Adankon, M.M., Cheriet, M., 2009. Model selection for the LS-SVM. Application to handwriting recognition. Pattern Recognition 42, 3264-3270. doi: https://doi.org/10.1016/j.patcog.2008.10.023 .
Addis, M.A., 2017. The geology of geomechanics: petroleum geomechanical engineering in field development planning. Geological Society, London, Special Publications 458, 7-29. doi: https://doi.org/10.1144/SP458.7 .
Ahmed, A., Elkatatny, S., Ali, A., Mahmoud, M., Abdulraheem, A., 2019a. New Model for Pore Pressure Prediction While Drilling Using Artificial Neural Networks. Arabian Journal for Science and Engineering 44, 6079-6088. doi: https://doi.org/10.2118/192318-MS .
Ahmed, S., Mahmoud, A.A., Elkatatny, S., Mahmoud, M., Abdulraheem, A., 2019b. Prediction of Pore and Fracture Pressures Using Support Vector Machine, International Petroleum Technology Conference. International Petroleum Technology Conference. doi: https://doi.org/10.2523/IPTC-19523-MS.
Anemangely, M., Ramezanzadeh, A., Amiri, H., Hoseinpour, S.-A., 2019a. Machine learning technique for the prediction of shear wave velocity using petrophysical logs. Journal of Petroleum Science and Engineering 174, 306-327. doi: https://doi.org/10.1016/j.petrol.2018.11.032 .
Anemangely, M., Ramezanzadeh, A., Behboud, M.M ,. 2019b. Geomechanical parameter estimation from mechanical specific energy using artificial intelligence. Journal of Petroleum Science and Engineering 175, 407-429. doi: https://doi.org/10.1016/j.petrol.2018.12.054 .
Atashbari, V., Tingay, M., 2012. Pore pressure prediction in carbonate reservoirs. OnePetro. doi: https://doi.org/10.2118/150835-MS.
Atashnezhad, A., Wood, D.A., Fereidounpour, A., Khosravanian, R., 2014. Designing and optimizing deviated wellbore trajectories using novel particle swarm algorithms. Journal of Natural Gas Science and Engineering 21, 1184-1204. doi: https://doi.org/10.1016/j.jngse.2014.05.029 .
Azadpour, M., Manaman, N.S., Kadkhodaie-Ilkhchi, A., Sedghipour, M.-R., 2015. Pore pressure prediction and modeling using well-logging data in one of the gas fields in south of Iran. Journal of Petroleum Science and Engineering 128, 15-23. doi: https://doi.org/10.1016/j.petrol.2015.02.022 .
Bowers, G.L., 1995. Pore pressure estimation from velocity data: Accounting for overpressure mechanisms besides undercompaction. SPE Drilling & Completion 10, 89-95. doi: https://doi.org/10.2118/27488-PA.
Choubineh, A., Ghorbani, H., Wood, D.A., Moosavi, S.R., Khalafi, E., Sadatshojaei, E., 2017. Improved predictions of wellhead choke liquid critical-flow rates: modelling based on hybrid neural network training learning based optimization. Fuel 207, 547-560. doi: https://doi.org/10.1016/j.fuel.2017.06.131 .
Coello, C.A.C., Lamont, G.B., Van Veldhuizen, D.A., 2007. Evolutionary algorithms for solving multi-objective problems. Springer.
Darabi, H., Kavousi, A., Moraveji, M., Masihi, M., 2010. 3D fracture modeling in Parsi oil field using artificial intelligence tools. Journal of Petroleum Science and Engineering 71, 67-76. doi: https://doi.org/10.1016/j.petrol.2010.01.004 .
Darling, T., 2005. Well logging and formation evaluation. Elsevier.
Eaton, B.A., 1975. The equation for geopressure prediction from well logs. OnePetro. doi: https://doi.org/10.2118/5544-MS.
Gandomi, A.H., Yang, X.-S., Talatahari, S., Alavi, A.H., 2013. Metaheuristic applications in structures and infrastructures. Newnes.
Ghasemi, M., Bayuk, I., 2020. Bounds for Pore Space Parameters of Petroelastic Models of Carbonate Rocks. IZVESTIYA, PHYSICS OF THE SOLID EARTH 56. doi: https://link.springer.com/article/10.1134/S1069351320020032 .
