Carbonate Reservoir Cementation Factor Modeling Using Wireline Logs and Artificial Intelligence Methodology

Abstract

An approach, comprising statistical and artificial intelligence techniques, to modeling rock cementation factor in a Saudi Arabian carbonate reservoir using wireline logs is presented. The objective is to obtain a more accurate prediction of rock cementation factor, denoted by the exponent, m, in Archie’s equation, as a variable log using multivariate linear regression (MLR), artificial neural networks, and support vector machines. Published equations by Nugent, Lucia and Shell are empirical derivations based on porosity logs and assumptions that may not be applicable in other geological settings. Typically, log analysts use the average of m values obtained from special core analysis (SCAL) measurements. Such constant values do not account for formation heterogeneity resulting in inaccurate water saturation and pore volume estimation with high operational and economic costs. In this study, six wireline logs from seven wells were combined with their corresponding core measured m values to build and optimize the proposed models to predict the m values for new wells or uncored sections of existing wells. The predicted m values produced by the MLR model closely matched available m data from SCAL measurements. This study fulfills the pressing need for variable m as a more accurate input to water saturation models.