Application of neural network modelling for classifying hydrocarbon bearing zone, water bearing zone and shale with estimation of petrophysical parameters in Cauvery basin, India

Abstract

This work has been developed to classify sand and shale from seven wells in the Cauvery basin using a multilayered feedforward neural network (MLFN) model. Seven wells distributed over 5100 km2 of this basin have been utilized for analysis of conventional well logs and reservoir characterization. Hydrocarbon bearing sediments of Andimadam, Bhuvanagiri, Nannilam and Niravi formations of the Cauvery basin are evaluated in terms of shaliness, cementation factor, porosity, water saturation, and permeability. Pickett plot has been applied to investigate the cementation factor, formation water resistivity, permeability. The cementation factor (m) varies from 1.31 to 1.86 in these formations, whereas permeability varies from 0.01 to 400 md. Very good quality reservoir exists in the Bhuvanagiri formation with high permeability 300–400 md, whereas a good quality reservoir is occurring in Niravi, Nannilam and Andimadam formations with hydrocarbon saturation 60–70%.