Incorporating Fuzzy Logic and Artificial Neural Networks for building a Hydraulic Unit-Based Model for Permeability Prediction of a Heterogeneous Carbonate Reservoir

Abstract

Abstract Permeability is one of the crucial parameters in dynamic reservoir modeling and simulation. Direct measurement of permeability through coring and wireline formation testing is expensive and sometimes hard to achieve. In addition, the interval of coring is always limited. In this study available core and wireline log data of a heterogeneous carbonate reservoir located in sarvak formation of Iran are used to predict permeability not only for the cored but un-cored wells too. To reach this purpose, the concept of rock typing has been taken into consideration to overcome the problem of heterogeneity. Flow zone index (FZI) approach is selected to determine the rock types of the reservoir understudy. Mathematical manipulation is then used to transform the continuous FZI values to discrete ones known as discrete rock types (DRT). Wireline log data corresponde to each DRT are individualized and subjected to statistical analysis to find their influence on the process of permeability prediction. Gamma ray, sonic, density and neutron porosity logs have been chosen as input parameters for building artificial neural network (ANN) models for permeability prediction. An individual ANN model is constructed for the process of estimating the permeability for each DRT. The result of permeability prediction using this technique is highly satisfactory but dependent on the successful prediction of FZI in uncored intervals/wells. Fuzzy logic is the approach that was used for estimating the FZI by wireline logs data. Applying fuzzy logic provided accurate predicted FZI logs for uncored wells. By deriving DRTs from the FZI log, relevant built ANN models for each DRT might be used for predicting permeability. Validation of the predictive capability of the method in two cored wells (Blind-test wells) proved the estimation technique to be robust. For the sake of comparison, permeability-effective porosity transform and multilinear regression are applied for permeability prediction of the reservoir understudy. Results of applying these methods are considerably less than the results achived in this work. Introduction Permeability is one of the crucial parameters in dynamic reservoir modeling and simulation. Direct measurement of permeability through coring and wireline formation testing is expensive and sometimes fail to achieve [1]. Also these measuremens do not provide a continuous profile of permeability along the well and continuous permeability values need to be predcited indirectly from independent data. In recent years, different methodologies have been introduced to the petroleum geosciences/engineering discipline to predict permeability from openhole logs. Most of these methods are based on using artificial intelligence techniques such as artificial neural networks (ANN) and fuzzy logic. These approaches could enhance the permeability prediction, but in the presence of heterogeneity the degree of accuracy and certainty of using these methods reduces too. Heterogeneity is a problem that occurs in most of the carbonate reservoirs. Due to the chemical nature of carbonates, some secondary diagenism processes such as acidic waters may cause dissolution in these systems and forms fractures and vuggs. Existence of fractures and vuggs increases the permeability to large amounts while does not have considerable effect on porosity values and therefore causes irregularties in the relationship between porosity and permeability. This fact can be obsereved on conventional cross plots of porosity-permeability of heterogeneous reservoirs where high values of permeability are corresponded to low or medium values of porosity.