Harding, T. Methods to enhance success of field application of in-situ combustion for heavy oil recovery. SPE Reservoir Eval. Eng. 26, 190–197 (2023).
Minakov, A. V., Meshkova, V. D., Guzey, D. V. & Pryazhnikov, M. I. Recent advances in the study of in situ combustion for enhanced oil recovery. Energies 16, 4266 (2023).
Dong, X. et al. Enhanced oil recovery techniques for heavy oil and oilsands reservoirs after steam injection. Appl. Energy 239, 1190–1211 (2019).
Google Scholar
Dong, X., Liu, H., Wu, K. & Chen, Z. in SPE Improved Oil Recovery Conference? D041S018R004 (SPE).
Yang, M., Harding, T. G. & Chen, Z. Numerical investigation of the mechanisms in co-injection of steam and enriched air process using combustion tube tests. Fuel 242, 638–648 (2019).
Medina, O. E., Olmos, C., Lopera, S. H., Cortés, F. B. & Franco, C. A. Nanotechnology applied to thermal enhanced oil recovery processes: A review. Energies 12, 4671 (2019).
Ado, M. R., Greaves, M. & Rigby, S. P. Numerical simulation investigations of the applicability of THAI in situ combustion process in heavy oil reservoirs underlain by bottom water. Petrol. Res. 8, 36–43 (2023).
Turta, A., Chattopadhyay, S., Bhattacharya, R., Condrachi, A. & Hanson, W. Current status of commercial in situ combustion projects worldwide. J. Can. Pet. Technol. https://doi.org/10.2118/07-11-GE (2007).
Ursenbach, M., Moore, R. & Mehta, S. Air injection in heavy oil reservoirs-a process whose time has come (again). J. Can. Petrol. Technol. 49, 48–54 (2010).
Turta, A., Singhal, A. K., Islam, M. N. & Greaves, M. in SPE Canadian Energy Technology Conference. D011S010R002 (SPE).
Martins, M. F. Structure d’un front de combustion propagé en co-courant dans un lit fixe de schiste bitumineux broyé, Institut National Polytechnique (Toulouse), (2008).
Ismail, N. B. & Hascakir, B. Impact of asphaltenes and clay interaction on in-situ combustion performance. Fuel 268, 117358 (2020).
Xu, Q. et al. Chemical-structural properties of the coke produced by low temperature oxidation reactions during crude oil in-situ combustion. Fuel 207, 179–188 (2017).
Bhattacharya, S., Mallory, D. G., Moore, R. G., Ursenbach, M. G. & Mehta, S. A. Vapor-phase combustion in accelerating rate calorimetry for air-injection enhanced-oil-recovery processes. SPE Reservoir Eval. Eng. 20, 669–680 (2017).
Gutiérrez, D. Development of a comprehensive and general approach to in situ combustion modelling. Development 2024, 03–26 (2024).
Bhattacharya, S., Mallory, D., Moore, R., Ursenbach, M. & Mehta, S. in SPE Western Regional Meeting. SPE-180451-MS (SPE).
Antolinez, J. D., Miri, R. & Nouri, A. In situ combustion: A comprehensive review of the current state of knowledge. Energies 16, 6306 (2023).
Mehrabi-Kalajahi, S., Varfolomeev, M. A., Sadikov, K. G., Mikhailova, A. N. & Feoktistov, D. A. The impact of the initiators on heavy oil oxidation in porous media during in situ combustion for in situ upgrading process. Energy Fuels 38, 4134–4141 (2024).
Yuan, C. et al. in SPE Kuwait Oil and Gas Show and Conference. D033S013R006 (SPE).
Belgrave, J. D., Moore, R. G., Ursenbach, M. G. & Bennion, D. W. A comprehensive approach to in-situ combustion modeling. SPE Adv. Technol. Ser. 1, 98–107 (1993).
Yamamoto, K., He, X. & Doolen, G. D. Simulation of combustion field with lattice Boltzmann method. J. Stat. Phys. 107, 367–383 (2002).
