Prediction of Porosity and Permeability of Oil and Gas Reservoirs Using Hybrid Computational Intelligence Models

Abstract

Abstract This work utilizes the capabilities of Data Mining and Computational Intelligence in the prediction of two important petroleum reservoir characteristics, viz., porosity and permeability, based on the hybridization of three existing Artificial Intelligence techniques: Fuzzy Logic, Support Vector Machines, and Functional Networks, using several real-life well-log data. Two hybrid models have been built. In both, Functional Networks was used to select the best input variables for training directly from data using its functional approximation capability. In the first model, the selected input variables were passed to Type-2 Fuzzy Logic System to handle uncertainties, if any exists. These are then passed to Support Vector Machines for training and making final predictions using the test data. In the second model, the input variables selected by Functional Networks were passed to Support Vector Machines to transform them to a higher dimension, called a feature space, and then to Type-2 Fuzzy Logic to handle uncertainties, if any exists, extract inference rules and make final predictions using the test data. The results showed that the hybrid models perform better with higher correlation coefficients than the individual techniques when used alone for the same sets of data. This is a further confirmation of the theory that hybrids perform better than their individual components and possess the best qualities of each of the components. In terms of execution time, the hybrid models took less time for both training and testing than the Type-2 Fuzzy Logic, but more time than Functional Networks and Support Vector Machines. This could be the price for having a better and more robust model. The process of combining multiple computational intelligence techniques to build a single hybrid model has become increasingly popular. As reported in the literature, the performance indices of these hybrid models have proved to be better than their individual components when used alone. Hybrid models are extremely useful in the field of reservoir characterization in petroleum engineering which requires a high quality information and accurate prediction for efficient exploration and management of oil and gas resources.