A hybrid particle swarm optimization and support vector regression model for modelling permeability prediction of hydrocarbon reservoir

Research paper by Kabiru. O. Akande, Taoreed. O. Owolabi, Sunday. O. Olatunji, AbdulAzeez AbdulRaheem

Indexed on: 26 Nov '16Published on: 25 Nov '16Published in: Journal of Petroleum Science and Engineering


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.

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