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

Abstract

TX 75083-3836, U.S.A., fax +1-972-952-9435. AbstractPermeability 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.

Hybrid intelligent systems in petroleum reservoir characterization and modeling: the journey so far and the challenges ahead

“…In view of the above, combining the limitations of GA, ANN, and fuzzy logic in a hybrid model could only result in a combined overall inefficiency despite their reported good individual performances (Xie et al 2005;Mohsen et al 2007;Ali et al 2008;Zarei et al 2008;Al-Anazi et al 2009;Shahvar et al 2009;Park et al 2010a). Various studies to address these reported problems of ANN through the development of other algorithms such as cascade correlation and radial basis function did not improve its overall performance (Bruen and Yang 2005).…”

“…In the spirit of pseudo-hybridization, Shahvar et al (2009) used fuzzy logic to predict the flow zone index (FZI) of reservoir rocks from wireline logs which served as part of the input for an ANN model for the estimation of permeability. They reported that with the successful prediction of FZI, the result of the permeability estimation was highly satisfactory and more robust when compared with the conventional multi-linear regression.…”

Hybridising Human Judgment, AHP, Grey Theory, and Fuzzy Expert Systems for Candidate Well Selection in Fractured Reservoirs

“…Many operators agree that the candidate well choosing process involves a high level of uncertainty and ambiguity [7,21]. Large scale field applications also show that one main factor leading to prominent dissatisfaction is the systems' inability to handle uncertainty [23,27].…”

“…Each of these approaches carries with it a set of advantages and limitations. Hence, it would seem natural that some attempts have been made to integrate them in order to obtain the best of all approaches [21][22][23][24][25].…”

Neuro-fuzzy reaping of shear wave velocity correlations derived by hybrid genetic algorithm-pattern search technique

“…Hitherto, several researchers have investigated the individual applicability of these tools to geosciences-related problems [10][11][12][13][14][15][16]. This study comprises two major parts.…”