We have developed a methodology that provides permeability estimates for all rock-types or lithologies, for a wide range of permeability. This is a hybrid Genetic Programming and Fuzzy/Neural Net inference system and which utilizes lithologic and permeability facies as indicators. This work was motivated by a need to have a volumetric estimate of permeability for reservoir modeling purposes. To this end, for our purposes, the inputs to this process are limited to properties that can be estimated from seismic data. The permeability transform is first estimated at the well locations using core permeability, elastic parameter logs and porosity. The output from the process can then be used, in conjunction with estimates of these properties from 3D seismic data, to provide an estimate of permeability on a volume basis. The inputs are then, the volume of shale (Vsh) or any other log type used to determine lithology, the sonic and density logs, the porosity log and core permeability measurments. The transform system is composed of three distinct modules. The first module serves to classify lithology and separates the reservoir interval into user-defined lithology types. The second module, based on Genetic Programming, is designed to predict permeability facies within lithology type. A permeability facies is defined as as a low, medium or high permeability set associated with each lithology type. A Fuzzy/Neural Net inference algorithm makes up the third module of the system, in which a TSK fuzzy logic relationship is formed, for each permeability facies and lithology.
The system has been applied in two oil fields, both offshore West Africa. In comparison with current estimation approaches, this system yields more consistent estimated permeability. The results from conducting cross-validation suggest this methodology is robust in estimating permeability in complex heterogeneous reservoirs. This system is designed to use elastic log properties inverted from seismic data, such as acoustic velocity and density as input so permeability volume can be obtained.