[1] Hu L, Deng J, Zhu H, Lin H, Chen Z, Deng F, et al. A new pore pressure prediction method-back propagation artificial neural network. Electron J Geotech Eng 2013;18:4093-107. doi:
http://www.ejge.com/2013/Ppr2013.371mlr.pdf.
[4] Zhang Y, Lv D, Wang Y, Liu H, Song G, Gao J. Geological characteristics and abnormal pore pressure prediction in shale oil formations of the Dongying depression, China. Energy Science & Engineering 2020;8(6):1962-79. doi:
https://doi.org/10.1002/ese3.641.
[5] Mousavipour F, Riahi MA, Moghanloo HGJJoPE, Technology P. Prediction of in situ stresses, mud window and overpressure zone using well logs in South Pars field. 2020;10(5):1869-79. doi:
https://doi.org/10.2118/189665-PA.
[6] Ahedor MK-N, Anumah P, Sarkodie-Kyeremeh J. Post-Drill Pore Pressure and Fracture Gradient Analyses of Y-Field in the Offshore Tano Basin of Ghana. OnePetro; 2020. doi:
https://doi.org/10.2118/203659-MS.
[7] Mahetaji M, Brahma J, Sircar A. Pre-drill pore pressure prediction and safe well design on the top of Tulamura anticline, Tripura, India: a comparative study. Journal of Petroleum Exploration and Production Technology 2020;10(3):1021-49. doi:
https://link.springer.com/article/10.1007/s13202-019-00816-0.
[9] Darvishpour A, Seifabad MC, Wood DA, Ghorbani H. Wellbore stability analysis to determine the safe mud weight window for sandstone layers. Petroleum Exploration and Development 2019;46(5):1031-8. doi:
https://doi.org/10.1016/S1876-3804(19)60260-0.
[10] Li S, George J, Purdy C. Pore-pressure and wellbore-stability prediction to increase drilling efficiency. Journal of Petroleum Technology 2012;64(02):98-101. doi:
https://doi.org/10.2118/144717-JPT.
[11] Benemaran RS, Esmaeili-Falak M. Optimization of cost and mechanical properties of concrete with admixtures using MARS and PSO. Computers and Concrete, An International Journal 2020;26(4):309-16. doi:
https://doi.org/10.1061/(ASCE)CR.1943-5495.0000188.
[12] Zhu W, Huang L, Mao L, Esmaeili‐Falak M. Predicting the uniaxial compressive strength of oil palm shell lightweight aggregate concrete using artificial intelligence‐based algorithms. Structural Concrete 2021. doi:
https://doi.org/10.1002/suco.202100656.
[13] Hottmann CE, Johnson RK. Estimation of formation pressures from log-derived shale properties. Journal of Petroleum Technology 1965;17(06):717-22. doi:
https://doi.org/10.2118/1110-PA.
[14] Bingham G. A new approach to interpreting rock drillability. TECHNICAL MANUAL REPRINT, OIL AND GAS JOURNAL, 1965 93 P 1965. doi:
[15] Jorden JR, Shirley OJ. Application of drilling performance data to overpressure detection. Journal of Petroleum Technology 1966;18(11):1387-94. doi:
https://doi.org/10.2118/1407-PA.
[16] Terzaghi K, Peck RB, Mesri G. Soil mechanics in engineering practice. John Wiley & Sons; 1996.
[19] Bowers GL. Pore pressure estimation from velocity data: Accounting for overpressure mechanisms besides undercompaction. SPE Drilling & Completion 1995;10(02):89-95. doi:
https://doi.org/10.2118/27488-PA.
[20] Yoshida C, Ikeda S, Eaton BA. An investigative study of recent technologies used for prediction, detection, and evaluation of abnormal formation pressure and fracture pressure in North and South America. OnePetro; 1996. doi:
https://doi.org/10.2118/36381-MS.
[21] Shen Y, Luan G, Zhang H, Liu Q, Zhang J, Ge H. Novel method for calculating the effective stress coefficient in a tight sandstone reservoir. KSCE Journal of Civil Engineering 2017;21(6):2467. doi: 10.1007/s12205-016-0514-5.
[23] Abad ARB, Mousavi S, Mohamadian N, Wood DA, Ghorbani H, Davoodi S, et al. Hybrid machine learning algorithms to predict condensate viscosity in the near wellbore regions of gas condensate reservoirs. Journal of Natural Gas Science and Engineering 2021;95:104210. doi:
https://doi.org/10.1016/j.jngse.2021.104210.
[24] Naveshki M, Naghiei A, Soltani Tehrani P, Ahmadi Alvar M, Ghorbani H, Mohamadian N, et al. Prediction of bubble point pressure using new hybrid computationail intelligence models. Journal of Chemical and Petroleum Engineering 2021. doi: 10.22059/JCHPE.2021.314719.1341.
[25] Hazbeh O, Ahmadi Alvar M, Aghdam K-y, Ghorbani H, Mohamadian N, Moghadasi J. Hybrid computing models to predict oil formation volume factor using multilayer perceptron algorithm. Journal of Petroleum and Mining Engineering 2021:14-27. doi: 10.21608/JPME.2021.52149.1062.
[26] Wang Y, Ma H, Fu W. Formation pressure prediction based on hybrid genetic algorithm. IEEE; 2010:2535-8. doi: 10.1109/ICOSP.2010.5656925.
[28] Haris A, Sitorus RJ, Riyanto A. Pore pressure prediction using probabilistic neural network: case study of South Sumatra Basin. IOP Conference Series: Earth and Environmental Science 2017;62:012021. doi: 10.1088/1755-1315/62/1/012021.
