An Intelligent Model for Estimating Relative Permeability in the Abu-Sennan Oil and Gas Fields, Southwestern Egypt

Abstract

Permeability is arguably the most critical vector flow parameter used in the analysis of hydrocarbon formations. Permeability data are usually obtained from well shut-in tests and core investigations; however, only a small number of wells have well tests or core measurements. In contrast, well logs are often available for most wells in a field. Therefore, techniques that evaluate relative permeability using well logs can be extremely useful. To this end, an effective and thorough model composed of a radial base function neural network has been constructed to predict the relative permeability of formations within the Abu-Sennan Oil and Gas Fields. A total of 105 previously reported relative permeability core data points scattered along the Abu-Sennan Fields were used to construct and evaluate the proposed model. Input parameters for the neural network were wettability, water saturation, irreducible water saturation, porosity and sample depth. The results of the proposed model were compared to reported field data. The results illustrate that the proposed model is able to predict the relative permeability of specific units within the Abu-Sennan Fields with a high correlation coefficient for unnamed data in the model. The proposed model was assessed using sensitivity analysis based on the input parameters.