عنوان مقاله [English]
Permeability, porosity and sedimentary facies are the main factors of reservoir characteristics. Porosity indicates the ability of a rock to store fluids. So far, many approaches including linear / nonlinear regressions have been developed to predict porosity. Neural networks have received a lot of attention in recent years, and various types of learning machines based on neural networks have been introduced. Multilayer perceptron neural network (MLP) is one of these networks that proven its ability, but each of these methods has disadvantages. In this research, the support vector machine (SVM) method has been used as the main method for regression and estimation of the reservoir porosity in one of the hydrocarbon reservoirs. This method has been compared with the multilayer perceptron method and the results of each have been investigated.
The best way to get accurate values of physical properties of reservoir is to measure them directly in the laboratory. However, this method has disadvantages: high cost, time consuming, lack of access to the entire depth of the well. For these reasons, geologists extract core from a number of wells and from a specific range. Geologists generally use a statistical approach involving multiple linear or nonlinear regressions to relate reservoir characteristics to each other (eg, porosity and permeability). In these contexts, a linear or non-linear relationship is assumed between porosity and other reservoir characteristics. However, these techniques are insufficient for certain issues, such as heterogeneous reservoirs. Recently, geoscientists have used artificial intelligence (AI) methods, especially neural networks (NNs), to predict reservoir parameters. Neural networks have been widely used in various fields of science and engineering.
To build a three-dimensional model of a reservoir, a thorough knowledge of permeability, porosity and sedimentary facies is required. Well logs and core information are local measurements that do not reflect the behavior of the reservoir as a whole. In addition, well information does not cover the entire field area, while 3D seismic information covers a larger area. Changes in lithology and fluids cause changes in amplitude, wavelet shape, coherence coefficient, and other seismic attributes. These attributes can provide information for building a repository model.
The main purpose of this research is to analyze training machines developed by computer scientists to predict reservoir characteristics such as porosity in vertical and lateral directions with the help of well logs and seismic attributes. The aim is to achieve the following steps to estimate a reliable porosity model of the reservoir:
Development of a multilayer perceptron (MLP) to estimate the porosity using well logs.
Development of a support vector machine (SVM) to estimate the porosity using well logs.
Comparing the proposed methods and choosing the best.
Estimation of porosity based on seismic attributes using the selected algorithm.
Making a three-dimensional model of the reservoir porosity based on the training machine.
As it was expected, these computational intelligence approaches overcome the weakness of the standard regression techniques. Generally, the results show that the performances of Support Vector Machine outperform that Multilayer Perceptron neural networks. In addition, Support Vector Regression (SVR) is more robust, easier and quicker to train. Therefore, it could be concluded that the use of SVM technique will be valuable and powerful for geoscientists to model the reservoir properties.