Machine learning prediction of methane,ethane,and propane solubility in pure water and electrolyte solutions:Implications for stray gas migration modeling  

作　　者：Ghazal Kooti Reza Taherdangkoo Chaofan Chen Nikita Sergeev Faramarz Doulati Ardejani Tao Meng Christoph Butscher 

机构地区：[1]Institute of Geotechnics,TU Bergakademie Freiberg,Gustav-Zeuner-Str.1,09599 Freiberg,Germany [2]Department of Petroleum Engineering,Amirkabir University of Technology,Tehran,Iran [3]Freiberg Center for Water Research ZeWaF,TU Bergakademie Freiberg,09599 Freiberg,Germany [4]School of Mining,College of Engineering,University of Tehran,Tehran,Iran [5]Taiyuan University of Science and Technology,Taiyuan 030024,China

出　　处：《Acta Geochimica》2024年第5期971-984,共14页地球化学学报（英文）

摘　　要：Hydraulic fracturing is an effective technology for hydrocarbon extraction from unconventional shale and tight gas reservoirs.A potential risk of hydraulic fracturing is the upward migration of stray gas from the deep subsurface to shallow aquifers.The stray gas can dissolve in groundwater leading to chemical and biological reactions,which could negatively affect groundwater quality and contribute to atmospheric emissions.The knowledge oflight hydrocarbon solubility in the aqueous environment is essential for the numerical modelling offlow and transport in the subsurface.Herein,we compiled a database containing 2129experimental data of methane,ethane,and propane solubility in pure water and various electrolyte solutions over wide ranges of operating temperature and pressure.Two machine learning algorithms,namely regression tree(RT)and boosted regression tree(BRT)tuned with a Bayesian optimization algorithm(BO)were employed to determine the solubility of gases.The predictions were compared with the experimental data as well as four well-established thermodynamic models.Our analysis shows that the BRT-BO is sufficiently accurate,and the predicted values agree well with those obtained from the thermodynamic models.The coefficient of determination(R2)between experimental and predicted values is 0.99 and the mean squared error(MSE)is 9.97×10^(-8).The leverage statistical approach further confirmed the validity of the model developed.

关 键 词：Gas solubility Hydraulic fracturing Thermodynamic models Regression tree Boosted regression tree Groundwater contamination 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] TE357.1[自动化与计算机技术—控制科学与工程]