Artificial Intelligence Technology Predicts Relative Permeability of Giant Carbonate Reservoirs

Saud M. Al-Fattah, SPE, and Hamad A. Al-Naim, SPE, Saudi AramcoSummaryDetermination of relative permeability data is required for almostall calculations of fluid flow in petroleum reservoirs. Water/oilrelative permeability data play important roles in characterizingthe simultaneous two-phase flow in porous rocks and in predictingthe performance of immiscible displacement processes in oilreservoirs. They are used, among other applications, for determin-ing fluid distributions and residual saturations, predicting futurereservoir performance, and estimating ultimate recovery. Un-doubtedly, these data are considered probably the most valuableinformation required in reservoir-simulation studies. Estimatesof relative permeability are generally obtained from laboratoryexperiments with reservoir-core samples. In the absence of thelaboratory measurement of relative permeability data, developingempirical correlations for obtaining accurate estimates of relativepermeability data showed limited success, and it proved difficult,especially for carbonate reservoir rocks.Artificial-neural-network (ANN) technology has proved suc-cessful and useful in solving complex structured and nonlinearproblems. This paper presents a new modeling technology topredict accurately water/oil relative permeability using ANNs.The ANN models of relative permeability were developed usingexperimental data from waterflood-core-tests samples collectedfrom carbonate reservoirs of giant Saudi Arabian oil fields. Threegroups of data sets were used for training, verification, and testingthe ANN models. Analysis of results of the testing data set showsexcellent agreement with the experimental relative permeabilitydata. In addition, error analyses show that the ANN models devel-oped in this study outperform all published correlations.The benefits of this work include meeting the increased de-mand for conducting special core analysis (SCA), optimizing thenumber of laboratory measurements, integrating into reservoir-simulation and reservoir-management studies, and providingsignificant cost savings on extensive laboratory work and substan-tial required time.IntroductionANNs have seen a great increase of interest during the pastfew years. They are powerful and useful tools for solvingpractical problems in the petroleum industry (Mohaghegh2005; Al-Fattah and Startzman 2003). Advantages of neuralnetwork techniques (Bishop 1995; Fausett 1994; Haykin 1994;Patterson 1996) over conventional techniques include the abilityto address highly nonlinear relationships, independence fromassumptions about the distribution of input or output variables,and the ability to address either continuous or categorical dataas either inputs or outputs. In addition, neural networks areintuitively appealing because they are based on crude low-levelmodels of biological systems. Neural networks, as in biologicalsystems, simply learn by examples. The neural-network userprovides representative data and trains the neural networks tolearn the behavior of the data.Design and Development of ANN ModelsIn regression problems, the objective is to estimate the value of acontinuous variable given the known input variables. Regression pro-blems can be solved using the following network types: multilayerperceptrons (MLPs), radial basis function (RBF), generalized-regres-sionneuralnetwork(GRNN),andlinear.Inthisstudy,weexperimen-ted with the first three types: MLP, RBF, and GRNN. The linearmodel is basically the conventional linear-regression analysis. Sincethe problem at hand is a regression type and because of its power andadvantages, we found that GRNN performsthebest forthisparticularstudy. This is because GRNN has several advantages.