Genetic implanted fuzzy model for water saturation determination

• In this study, conventional well logs were formulated to water saturation.
• Fuzzy logic (FL) structure was used for modeling aforementioned formulation.
• To improve FL, genetic algorithm was implanted instead of subtractive clustering.
• Results showed GA performed better than SC in extracting fuzzy rules and clusters.

The portion of rock pore volume occupied with non-hydrocarbon fluids is called water saturation, which plays a significant role in reservoir description and management. Accurate water saturation, directly measured from special core analysis is highly expensive and time consuming. Furthermore, indirect measurements of water saturation from well log interpretation such as empirical correlations or statistical methods do not provide satisfying results. Recent works showed that fuzzy logic is a robust tool for handling geosciences problems which provide more reliable results compared with empirical correlations or statistical methods. This study goes further to improve fuzzy logic for enhancing accuracy of final prediction. It employs hybrid genetic algorithm-pattern search technique instead of widely held subtractive clustering approach for setting up fuzzy rules and for extracting optimal parameters involved in computational structure of fuzzy model. The proposed strategy, called genetic implanted fuzzy model, was used to formulate conventional well log data, including sonic transit time, neutron porosity, formation bulk density, true resistivity, and gamma ray into water saturation, obtained from subtractive clustering approach. Results indicated genetic implanted fuzzy model performed more satisfyingly compared with traditional fuzzy logic model. The propounded model was successfully applied to one of Iranian carbonate reservoir rocks.