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Permeability Estimation From Well Log Responses

By E. M. El-M. Shokir, A. A. Alsughayer and A. Al-Ateeq

Abstract

Permeability is one of the most important characteristics of hydrocarbon bearing formations. Formation permeability is often measured in the laboratory from reservoir core samples or evaluated from well test data. However, core analysis and well test data are usually only available from a few wells in a field. On the other hand, almost all wells are logged. This paper presents a non-parametric model to predict reservoir permeability from conventional well log data using an artificial neural network (ANN). The ANN technique is demonstrated by applying it to one of Saudi Arabia’s oil fields. The field is the largest offshore oil field in the world and was deposited in a fluvial dominated deltaic environment. The use of conventional regression methods to predict permeability in this case was not successful. The ANN permeability prediction model was developed using some of the core permeability and well log data from three early development wells. The ANN model was built and trained from the well log data and their corresponding core measurements by using a back propagation neural network (BPNN). The resulting model was blind tested using data which was taken from the modelling process. The results of this study show that the ANN model permeability predictions are consistent with actual core data. It could be concluded that the ANN model is a powerful tool for permeability prediction from well log data

Year: 2006
OAI identifier: oai:CiteSeerX.psu:10.1.1.135.1795
Provided by: CiteSeerX
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