Reservoir rock permeability prediction using support vector regression in an Iranian oil field

Abstract

Reservoir permeability is a critical parameter for the evaluation of hydrocarbon reservoirs. It is often measured in the laboratory from reservoir core samples or evaluated from well test data. The prediction of reservoir rock permeability utilizing well log data is important because the core analysis and well test data are usually only available from a few wells in a field and have high coring and laboratory analysis costs. Since most wells are logged, the common practice is to estimate permeability from logs using correlation equations developed from limited core data; however, these correlation formulae are not universally applicable. Recently, support vector machines (SVMs) have been proposed as a new intelligence technique for both regression and classification tasks. The theory has a strong mathematical foundation for dependence estimation and predictive learning from finite data sets. The ultimate test for any technique that bears the claim of permeability prediction from well log data is the accurate and verifiable prediction of permeability for wells where only the well log data are available. The main goal of this paper is to develop the SVM method to obtain reservoir rock permeability based on well log data.