[1] Bettir, N., et al. Improved Shale Volume Prediction Using Machine Learning Algorithms in Complex Reservoirs. in ARMA US Rock Mechanics/Geomechanics Symposium. 2024. ARMA.
[2] Ma, Y., et al., Influence of Rock Fabric on Physical Properties of Shale Oil Reservoir Under Effective Pressure Conditions. Lithosphere, 2024. 2024(2): p. lithosphere_2023_338.
[3] Mabiala Mbouaki, A.P., et al., Petrophysical Evaluation of a Shaly Sandstone Reservoir and the Effect of Clay Minerals on Reservoir Quality: A Case Study from the Barremian Mengo Sandstone, Kouilou Basin, Republic of Congo. ACS omega, 2025. 10(10): p. 10081-10106.
[4] Ganguli, S.S. and V.P. Dimri, Reservoir Characterization, Modeling and Quantitative Interpretation: Recent Workflows to Emerging Technologies. Vol. 6. 2023: Elsevier.
[5] Bin, W., et al., Experimental study on hydraulic fracture propagation behavior in heterogeneous shale formations. Frontiers in Energy Research, 2024. 11: p. 1309591.
[6] Gong, X., X. Ma, and Y. Liu, Analysis of geological factors affecting propagation behavior of fracture during hydraulic fracturing shale formation. Geomechanics and Geophysics for Geo-Energy and Geo-Resources, 2024. 10(1): p. 102.
[7] Belyadi, H., E. Fathi, and F. Belyadi, Hydraulic fracturing in unconventional reservoirs: theories, operations, and economic analysis. 2019: Gulf Professional Publishing.
[8] Asquith, G.B. and C.R. Gibosn, Basic well log analysis for geologists. 1982: American Association of Petroleum Geologists.
[9] Vu, D.H. and H.T. Nguyen, Estimation of shale volume from well logging data using Artificial Neural Network. Tạp chí Khoa học kỹ thuật Mỏ-Địa chất, 2021: p. 46-52.
[10] Szabó, N.P., Shale volume estimation based on the factor analysis of well-logging data. Acta Geophysica, 2011. 59: p. 935-953.
[11] Zhang, H. and W. Wu, Shale content prediction of well logs based on CNN-BiGRU-VAE neural network. Journal of Earth System Science, 2023. 132(3): p. 139.
[12] Ebrahimi, P., et al., Shale volume estimation using machine learning methods from the southwestern fields of Iran. Results in Engineering, 2025. 25: p. 104506.
[13] Dehghani, M., S. Jahani, and A. Ranjbar, Comparing the performance of machine learning methods in estimating the shear wave transit time in one of the reservoirs in southwest of Iran. Scientific Reports, 2024. 14(1): p. 4744.
[14] Mondal, D., V. Srivardhan, and B. Singh. A Wavelet and Neural Network based approach towards determination of Shale Volume using well logs of Indian Coalfields. in 79th EAGE Conference and Exhibition 2017. 2017. EAGE Publications BV.
[15] Mohammadinia, F., et al., Shale volume estimation using ANN, SVR, and RF algorithms compared with conventional methods. Journal of African Earth Sciences, 2023. 205: p. 104991.
[16] Ardebili, P.N., G. Jozanikohan, and A. Moradzadeh, Estimation of porosity and volume of shale using artificial intelligence, case study of Kashafrud Gas Reservoir, NE Iran. Journal of Petroleum Exploration and Production Technology, 2024. 14(2): p. 477-494.
[17] Huang, S., et al., Support vector regression based on the particle swarm optimization algorithm for tight oil recovery prediction. ACS omega, 2021. 6(47): p. 32142-32150.
[18] Meng, Y., et al., Prediction of Total Organic Carbon Content in Shale Based on PCA-PSO-XGBoost. Applied Sciences, 2025. 15(7): p. 3447.
[19] Wang, T., et al., Productivity prediction of fractured horizontal well in shale gas reservoirs with machine learning algorithms. Applied Sciences, 2021. 11(24): p. 12064.
[20] Yang, R., et al., Long short-term memory suggests a model for predicting shale gas production. Applied Energy, 2022. 322: p. 119415.
[21] Garcia-Cifuentes, K., et al., Identification of Extended Emission Gamma-Ray Burst Candidates Using Machine Learning. The Astrophysical Journal, 2023. 951(1): p. 4.
