Permeability is one of the most important characteristics of hydrocarbons bearing formations. Formation permeability is often measured in laboratory from cores 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 nonparametric model to predict reservoir permeability from a conventional well logs data using artificial neural network (ANN). The ANN technique is demonstrated with an application to one of Saudi oil fields. This field is the largest offshore oil field in the world and wasdeposited in a fluvial dominated deltaic environment.
The use of conventional regression methods to predictpermeability in this case was not successful. The ANN permeability prediction model was developed from some of the data set consisting of core permeability and well logs data from three early development wells. The ANN model was built and trained from some of the well logs data and their corresponding core measurements by using a back propagation neural network (BPNN). The resulted model was blind tested using data, which was withdrawn from the modeling process. The results of this study show that 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.
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 reflect rock 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 sought, such as the Kozeny-Carmen theory which 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 relates permeability to other logs and/or log-derived parameters such as resistivity and irreducible water saturation [2]. These relationships are applied only either to the region above the transition zone or to the transition zone itself. Since core permeability data are available in most exploration and development wells, statistical methods have become a more versatile alternative in the solution of this problem domain. Therefore, regression is widely used as a statistical method in searching for relationships between core permeability and well logs parameters [3,4].
