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. Introduction Many oil reservoirs have heterogeneity in rock properties. Understanding the form and spatial distribution of these heterogeneities is fundamental to the successful exploitation of these reservoirs. Permeability is one of the fundamental rock properties, which reflects the rock's ability to transmit fluids when subjected to pressure gradients. While this property is very important in reservoir engineering, there is no specific geophysical well log for permeability, and its determination from conventional log analysis is often unsatisfactory(1). In general, porosity and permeability are independent properties of a reservoir. However, permeability is low if porosity is disconnected, whereas permeability is high when porosity is interconnected and effective. Despite this observation, theoretical relationships between permeability and porosity have been formulated, such as the Kozeny-Carmen theory. The Kozeny-Carmen theory relates permeability to porosity and specific surface area of a porous rock which is treated as an idealized bundle of capillary tubes. This theory, however, ignores the influence of conical flow in the constrictions and expansions of the flow channels and treats the highly complex porous medium in a very simple manner. Empirical relationships based on the Kozeny-Carmen theory have also been developed that relate permeability to other logs and/or log-derived parameters such as resistivity and irreducible water saturation(2). These relationships are applied only to the region above the transition zone or to the transition zone itself. Since core permeability data are available for most exploration and development wells, statistical methods have become a more versatile alternative in solving the problem of determining reservoir permeability. Regression is widely used as a statistical method to search for relationships between core permeability and well log parameters(3, 4).

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