A Comprehensive Review of Soft Computing Models for Permeability Prediction

Crude oil is a vital and valuable commodity in the energy industry. In order to maintain continuous, stable, and reasonably priced supplies, oil producers need cheaper exploration and extraction techniques. Permeability is one of the formation parameters that is a key interest to petroleum engineers in determining the economic worth and yield of crude deposits, yet permeability prediction remains a difficult problem. Many approaches have been applied to solving this important issue. Soft computing has been deployed to predict permeability. In this paper, we present an extensive review of the existing research that has been conducted on applications of soft computing for permeability prediction. This paper finds out that traditional approaches for permeability prediction are still relevant in the oil and gas industry. Soft computing methods in particular are worthy of addition to this interesting area. This extensive review is intended to be an entry point for further exploration of other approaches that have received little or no attention from researchers.