Permeability Determination Using Neural Networks in the Ravva Field, Offshore India

Abstract

Summary This paper describes the use of the backpropagation neural network (BPNN) technique to predict reservoir permeability using conventional well log data. The technique is demonstrated with an application to the Ravva oil and gas field, offshore India. The Ravva field reservoirs are middle Miocene age nearshore marine sandstones that are often laminated to thinly interbedded shale. The use of conventional permeability-porosity crossplots to predict permeability in this field was not successful. The BPNN permeability prediction model ("RAVVANET") was developed from a data set consisting of core permeability and well log data from two early development wells. The model was blind tested with data from a third well, which was withheld from the modeling process. The results of this study show that BPNN model permeability predictions are consistent with core analysis results.