[22] Gao, P., et al., Influence of dispersion and stabilization of active metals on Ni-Cu/AC catalyst on gas phase carbonylation of ethanol. Fuel, 2021. 292: p. 120308.
[23] Ye, L., et al., Application of machine learning in cosmic ray particle identification. ACTA PHYSICA SINICA, 2023. 72(14).
[24] Kuran, F., G. Tanırcan, and E. Pashaei, Developing machine learning-based ground motion models to predict peak ground velocity in Turkiye. Journal of Seismology, 2024. 28(5): p. 1183-1204.
[25] Ali, M., Machine learning based shale volume prediction from the Norwegian North Sea. 2021, uis.
[26] Han, D. and S. Kwon, Application of machine learning method of data-driven deep learning model to predict well production rate in the shale gas reservoirs. Energies, 2021. 14(12): p. 3629.
[27] Rezaei Mirghaed, B., A. Dehghan Monfared, and A. Ranjbar, Enhanced petrophysical evaluation through machine learning and well logging data in an Iranian oil field. Scientific Reports, 2024. 14(1): p. 28941.
[28] Breiman, L., Random forests. Machine learning, 2001. 45: p. 5-32.
[29] Geurts, P., D. Ernst, and L. Wehenkel, Extremely randomized trees. Machine learning, 2006. 63: p. 3-42.
[30] Goodfellow, I., et al., Deep learning. Vol. 1. 2016: MIT press Cambridge.
[31] Friedman, J., T. Hastie, and R. Tibshirani, Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors). The annals of statistics, 2000. 28(2): p. 337-407.
[32] Hastie, T., et al., Random forests. The elements of statistical learning: Data mining, inference, and prediction, 2009: p. 587-604.
[33] Liaw, A. and M. Wiener, Classification and regression by randomForest. R news, 2002. 2(3): p. 18-22.
[34] Ho, T.K., The random subspace method for constructing decision forests. IEEE transactions on pattern analysis and machine intelligence, 1998. 20(8): p. 832-844.
[35] Pal, M., Random forest classifier for remote sensing classification. International journal of remote sensing, 2005. 26(1): p. 217-222.
[36] Quinlan, J.R., Induction of decision trees. Machine learning, 1986. 1: p. 81-106.
[37] Loh, W.Y., Classification and regression trees. Wiley interdisciplinary reviews: data mining and knowledge discovery, 2011. 1(1): p. 14-23.
[38] Kononenko, I., Machine learning for medical diagnosis: history, state of the art and perspective. Artificial Intelligence in medicine, 2001. 23(1): p. 89-109.
[39] Pedregosa, F., et al., Scikit-learn: Machine learning in Python. the Journal of machine Learning research, 2011. 12: p. 2825-2830.
[40] Srivastava, N., et al., Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research, 2014. 15(1): p. 1929-1958.
[41] Goodfellow, I., Deep learning. 2016, MIT press.
[42] Goodfellow, I.J., et al., An empirical investigation of catastrophic forgetting in gradient-based neural networks. arXiv preprint arXiv:1312.6211, 2013.
[43] Nair, V. and G.E. Hinton. Rectified linear units improve restricted boltzmann machines. in Proceedings of the 27th international conference on machine learning (ICML-10). 2010.
[44] Géron, A., Hands-on machine learning with scikit-learn, keras, and tensorflow: concepts. Aurélien Géron-Google Kitaplar, yy https://books. google. com. tr/books, 2019.
[45] Duda, R.O. and P.E. Hart, Pattern classification. 2006: John Wiley & Sons.
[46] Kingma, D.P., Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.
[47] Chicco, D., M.J. Warrens, and G. Jurman, The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. Peerj computer science, 2021. 7: p. e623.
[48] Willmott, C.J. and K. Matsuura, Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate research, 2005. 30(1): p. 79-82.
[49] Ebrahimi, A., et al., Estimation of shear wave velocity in an Iranian oil reservoir using machine learning methods. Journal of Petroleum Science and Engineering, 2022. 209: p. 109841.
[50] Ebrahimi, P., et al., Young’s Modulus Estimation Using Machine Learning Methods and Daily Drilling Reports. Journal of Oil, Gas and Petrochemical Technology, 2023. 10(1): p. 1-24.
[51] Akbari, A., et al., Enhanced water saturation estimation in hydrocarbon reservoirs using machine learning. Scientific Reports, 2025. 15(1): p. 29846.