Ghorbani, H., Moghadasi, J., Wood, D.A., 2017. Prediction of gas flow rates from gas condensate reservoirs through wellhead chokes using a firefly optimization algorithm. Journal of Natural Gas Science and Engineering 45, 256-271. doi :
Ghorbani, H., Wood, D.A., Choubineh, A., Mohamadian, N., Tatar, A., Farhangian, H., Nikooey, A., 2020. Performance comparison of bubble point pressure from oil PVT data: Several neurocomputing techniques compared. Experimental and Computational Multiphase Flow 2, 225-246. doi :
Ghorbani, H., Wood, D.A., Moghadasi, J., Choubineh, A., Abdizadeh, P., Mohamadian, N., 2019. Predicting liquid flow-rate performance through wellhead chokes with genetic and solver optimizers: an oil field case study. Journal of Petroleum Exploration and Production Technology 9, 1355-1373. doi :
Gutierrez, M.A., Braunsdor, N.R., Couzens, B.A., 2006. Calibration and ranking of pore-pressure prediction models. The Leading Edge 25, 1516-1523. doi: https://doi.org/10.1190/1.1500391 .
Hassanpouryouzband, A., Yang, J., Okwananke, A., Burgass, R., Tohidi, B., Chuvilin, E., Istomin, V., Bukhanov, B., 2019a. An Experimental Investigation on the Kinetics of Integrated Methane Recovery and CO 2 Sequestration by Injection of Flue Gas into Permafrost Methane Hydrate Reservoirs. Scientific reports 9, 1-9. doi: https://doi.org/10.1038/s41598-019-52745-x .
Hassanpouryouzband, A., Yang, J., Tohidi, B., Chuvilin, E., Istomin, V., Bukhanov, B., 2019b. Geological CO2 capture and storage with flue gas hydrate formation in frozen and unfrozen sediments: method development, real time-scale kinetic characteristics, efficiency, and clathrate structural transition. ACS Sustainable Chemistry and Engineering 7, 5338-5345. doi: https//:pubs.acs.org/doi/abs/10.1021/acssuschemeng.8b06374 .
Hassanpouryouzband, A., Yang, J., Tohidi, B., Chuvilin, E., Istomin, V., Bukhanov, B., Cheremisin, A., 2018a. CO2 capture by injection of flue gas or CO2–N2 mixtures into hydrate reservoirs: Dependence of CO2 capture efficiency on gas hydrate reservoir conditions. Environmental science and technology 52, 4324-4330. doi: https://pubs.acs.org/doi/abs/10.1021/acs.est.7b05784 .
Hassanpouryouzband, A., Yang, J., Tohidi, B., Chuvilin, E., Istomin, V., Bukhanov, B., Cheremisin, A., 2018b. Insights into CO2 capture by flue gas hydrate formation: gas composition evolution in systems containing gas hydrates and gas mixtures at stable pressures. ACS Sustainable Chemistry and Engineering 6, 5732-5736. doi: https//:pubs.acs.org/doi/abs/10.1021/acssuschemeng.8b00409 .
Jafari, I., Masihi, M., Zarandi, M.N., 2018. Scaling of counter-current imbibition recovery curves using artificial neural networks. Journal of Geophysics and Engineering 15, 1062-1070. doi: https://doi .org/10.1088/1742-2140/aa9fe3 .
Kennedy, J., 1997. The particle swarm: social adaptation of knowledge, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC'97). IEEE, pp. 303-308. doi :
Kennedy, J., Eberhart, R., 1995. Particle swarm optimization, Proceedings of ICNN'95-International Conference on Neural Networks. IEEE, pp. 1942-1948. doi :
Keshavarzi, R., Jahanbakhshi, R., 2013. Real-time prediction of pore pressure gradient through an artificial intelligence approach: a case study from one of middle east oil fields. European journal of environmental and civil engineering 17, 675-686. doi: https://doi.org/10.1080/19648189.2013.811614 .
Kisi, O., Parmar, K.S., 2016. Application of least square support vector machine and multivariate adaptive regression spline models in long term prediction of river water pollution. Journal of Hydrology 534, 104-112. doi: https://doi.org/10.1016/j.jhydrol.2015.12.014 .
Lima, C.A., Coelho, A.L., Eisencraft, M., 2010. Tackling EEG signal classification with least squares support vector machines: A sensitivity analysis study. Computers in Biology and Medicine 40, 705-714. doi: https://doi.org/10.1016/j.compbiomed.2010.06.005 .
Liu, H., 2017. Principles and applications of well logging. Springer.