Google Scholar
Yamamoto, K., Takada, N. & Misawa, M. Combustion simulation with Lattice Boltzmann method in a three-dimensional porous structure. Proc. Combust. Inst. 30, 1509–1515 (2005).
Xu, Q. et al. Coke formation and coupled effects on pore structure and permeability change during crude oil in situ combustion. Energy Fuels 30, 933–942 (2016).
Zhao, S., Pu, W., Peng, X., Zhang, J. & Ren, H. Low-temperature oxidation of heavy crude oil characterized by TG, DSC, GC-MS, and negative ion ESI FT-ICR MS. Energy 214, 119004 (2021).
Zhao, S., Pu, W., Pan, J., Chen, S. & Zhang, L. Thermo-oxidative characteristics and kinetics of light, heavy, and extra-heavy crude oils using accelerating rate calorimetry. Fuel 312, 123001 (2022).
Askarova, A., Popov, E., Maerle, K. & Cheremisin, A. Comparative study of in-situ combustion tests on consolidated and crushed cores. SPE Reservoir Eval. Eng. 26, 167–179 (2023).
Cao, D. et al. Correction of linear fracture density and error analysis using underground borehole data. J. Struct. Geol. 184, 105152 (2024).
Yanchun, L. et al. Surrogate model for reservoir performance prediction with time-varying well control based on depth generative network. Pet. Explor. Dev. 51, 1287–1300 (2024).
Zhang, Y. et al. Comparison of phenolic antioxidants’ impact on thermal oxidation stability of pentaerythritol ester insulating oil. IEEE Trans. Dielectr. Electr. Insul. https://doi.org/10.1109/TDEI.2024.3487148 (2024).
Li, S. et al. The hidden vulnerabilities of pentaerythritol synthetic ester insulating oil for eco-friendly transformers: A dive into molecular behavior. IEEE Trans. Dielectr. Electr. Insul. https://doi.org/10.1109/TDEI.2024.3510476 (2024).
Wang, C. et al. Integrating experimental study and intelligent modeling of pore evolution in the Bakken during simulated thermal progression for CO2 storage goals. Appl. Energy 359, 122693 (2024).
Zhou, Y., Guan, W., Sun, Q., Zou, X. & He, Z. Effect of multi-scale rough surfaces on oil-phase trapping in fractures: Pore-scale modeling accelerated by wavelet decomposition. Comput. Geotech. 179, 106951 (2025).
Zhang, S. et al. Probing the combustion characteristics of micron-sized aluminum particles enhanced with graphene fluoride. Combust. Flame 272, 113858 (2025).
Gan, B. et al. Phase transitions of CH4 hydrates in mud-bearing sediments with oceanic laminar distribution: Mechanical response and stabilization-type evolution. Fuel 380, 133185 (2025).
Zhang, L. et al. Effect of coal tar components and thermal polycondensation conditions on the formation of mesophase pitch. Materials 18, 1002 (2025).
Google Scholar
Rasouli, A., Dabiri, A. & Nezamabadi-pour, H. A multi-layer perceptron-based approach for prediction of the crude oil pyrolysis process. Energy Sources Part A Recovery Util. Environ. Eff. 37, 1464–1472 (2015).
Norouzpour, M., Rasouli, A. R., Dabiri, A., Azdarpour, A. & Karaei, M. A. Prediction of crude oil pyrolysis process using radial basis function networks. QUID: Investigación, Ciencia Y Tecnología, 567–576 (2017).
Mohammadi, M.-R., Hemmati-Sarapardeh, A., Schaffie, M., Husein, M. M. & Ranjbar, M. Application of cascade forward neural network and group method of data handling to modeling crude oil pyrolysis during thermal enhanced oil recovery. J. Petrol. Sci. Eng. 205, 108836 (2021).
Mohammadi, M.-R. et al. On the evaluation of crude oil oxidation during thermogravimetry by generalised regression neural network and gene expression programming: application to thermal enhanced oil recovery. Combust. Theor. Model. 25, 1268–1295 (2021).
