Design and Development of An Artificial Neural Network for Estimation of Formation Permeability

Abstract

Summary Permeability is one of the most important characteristics of hydrocarbon-bearing formations and one of the most important pieces of information in the design and management of enhanced recovery operations. With accurate knowledge of permeability, petroleum engineers can manage the production process of a field efficiently. Although formation permeability is often measured in the laboratory from cores or evaluated from well-test data, core analysis and well-test data are only available from a few wells in a field, while the majority of wells are logged. In this study, we have designed an artificial neural network that can accurately predict the permeability of the formations by use of the data provided by geophysical well logs. Artificial neural network, a biologically inspired computing method which has an ability to learn, self-adjust, and be trained, provides a powerful tool in solving pattern recognition problems. In the past, others have attempted to use well logs to predict permeability.1 The problems with previous approaches, however, were two-fold: the limited number of variables (only one variable— porosity) and the use of regression analysis as the main tool for correlations. The approach introduced in this paper attempts to overcome these shortcomings. We do this by first, using many variables from well logs that may provide information about the permeability, and second, recognizing the existence of possible patterns between these variables and formation permeability by use of artificial neural networks. Neural nets are analog, inherently parallel, distributive systems. These characteristics enable artificial neural networks to be successful in predicting the formation permeability in rocks from well-log data.