HETEROGENOUS DISTRIBUTION OF INITIAL WATER SATURATION USING ARTIFICIAL NEURAL NETWORKS

Abstract

Artificial neuronal networks (ANNs) are rapidly becoming a very useful tool in the petroleum industry for predicting different evolution types for different parameters and use the human nervous system principles in creating the required prediction algorithm. Main objective of paper is to use a feedforward neural network to estimate the distribution of initial water saturation, as a small part of reservoir characterization in the presence of heterogeneities. It is very known that ANNs are complicated structures, take a long time in programing, are computer-time consuming and often require specialized aid in using them. Therefore, it will be an asset to know if reservoir heterogeneities can be pointed out with ANNs, or other prediction methods are indicated for these cases.

Keywords

• modelling
• artificial neural networks
• water distribution