Intelligent Integration of Neutron, Density and Gamma Ray Data for Subsurface Characterization

Earth subsurface recognition through describing the underground layers can be carried out using physical measurements to obtain clearer and more accurate subsurface model. This can be conducted applying well logging method. Among various information that can be provided by different well logging sensors, neutron, density and gamma-ray data are the most considerable in subsurface type determination. However, current analysis of these data stills a subjective task which adds a variable confidence interval to the obtained results. In this study, a real time decision level fusion system uses fuzzy logic approach is introduced to incorporate neutron, density, and gamma-ray information in order to provide more specific interpretation of the subsurface structures. Results of the proposed approach agreed well with the results of an offline subsurface determination program, with an average ratio of about 89.75%. The suggested approach was evaluated against real data from eight wells, and the results were promising to yield more objective interpretation.