Utilization of adaptive neuro-fuzzy interference system and functional network in prediction of total organic carbon content

[1] Tang H(2019)Investigating the pressure characteristics and production performance of liquid-loaded horizontal wells in unconventional gas reservoirs J Pet Sci Eng 176 456-465

[2] Sun Z(2019)Estimating thermal maturity of organic-rich shale from well logs: Case studies of two shale plays Fuel 235 1195-1206

[3] He Y(2021)Smart proxy modeling of a fractured reservoir model for production optimization: implementation of metaheuristic algorithm and probabilistic application Nat Resour Res 30 2431-2462

[4] Chai Z(2021)Optimization of WAG in real geological field using rigorous soft computing techniques and nature-inspired algorithms J Pet Sci Eng 206 109038-630

[5] Hasan AR(2020)Applying hybrid support vector regression and genetic algorithm to water alternating CO 2 gas EOR Greenh Gases Sci Technol 10 613-33287

[6] Killough J(2020)Pore-scale 3D dynamic modeling and characterization of shale samples: considering the effects of thermal maturation J. Geophys. Res. Solid Earth 188 33274-16

[7] Zhao P(2020)A new and reliable dual model- and data-driven TOC prediction concept: A TOC logging evaluation method using multiple overlapping methods integrated with semi-supervised deep learning J Pet Sci Eng 45 1-354

[8] Ostadhassan M(2021)Modeling of methane adsorption capacity in shale gas formations using white-box supervised machine learning techniques J Pet Sci Eng 20 339-643

[9] Shen B(2020)Modeling solubility of sulfur in pure hydrogen sulfide and sour gas mixtures using rigorous machine learning methods Int J Hydrogen Energy 97 634-80

[10] Liu W(2015)Differences and relations between unconventional and conventional oil and gas China Pet Explor 175 72-678

[1] Tang H(2019)Investigating the pressure characteristics and production performance of liquid-loaded horizontal wells in unconventional gas reservoirs J Pet Sci Eng 176 456-465

[2] Sun Z(2019)Estimating thermal maturity of organic-rich shale from well logs: Case studies of two shale plays Fuel 235 1195-1206

[3] He Y(2021)Smart proxy modeling of a fractured reservoir model for production optimization: implementation of metaheuristic algorithm and probabilistic application Nat Resour Res 30 2431-2462

[4] Chai Z(2021)Optimization of WAG in real geological field using rigorous soft computing techniques and nature-inspired algorithms J Pet Sci Eng 206 109038-630

[5] Hasan AR(2020)Applying hybrid support vector regression and genetic algorithm to water alternating CO 2 gas EOR Greenh Gases Sci Technol 10 613-33287

[6] Killough J(2020)Pore-scale 3D dynamic modeling and characterization of shale samples: considering the effects of thermal maturation J. Geophys. Res. Solid Earth 188 33274-16

[7] Zhao P(2020)A new and reliable dual model- and data-driven TOC prediction concept: A TOC logging evaluation method using multiple overlapping methods integrated with semi-supervised deep learning J Pet Sci Eng 45 1-354

[8] Ostadhassan M(2021)Modeling of methane adsorption capacity in shale gas formations using white-box supervised machine learning techniques J Pet Sci Eng 20 339-643

[9] Shen B(2020)Modeling solubility of sulfur in pure hydrogen sulfide and sour gas mixtures using rigorous machine learning methods Int J Hydrogen Energy 97 634-80

[10] Liu W(2015)Differences and relations between unconventional and conventional oil and gas China Pet Explor 175 72-678