Levenberg-Marquardt algorithm neural network for clay volume estimation from well-log data in an unconventional tight sand gas reservoir of Ahnet basin (Algerian Sahara)

Abstract

The main goal of this paper is to show the contribution of artificial intelligence, namely a neural network, in reservoir characterisation to predict the clay volume in an unconventional tight sand gas reservoir. Clay volume is usually estimated using the natural gamma ray log, which can give bad results if non-clayey radioactive minerals are present in the reservoir. Our purpose is to implement a multilayer perceptron neural network machine to predict the clay volume using the conventional well-log data as an input and the measured mineralogical component, as desired output with a Levenberg-Marquardt algorithm. Application to two Ordovician reservoir intervals of a borehole located in the Ahnet basin in the Algerian Sahara shows the contribution and the efficacy of the implemented neural network machine in unconventional tight sand reservoirs characterisation