[29] Rashidi M, Asadi A. An Artificial Intelligence Approach in Estimation of Formation Pore Pressure by Critical Drilling Data. OnePetro; 2018. doi:
[31] Abdelaal A, Elkatatny S, Abdulraheem A. Data-Driven Modeling Approach for Pore Pressure Gradient Prediction while Drilling from Drilling Parameters. ACS omega 2021. doi:
https://doi.org/10.1021/acsomega.1c01340.
[32] Legg S, Hutter M. A collection of definitions of intelligence. Frontiers in Artificial Intelligence and applications 2007;157:17. doi:
[34] Poole D, Mackworth A, Goebel R. Computational Intelligence. 1998. doi:
[35] Nie G, Rowe W, Zhang L, Tian Y, Shi Y. Credit card churn forecasting by logistic regression and decision tree. Expert Systems with Applications 2011;38(12):15273-85. doi:
https://doi.org/10.1016/j.eswa.2011.06.028.
[36] Tsai C-F, Chiou Y-J. Earnings management prediction: A pilot study of combining neural networks and decision trees. Expert systems with applications 2009;36(3):7183-91. doi:
https://doi.org/10.1016/j.eswa.2008.09.025.
[39] Fakhari A, Moghadam AME. Combination of classification and regression in decision tree for multi-labeling image annotation and retrieval. Applied Soft Computing 2013;13(2):1292-302. doi:
https://doi.org/10.1016/j.asoc.2012.10.019.
[40] Liu J, Sui C, Deng D, Wang J, Feng B, Liu W, et al. Representing conditional preference by boosted regression trees for recommendation. Information Sciences 2016;327:1-20. doi:
https://doi.org/10.1016/j.ins.2015.08.001.
[41] Ahmad MS, Adnan SM, Zaidi S, Bhargava P. A novel support vector regression (SVR) model for the prediction of splice strength of the unconfined beam specimens. Construction and Building Materials 2020;248:118475. doi:
https://doi.org/10.1016/j.conbuildmat.2020.118475.
[42] Rui J, Zhang H, Zhang D, Han F, Guo Q. Total organic carbon content prediction based on support-vector-regression machine with particle swarm optimization. Journal of Petroleum Science and Engineering 2019;180:699-706. doi:
https://doi.org/10.1016/j.petrol.2019.06.014.
[43] Shao M, Wang X, Bu Z, Chen X, Wang Y. Prediction of energy consumption in hotel buildings via support vector machines. Sustainable Cities and Society 2020;57:102128. doi:
https://doi.org/10.1016/j.scs.2020.102128.
[45] Vapnik V. The nature of statistical learning theory. Springer science & business media; 2013.
[46] Hashemitaheri M, Mekarthy SMR, Cherukuri H. Prediction of specific cutting forces and maximum tool temperatures in orthogonal machining by support vector and Gaussian process regression methods. Procedia Manufacturing 2020;48:1000-8. doi:
https://doi.org/10.1016/j.promfg.2020.05.139.
[47] Zhou X, Lu P, Zheng Z, Tolliver D, Keramati A. Accident prediction accuracy assessment for highway-rail grade crossings using random forest algorithm compared with decision tree. Reliability Engineering & System Safety 2020;200:106931. doi:
https://doi.org/10.1016/j.ress.2020.106931.
[48] Grape S, Branger E, Elter Z, Balkeståhl LP. Determination of spent nuclear fuel parameters using modelled signatures from non-destructive assay and Random Forest regression. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 2020;969:163979. doi:
https://doi.org/10.1016/j.nima.2020.163979.
[49] Ahmad MW, Reynolds J, Rezgui Y. Predictive modelling for solar thermal energy systems: A comparison of support vector regression, random forest, extra trees and regression trees. Journal of cleaner production 2018;203:810-21. doi:
https://doi.org/10.1016/j.jclepro.2018.08.207.
[50] Ahmad MW, Mourshed M, Rezgui Y. Trees vs Neurons: Comparison between random forest and ANN for high-resolution prediction of building energy consumption. Energy and Buildings 2017;147:77-89. doi:
https://doi.org/10.1016/j.enbuild.2017.04.038.
[51] Shahbaz M, Taqvi SA, Loy ACM, Inayat A, Uddin F, Bokhari A, et al. Artificial neural network approach for the steam gasification of palm oil waste using bottom ash and CaO. Renewable Energy 2019;132:243-54. doi:
https://doi.org/10.1016/j.renene.2018.07.142.
[53] Abad ARB, Ghorbani H, Mohamadian N, Davoodi S, Mehrad M, Aghdam SK-y, et al. Robust hybrid machine learning algorithms for gas flow rates prediction through wellhead chokes in gas condensate fields. Fuel 2022;308:121872. doi:
https://doi.org/10.1016/j.fuel.2021.121872.
[57] Jain A, Zongker D. Feature selection: Evaluation, application, and small sample performance. IEEE transactions on pattern analysis and machine intelligence 1997;19(2):153-8. doi:
https://doi.org/10.1109/34.574797.
[59] Salehi M, Farhadi S, Moieni A, Safaie N, Ahmadi HJFips. Mathematical modeling of growth and paclitaxel biosynthesis in Corylus avellana cell culture responding to fungal elicitors using multilayer perceptron-genetic algorithm. 2020;11. doi: 10.3389/fpls.2020.01148.
[60] Jotheeswaran J, Koteeswaran S. Sentiment Polarity Classification Using Conjure of Genetic Algorithm and Differential Evolution Methods for Optimized Feature Selection. Recent Advances in Computer Science and Communications (Formerly: Recent Patents on Computer Science) 2020;13(6):1284-91. doi:
https://doi.org/10.2174/2213275911666180904110105.