A fast and independent architecture of artificial neural network for permeability prediction

Abstract Permeability is one of the most important parameters of the hydrocarbon reservoirs which represent and control the production and flow paths. Different direct and indirect methods try to measure this parameter which most of them, such as core analysis, are very time and cost consuming. Therefore, applying an efficient method which can model this important parameter is necessary. One of these methods which recently have been used frequently is artificial neural networks (ANNs) which have a significant ability to find the complex spatial relationship in the existence parameters of reservoir. Despite all of the applications of ANNs, most of them model the whole reservoir together and one should separate the different domains and use different networks. Also, most of them suffer from not using a priori knowledge or other source of data efficiently. Furthermore, the previous networks when encountering with very large dataset are slow and CPU demanding and they missed their accuracy when a few data are available. Therefore, all of these limitations lead us to use the modularity concept which is browed for biological system to address those problems. Thus, to mitigate these problems, a modular neural network (MNN) is presented. For this aim, one of Iran's oil field which contains three wells was selected for this application. Therefore, different multilayer perceptron and MNN were compared. In other words, the proposed method along four different architectures was used to predict the permeability and the obtained results were compared statistically. According to the obtained results when compared with traditional multilayer perceptron (MLP), this new method is promising very low computational time, the ability to encounter with complex problems, high learning capacity and affordability for most of the applications. The results show that the R 2 was improved from 0.94 to 0.99 for MLP and MNN networks, respectively.