Contents lists available at ScienceDirect Journal of Petroleum Science and Engineering journal homepage: www.elsevier.com/locate/petrol A hybrid particle swarm optimization and support vector regression model for modelling permeability prediction of hydrocarbon reservoir Kabiru. O. Akande a, ⁎ , Taoreed. O. Owolabi b,e , Sunday. O. Olatunji c , AbdulAzeez AbdulRaheem d a Institute for Digital Communications, School of Engineering, University of Edinburgh, United Kingdom b Physics Department, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia c Computer Science Department, University of Dammam, Dammam, Saudi Arabia d Petroleum Engineering Department, King Fahd University of Petroleum and Minerals (KFUPM), Saudi Arabia e Physics and Electronics Department, Adekunle Ajasin University, Akungba Akoko, Ondo State, Nigeria ARTICLE INFO Keywords: Particle swarm optimization (PSO) Support vector regression (SVR) Hydrocarbon reservoir Permeability prediction Optimization techniques ABSTRACT The significance of accurate permeability prediction cannot be over-emphasized in oil and gas reservoir characterization. Support vector machine regression (SVR), a computational intelligence technique, has been very successful in the estimation of permeability and has been widely deployed due to its unique features. However, careful selection of SVR hyper-parameters is highly essential to its optimum performance and this task is traditionally done using trial and error approach (TE-SVR) which takes a lot of time and do not guarantee optimal selection of the hyper-parameters. In this work, the performance of particle swarm optimization (PSO) technique, a heuristic optimization technique, is investigated for the optimal selection of SVR hyper-parameters for the first time in modelling and characterization of hydrocarbon reservoir. The technique is capable of automatic selection of the optimum combination of SVR hyper-parameters resulting in higher predictive accuracy and generalization ability of the developed model. The resulting PSO-SVR model is compared to SVR models whose parameters are obtained through random search (RAND-SVR) and trial and error approach (TE- SVR). The comparison is done using real-life industrial datasets obtained during petroleum exploration from four distinct oil wells located in a Middle Eastern oil and gas field. Simulation results indicate that the PSO-SVR model outperforms all the other models. Error reduction of 15.1%, 26.15%, 12.32% and 7.1% are recorded for PSO-SVR model compared to ordinary SVR (TE-SVR) in well-A, well-B, well-C and well-D, respectively. Also, reduction of 12.8%, 23.97%, 2.51% and 0.11 are recorded when PSO-SVR and RAND-SVR results are compared in the respective wells. Furthermore, the results show the potential of the application of heuristics algorithms, such as PSO, in the optimization of computational intelligence techniques employed in hydrocarbon reservoir characterizations. Therefore, PSO technique is proposed for the optimization of SVR hyper-parameters in permeability prediction and reservoir characterization based on its superior performance over the commonly employed optimization techniques. 1. Introduction Permeability is defined as the ease of movement of oil and gas through a porous rock (Olatunji et al., 2014). It is a very important property in reservoir characterization and its accurate prediction is essential to a successful oil and gas exploration. Several decisions regarding the overall management of oil and gas reservoir are made based on the knowledge of permeability. Information such as the scale of the oil and gas present in the reservoir, the amount of recoverable oil, flow rate of the medium, estimate of future exploration and the various exploration equipment and techniques to be employed during the drilling process are supplied based on accurate prediction of permeability (Akande et al., 2015; Tusiani and Shearer, 2007). It is not sufficient to have oil or gas in the reservoir or formation, the so called ‘oil in place’. Rather, what is paramount is for these hydrocarbons to be able to flow from the formation to the well bore so as to be recoverable at the surface. Permeability, defined as the ease with which fluids flow through the rock, determines this flow rate. Hence, permeability determines the recoverable reserves (amount of recoverable hydrocarbons) from the reservoir volume (oil in place). This makes permeability one of the most important flow characteriza- tions of oil and gas reservoir whose accurate determination is very vital http://dx.doi.org/10.1016/j.petrol.2016.11.033 Received 17 May 2016; Received in revised form 18 October 2016; Accepted 24 November 2016 ⁎ Corresponding author. E-mail addresses: koakande@gmail.com (K.O. Akande), owolabitaoreedolakunle@gmail.com (T.O. Owolabi), oluolatunji.aadam@gmail.com (S.O. Olatunji), aazeez@kfupm.edu.sa (A. AbdulRaheem). Journal of Petroleum Science and Engineering xx (xxxx) xxxx–xxxx 0920-4105/ © 2016 Published by Elsevier B.V. Available online xxxx Please cite this article as: Akande, K.O., Journal of Petroleum Science and Engineering (2016), http://dx.doi.org/10.1016/j.petrol.2016.11.033