The application of machine learning models such as support vector machine (SVM) and artificial neural networks (ANN) in predicting reservoir properties has been effective in the recent years when compared with the traditional empirical methods. Despite that the machine learning models suffer a lot in the faces of uncertain data which is common characteristics of well log dataset. The reason for uncertainty in well log dataset includes a missing scale, data interpretation and measurement error problems. Feature Selection aimed at selecting feature subset that is relevant to the predicting property. In this paper a feature selection based on mutual information criterion is proposed, the strong point of this method relies on the choice of threshold based on statistically sound criterion for the typical greedy feedforward method of feature selection. Experimental results indicate that the proposed method is capable of improving the performance of the machine learning models in terms of prediction accuracy and reduction in training time.

Computational intelligence (CI) techniques have positively impacted the petroleum reservoir characterization and modeling landscape. However, studies have showed that each CI technique has its strengths and weaknesses. Some of the techniques have the ability to handle datasets of high dimensionality and fast in execution, while others are limited in their ability to handle uncertainties, difficult to learn, and could not deal with datasets of high or low dimensionality. The “no free lunch” theorem also gives credence to this problem as it postulates that no technique or method can be applicable to all problems in all situations. A technique that worked well on a problem may not perform well in another problem domain just as a technique that was written off on one problem may be promising with another. There was the need for robust techniques that will make the best use of the strengths to overcome the weaknesses while producing the best results. The machine learning concepts of hybrid intelligent system (HIS) have been proposed to partly overcome this problem. In this review paper, the impact of HIS on the petroleum reservoir characterization process is enumerated, analyzed, and extensively discussed. It was concluded that HIS has huge potentials in the improvement of petroleum reservoir property predictions resulting in improved exploration, more efficient exploitation, increased production, and more effective management of energy resources. Lastly, a number of yet-to-be-explored hybrid possibilities were recommended.