Intelligent Prediction of Porosity and Permeability from Well Logs for An Iranian Fractured Carbonate Reservoir

Abstract

One of the most important processes in reservoir engineering is reservoir characterization, in which the reservoir parameters such as porosity and permeability are calculated. These parameters have crucial importance in reservoir engineering computations like reserve estimates and reservoir management. Estimation of porosity and permeability from conventional well logs for uncored well intervals is a good suggestion, but the complexity of the fractured carbonate reservoir makes the application of traditional statistical models totally unreliable. In this article the power of the pattern recognition of artificial neural networks (ANNs) has been applied to develop a transformation map from available most related well logs to rock petrophysical properties of Darquvain reservoir in the southwest of Iran. Comparison of the obtained results illustrates that ANN models can yield more reliable results with respect to traditional models of estimating petrophysical properties. An ANN can be utilized as a flexible and powerful tool for reservoir characterization from available well logs in development projects in the oil and gas industry.