This paper reports a comprehensive study towards quantitative characterization of the permeability distribution in complex Mauddud-Burgan reservoir in Kuwait. The main objective in this study is to develop a generalized strategy for data-mining a large data-set of rock measurements. This study utilizes measurements of petrophysical and grain/ pore morphology properties in order to correlate permeability. Data-set contain measurements obtained from different length scales, ranging from SEM to wire-line log scale. Characterizing the permeability for the Mauddud-Burgan reservoir is a challenge because of the complexity of this reservoir. The process is dependant on the type of data available for the reservoir. This study strives toward comprehensive data mining to understand the permeability of this complex reservoir. A Multiple-Layer Feed Forward, MLFF, with back propagation neural network is developed to calculate the permeability at each desired vertical depth in the reservoir. This tool can assist in determining permeability at any vertical depth of the reservoir, within the boundaries of the reservoir model. Knowledge of other petrophysical properties, such as, porosity, pore type, and pore size distribution, as available, are integrated to estimate the permeability.
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