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

Abstract

Permeability is one of the most important characteristics of hydrocarbon bearing formations. An accurate knowledge of permeability gives petroleum engineers a tool for efficiently managing the production process of a field. It is also one of the most important pieces of information in the design and management of enhanced recovery operations. Formation permeability is often measured in the laboratory from cores or evaluated from well test data. Core analysis and well test data, however, are only available from a few wells in afield. On the other hand, almost all wells are logged. In this study an artificial neural network has been designed that is able to predict the permeability of the formations using the data provided by geophysical well logs with good accuracy. Artificial neural network, a biologically inspired computing method, with its ability to learn, self-adjust, and be trained provide a powerful tool to solve problems that involve pattern recognition. Using well logs to predict permeability has been attempted in the past. The problems with previous approaches were mainly two fold, namely, the number of variables used (only one variable-porosity), and using regression analysis as the main tool for correlations. The approach introduced in this paper is an attempt to overcome these short comings. This is done, first, by using many variables from well logs that may provide information about the permeability. Second, by recognizing the existence of possible patterns between these variables and formation permeability using artificial neural networks. Neuralnets are analog, inherently parallel and distributive systems. These characteristics, which will be discussed in the paper, are the main characteristics that enable artificial neural networks to be successful in predicting the permeability in rocks using well log information. Introduction Acquiring knowledge on formation permeability has remained one of the fundamental challenges to petroleum engineers.

Porous medium permeability estimation for well imagery and characterization using complex resistivity spectra

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Using artificial neural networks to estimate the z-factor for natural hydrocarbon gases

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