The Application of Fuzzy Logic and Genetic Algorithms to Reservoir Characterization and Modeling

Abstract

A 3D model of oil and gas fields is important for reserves estimation, for cost effective well placing and for input into reservoir simulators. Reservoir characterization of permeability, litho-facies and other properties of the rocks is essential. A good model depends on calibration at the well locations, with cored wells providing the best data. A subset of wells may contain specialized information such as shear velocity data, whereas other wells may contain only basic logs. We have developed techniques able to populate the entire field database with a complete set of log and core data using fuzzy Logic, genetic algorithms and hybrid models. Once the gaps in the well database have been filled, well logs can be imported to a 3D modeling software package, blocked and upscaled to match the geocellular model cell size.

Litho-facies typing and permeability are important for understanding sedimentological controls on reservoir quality distribution as well an input to 3D reservoir models. Litho-facies and permeability prediction have presented a challenge due to the lack of borehole tools that measure them directly. We demonstrate, using several field examples, how these new predictive methods can be applied in a variety of ways to enhance the understanding of rock physical properties. Examples include prediction of litho-facies, permeability and shear sonic logs. The new techniques give better predictions compared to conventional methods such as multiple linear regression and cluster analysis.