Mustafa, M., Rezaur, R., Rahardjo, H., Isa, M., 2012. Prediction of pore-water pressure using radial basis function neural network. Engineering Geology 135, 40-47. doi: https://doi.org/10.1016/j.enggeo.2012.02.008 .
Osborne, M.J., Swarbrick, R.E., 1997. Mechanisms for generating overpressure in sedimentary basins: A reevaluation. AAPG bulletin 81, 1023-1041. doi:
https://doi.org/10.1306/522B49C9-1727-11D7 8645000102C1865D .
Pedersen, M.E.H., Chipperfield, A.J., 2010. Simplifying particle swarm optimization. Applied Soft Computing 10, 618-628. doi :
Polito, C.P., Green, R.A., Lee, J., 2008. Pore pressure generation models for sands and silty soils subjected to cyclic loading. Journal of Geotechnical and Geoenvironmental Engineering 134, 1490-1500. doi: https://doi.org/10.1061/(ASCE)1090-0241(2008)134:10 .(1490)
Rehm, B., Schubert, J., Haghshenas, A., Paknejad, A.S., Hughes, J., 2013. Managed pressure drilling. Elsevier .
Sayers, C.M., Johnson, G.M., Denyer, G., 2002. Predrill pore-pressure prediction using seismic data. Geophysics 67, 1286-1292. doi: https://doi.org/10.1190/1.1500391 .
Shi, Y., Wang, C.Y., 1986. Pore pressure generation in sedimentary basins: overloading versus aquathermal. Journal of Geophysical Research: Solid Earth 91, 2153-2162. doi: https://doi.org/10.1029/JB091iB02p02153 .
Stephen, J.E., Kumar, S.S., Jayakumar, J., 2014. Nonlinear modeling of a switched reluctance motor using LSSVM-ABC. Acta Polytechnica Hungarica 11, 143-158. doi :
Suykens, J.A., Vandewalle, J., 1999. Least squares support vector machine classifiers. Neural processing letters 9, 293-300. doi: https://link.springer.com/article/10.1023/A:1018628609742 .
Tsujinishi, D., Abe, S., 2003. Fuzzy least squares support vector machines for multiclass problems. Neural Networks 16, 785-792 .doi: https://doi.org/10.1016/S0893-6080(03)00110-2 .
Vapnik, V., 2013. The nature of statistical learning theory. Springer science & business media .
Yang, X.-S., Papa, J.P., 2016. Bio-inspired computation and applications in image processing. Academic Press .
Yarveicy, H., Moghaddam, A.K., Ghiasi, M.M., 2014. Practical use of statistical learning theory for modeling freezing point depression of electrolyte solutions: LSSVM model. Journal of Natural Gas Science and Engineering 20, 414-421. doi: https://doi.org/10.1016/j.jngse.2014.06.020 .
Yeganeh, M., Masihi, M., Fatholahi, S., 2012. The estimation of formation permeability in a carbonate reservoir using an artificial neural network. Petroleum science and technology 30, 1021-1030. doi: https://www.tandfonline.com/doi/abs/10.1080/10916466.2010.490805 .
Yoshida, C., Ikeda, S., Eaton, B.A., 1996. 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. doi: https://doi.org/10.2118/36381-MS .
Yu, L., Chen, H., Wang, S., Lai, K.K., 2008. Evolving least squares support vector machines for stock market trend mining. IEEE transactions on evolutionary computation 13, 87-102. doi: https://doi.org/10.1109/TEVC.2008.928176 .
Yuan, X., Chen, C., Yuan, Y., Huang, Y., Tan, Q., 2015. Short-term wind power prediction based on LSSVM–GSA model. Energy Conversion and Management 101, 393-401. doi: https://doi.org/10.1016/j.enconman.2015.05.065 .
Zhang, J ,. 2011 . Pore pressure prediction from well logs: Methods, modifications, and new approaches. Earth-Science Reviews 108, 50-63. doi: https://doi.org/10.1016/j.earscirev.2011.06.001 .
Zhang, J., Yin, S., 2017. Real-time pore pressure detection: indicators and improved methods. Geofluids 2017. doi: https://www.hindawi.com/journals/geofluids/2017/3179617 ./
Zheng, H., Liao, R., Grzybowski, S., Yang, L., 2011. Fault diagnosis of power transformers using multi-class least square support vector machines classifiers with particle swarm optimisation. IET Electric Power Applications 5, 691-696. doi: https://doi.org/10.1049/iet-epa.2010.0298.
)
