Artificial neural networks for predicting petroleum quality

Abstract

Due to limited understanding of many diagenetic processes which contributes to petroleum quality determination, mathematical models become a very useful tool to improve understanding of these processes and to improve reservoir quality predictions prior drilling. Especially for reservoir engineers and petrophysicists the distribution of porosity and permeability are very important in the formation evaluation and definition of recovery strategies and evaluation of reservoir quality. In this context, we have developed an artificial neural network based model to predict macroporosity of sandstones reservoir systems. We have used a score to quantify the importance of each feature in prediction process. This score allows creating progressive enhancement neural models, which are simpler and more accurate than conventional neural network models and multiple regressions. The main contribution of this paper is the building of a reduced model just with the most relevant features to macroporosity prediction. A dataset, containing petrographic and petrophysical characteristics, containing samples of the same formation sandstone reservoir was investigated. Study results show that progressive enhancement neural network is able to predict macroporosity with accuracy near 90%, suggesting that this technique is a valuable tool for reservoir quality prediction.

Key words: progressive enhancement neural model, reservoir quality prediction, macroporosity prediction.