A Review of Permeability-Prediction Methods for Carbonate Reservoirs Using Well-Log Data

Abstract

Summary The prediction of permeability in heterogeneous carbonates from well-log data represents a difficult and complex problem. Generally, a simple correlation between permeability and porosity cannot be developed, and other well-log parameters need to be embedded into the correlation. The first part of this paper covers an extensive review of the existing correlations in the literature. The use of porosity and other petrophysical properties of rock in permeability prediction is discussed for carbonaceous rocks. This discussion also covers the usefulness of a wide variety of correlations developed using pore-scale (Kozeny-Carman, percolation, and fractal models) to field-scale models (well logs). In the second part of the paper, a case study is presented. The data are obtained from a complex carbonate field in Oman. Conventional and nonconventional (mainly nuclear magnetic resonance, or NMR) well-log data are evaluated to seek the parameters reflecting a good correlation with permeability. After testing each independent variable against core permeability, the variables yielding the highest correlation coefficient (CC) are included in multiple regression analysis. Data collected from seven wells are used to obtain the permeability correlations for the whole field and for four geological units separately. The test of the correlations is achieved through the comparison of the estimated permeability values to core permeability. Finally, the correlations are compared with the core permeability of the eighth well (data from this well are not included in the development of the correlation) for validation. The correlations are obtained for the four geological units. Two of these units responded well to conventional well-log data; the other two units yielded reasonable correlations only with NMR log data.