Google Scholar
Hadavimoghaddam, F. et al. Modeling crude oil pyrolysis process using advanced white-box and black-box machine learning techniques. Sci. Rep. 13, 22649 (2023).
Google Scholar
Karimian, M., Schaffie, M. & Fazaelipoor, M. H. Estimation of the kinetic triplet for in-situ combustion of crude oil in the presence of limestone matrix. Fuel 209, 203–210 (2017).
Karimian, M., Schaffie, M. & Fazaelipoor, M. H. Determination of activation energy as a function of conversion for the oxidation of heavy and light crude oils in relation to in situ combustion. J. Therm. Anal. Calorim. 125, 301–311 (2016).
Li, Y.-B. et al. Low temperature oxidation characteristics analysis of ultra-heavy oil by thermal methods. J. Ind. Eng. Chem. 48, 249–258 (2017).
Varfolomeev, M. A. et al. Chemical evaluation and kinetics of Siberian, north regions of Russia and Republic of Tatarstan crude oils. Energy Sources Part A Recovery Util. Environ. Eff. 38, 1031–1038 (2016).
Pu, W., Chen, Y., Li, Y., Zou, P. & Li, D. Comparison of different kinetic models for heavy oil oxidation characteristic evaluation. Energy Fuels 31, 12665–12676 (2017).
Wang, Y. et al. New insights into the oxidation behaviors of crude oils and their exothermic characteristics: Experimental study via simultaneous TGA/DSC. Fuel 219, 141–150 (2018).
Kok, M. V., Varfolomeev, M. A. & Nurgaliev, D. K. Low-temperature oxidation reactions of crude oils using TGA–DSC techniques. J. Therm. Anal. Calorim. 141, 775–781 (2019).
Li, Y.-B. et al. Study of the catalytic effect of copper oxide on the low-temperature oxidation of Tahe ultra-heavy oil. J. Therm. Anal. Calorim. 135, 3353–3362 (2019).
Google Scholar
Yuan, C. et al. Mechanistic and kinetic insight into catalytic oxidation process of heavy oil in in-situ combustion process using copper (II) stearate as oil soluble catalyst. Fuel 284, 118981 (2021).
Zhao, S. et al. Low-temperature oxidation of light and heavy oils via thermal analysis: Kinetic analysis and temperature zone division. J. Petrol. Sci. Eng. 168, 246–255 (2018).
Kök, M. V., Varfolomeev, M. A. & Nurgaliev, D. K. TGA and DSC investigation of different clay mineral effects on the combustion behavior and kinetics of crude oil from Kazan region, Russia. J. Petrol. Sci. Eng. 200, 108364 (2021).
Yuan, C., Emelianov, D. A. & Varfolomeev, M. A. Oxidation behavior and kinetics of light, medium, and heavy crude oils characterized by thermogravimetry coupled with Fourier transform infrared spectroscopy. Energy Fuels 32, 5571–5580 (2018).
Chen, H. et al. The impact of the oil character and quartz sands on the thermal behavior and kinetics of crude oil. Energy 210, 118573 (2020).
Ranjbar, M. & Pusch, G. Pyrolysis and combustion kinetics of crude oils, asphaltenes and resins in relation to thermal recovery processes. J. Anal. Appl. Pyrol. 20, 185–196 (1991).
Kharazi Esfahani, P., Peiro Ahmady Langeroudy, K. & Khorsand Movaghar, M. R. Enhanced machine learning—ensemble method for estimation of oil formation volume factor at reservoir conditions. Sci. Rep. 13, 15199 (2023).
Google Scholar
Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V. & Gulin, A. CatBoost: Unbiased boosting with categorical features. In Advances in Neural Information Processing Systems, Vol. 31 (2018).
Yu, R. et al. Ensemble learning for predicting average thermal extraction load of a hydrothermal geothermal field: A case study in Guanzhong Basin, China. Energy 296, 131146 (2024).
Pu, B. et al. Machine Learning Evaluation Method of Inter-well Sand Body Connectivity by CatBoost Model: A Field Example from Tight Oil Reservoir, Ordos Basin, China.
