APPLICATION OF NEURAL NETWORKS TO THE EVALUATION OF RESERVOIR QUALITY IN A LITHOLOGICALLY COMPLEX FORMATION

Abstract

Neural networks are non-algorithmic, analog, distributive and massively parallel information processing systems that have a number of performance characteristics in common with biological neural networks or the human brain. Neural networks can simulate the nervous systems of living animals which work differently from conventional computing, to analyse, compute and solve some complex practical problems making use of computers. Neural networks are able to discover highly complex relationships between several variables that are presented to the network. Studies show that neural networks can be used to solve a great number of practical problems which occur in modeling, predictions, assessments, recognition and image processing. In particular, neural networks are suitable for application to problems where some results are known but the manner in which these results can be achieved are not known (or are difficult to implement) or the results themselves are not known. An important challenge for geologists, geophysicists and reservoir engineers is to accurately determine petrophysical parameters and to improve reservoir evaluation and description. It is important to be able to obtain realistic values of petrophysical parameters from well logs because core data are often not available either because of bore hole conditions or due to the high cost of coring. In lithologically complex formations conventional petrophysical evaluation methods cannot be used because of the lithological heterogeneity. This paper presents an application of neural networking to estimate petrophysical parameters from well logs and to evaluate reservoir quality in the Mardie Greensand in the Carnarvon Basin in Western Australia.