作者 Reza Daneshfar Hossein Dehdar Bahram Soltani Soulgani 《Petroleum》 EI CSCD 2022年第2期264-269,共6页
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 a... 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. 展开更多
关键词 Gas adsorption SHALE Machine learning MODEL Support vector machine Grey wolf optimizer
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