Kouassi, A. K. F. et al. Identification of karst cavities from 2D seismic wave impedance images based on Gradient-Boosting Decision Trees Algorithms (GBDT): Case of ordovician fracture-vuggy carbonate reservoir, Tahe Oilfield, Tarim Basin, China. Energies 16, 643 (2023).
Hancock, J. T. & Khoshgoftaar, T. M. CatBoost for big data: An interdisciplinary review. J. Big Data 7, 94 (2020).
Google Scholar
Esfandi, T., Sadeghnejad, S. & Jafari, A. Effect of reservoir heterogeneity on well placement prediction in CO2-EOR projects using machine learning surrogate models: Benchmarking of boosting-based algorithms. Geoenergy Sci. Eng. 233, 212564 (2024).
Lv, Q. et al. Modelling minimum miscibility pressure of CO2-crude oil systems using deep learning, tree-based, and thermodynamic models: Application to CO2 sequestration and enhanced oil recovery. Sep. Purif. Technol. 310, 123086 (2023).
Nakhaei-Kohani, R. et al. Extensive data analysis and modelling of carbon dioxide solubility in ionic liquids using chemical structure-based ensemble learning approaches. Fluid Phase Equilib. 585, 114166 (2024).
Liu, B. & Li, C. Mining and analysis of production characteristics data of tight gas reservoirs. Processes 11, 3159 (2023).
Chen, T. & Guestrin, C. in Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. 785–794.
Pan, S., Zheng, Z., Guo, Z. & Luo, H. An optimized XGBoost method for predicting reservoir porosity using petrophysical logs. J. Petrol. Sci. Eng. 208, 109520 (2022).
Xie, Z. et al. Prediction of conformance control performance for cyclic-steam-stimulated horizontal well using the XGBoost: A case study in the Chunfeng heavy oil reservoir. Energies 14, 8161 (2021).
Gu, Y., Zhang, D. & Bao, Z. A new data-driven predictor, PSO-XGBoost, used for permeability of tight sandstone reservoirs: A case study of member of chang 4+ 5, western Jiyuan Oilfield, Ordos Basin. J. Petrol. Sci. Eng. 199, 108350 (2021).
Mahdaviara, M., Sharifi, M. & Ahmadi, M. Toward evaluation and screening of the enhanced oil recovery scenarios for low permeability reservoirs using statistical and machine learning techniques. Fuel 325, 124795 (2022).
Lv, Q. et al. Modeling thermo-physical properties of hydrogen utilizing machine learning schemes: Viscosity, density, diffusivity, and thermal conductivity. Int. J. Hydrogen Energy 72, 1127–1142 (2024).
Google Scholar
Dong, Y. et al. A physics-guided eXtreme gradient boosting model for predicting the initial productivity of oil wells. Geoenergy Sci. Eng. 231, 212402 (2023).
Sun, H., Luo, Q., Xia, Z., Li, Y. & Yu, Y. Bottomhole pressure prediction of carbonate reservoirs using XGBoost. Processes 12, 125 (2024).
Pirizadeh, M., Alemohammad, N., Manthouri, M. & Pirizadeh, M. A new machine learning ensemble model for class imbalance problem of screening enhanced oil recovery methods. J. Petrol. Sci. Eng. 198, 108214 (2021).
Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).
Hidayat, F. & Astsauri, T. M. S. Applied random forest for parameter sensitivity of low salinity water Injection (LSWI) implementation on carbonate reservoir. Alex. Eng. J. 61, 2408–2417 (2022).
Rahimi, M. & Riahi, M. A. Reservoir facies classification based on random forest and geostatistics methods in an offshore oilfield. J. Appl. Geophys. 201, 104640 (2022).
Zaiery, M., Kadkhodaie, A., Arian, M. & Maleki, Z. Application of artificial neural network models and random forest algorithm for estimation of fracture intensity from petrophysical data. J. Pet. Explor. Prod. Technol. 13, 1877–1887 (2023).
