Artificial-Intelligence Technology Predicts Relative Permeability of Giant Carbonate Reservoirs

Abstract

Summary Determination of relative permeability data is required for almost all calculations of fluid flow in petroleum reservoirs. Water/oil relative permeability data play important roles in characterizing the simultaneous two-phase flow in porous rocks and in predicting the performance of immiscible displacement processes in oil reservoirs. They are used, among other applications, for determining fluid distributions and residual saturations, predicting future reservoir performance, and estimating ultimate recovery. Undoubtedly, these data are considered probably the most valuable information required in reservoir-simulation studies. Estimates of relative permeability are generally obtained from laboratory experiments with reservoir-core samples. In the absence of the laboratory measurement of relative permeability data, developing empirical correlations for obtaining accurate estimates of relative permeability data showed limited success, and it proved difficult, especially for carbonate reservoir rocks. Artificial-neural-network (ANN) technology has proved successful and useful in solving complex structured and nonlinear problems. This paper presents a new modeling technology to predict accurately water/oil relative permeability using ANNs. The ANN models of relative permeability were developed using experimental data from waterflood-core-tests samples collected from carbonate reservoirs of giant Saudi Arabian oil fields. Three groups of data sets were used for training, verification, and testing the ANN models. Analysis of results of the testing data set shows excellent agreement with the experimental relative permeability data. In addition, error analyses show that the ANN models developed in this study outperform all published correlations. The benefits of this work include meeting the increased demand for conducting special core analysis (SCA), optimizing the number of laboratory measurements, integrating into reservoirsimulation and reservoir-management studies, and providing significant cost savings on extensive laboratory work and substantial required time.