Guo, J. et al. Optimized random forest method for 3D evaluation of coalbed methane content using geophysical logging data. ACS Omega 9, 35769–35788 (2024).
Google Scholar
Wang, M., Feng, D., Li, D. & Wang, J. Reservoir parameter prediction based on the neural random forest model. Front. Earth Sci. 10, 888933 (2022).
Google Scholar
Zheng, H., Mahmoudzadeh, A., Amiri-Ramsheh, B. & Hemmati-Sarapardeh, A. Modeling viscosity of CO2–N2 gaseous mixtures using robust tree-based techniques: Extra tree, random forest, GBoost, and LightGBM. ACS Omega 8, 13863–13875 (2023).
Google Scholar
Wang, J. et al. Prediction of permeability using random forest and genetic algorithm model. Comput. Model. Eng. Sci. 125, 1135–1157 (2020).
Song, L. et al. in Journal of Physics: Conference Series. 012084 (IOP Publishing).
Shen, B. et al. A novel CO2-EOR potential evaluation method based on BO-LightGBM algorithms using hybrid feature mining. Geoenergy Sci. Eng. 222, 211427 (2023).
Wang, B., Wu, D., Zhang, K., Zhang, H. & Zhang, C. prediction model of fault block reservoir measure index based on 1DCNN-LightGBM. Sci. Program. 2023, 8555423 (2023).
Liu, Y., Zhu, R., Zhai, S., Li, N. & Li, C. Lithofacies identification of shale formation based on mineral content regression using LightGBM algorithm: A case study in the Luzhou block, South Sichuan Basin, China. Energy Sci. Eng. 11, 4256–4272 (2023).
Wang, X. et al. Prediction technology of a reservoir development model while drilling based on machine learning and its application. Processes 12, 975 (2024).
Kumar, I., Tripathi, B. K. & Singh, A. Synthetic well log modeling with light gradient boosting machine for Assam-Arakan Basin, India. J. Appl. Geophys. 203, 104697 (2022).
Ke, G. et al. Lightgbm: A highly efficient gradient boosting decision tree. In Advances in neural information processing systems, Vol. 30 (2017).
Mohammadi, M.-R., Larestani, A., Schaffie, M., Hemmati-Sarapardeh, A. & Ranjbar, M. Predictive modeling of CO2 solubility in piperazine aqueous solutions using boosting algorithms for carbon capture goals. Sci. Rep. 14, 22112 (2024).
Google Scholar
Mohammadi, M.-R. et al. Modeling the solubility of light hydrocarbon gases and their mixture in brine with machine learning and equations of state. Sci. Rep. 12, 14943 (2022).
Google Scholar
Murugan, P., Mani, T., Mahinpey, N. & Asghari, K. The low temperature oxidation of Fosterton asphaltenes and its combustion kinetics. Fuel Process. Technol. 92, 1056–1061 (2011).
Chen, G. et al. The genetic algorithm based back propagation neural network for MMP prediction in CO2-EOR process. Fuel 126, 202–212 (2014).
Xu, M., Wong, T.-C. & Chin, K.-S. Modeling daily patient arrivals at Emergency Department and quantifying the relative importance of contributing variables using artificial neural network. Decis. Support Syst. 54, 1488–1498 (2013).
Madani, S. A. et al. Modeling of nitrogen solubility in normal alkanes using machine learning methods compared with cubic and PC-SAFT equations of state. Sci. Rep. 11, 24403 (2021).
Google Scholar
Leroy, A. M. & Rousseeuw, P. J. Robust regression and outlier detection. rrod (1987).
Goodall, C. R. 13 Computation using the QR decomposition (1993).
Gramatica, P. Principles of QSAR models validation: Internal and external. QSAR Comb. Sci. 26, 694–701 (2007).
Lv, Q. et al. Application of group method of data handling and gene expression programming to modeling molecular diffusivity of CO2 in heavy crudes. Geoenergy Sci. Eng. 237, 212789 (2024).
Rousseeuw, P. J. & Leroy, A. M. Robust Regression and Outlier Detection (John wiley & sons, Hoboken, 2005).
