Simulating porosity and permeability of the nuclear magnetic resonance (NMR) log in carbonate reservoirs of campos basin, southeastern Brazil, using conventional logs and artificial intelligence approaches

Abstract

We examined, in this study, the artificial intelligence techniques ability in deriving parameters of the Nuclear Magnetic Resonance log, starting from conventional logs. To perform this, it was applied Fuzzy Logic and Artificial Neural Network techniques separately, forming independent schemes. On the other hand, Simple Average and Genetic Algorithm approaches were used to assign weighting factors to Fuzzy Logic and Artificial Neural Network estimates, with the objective to optimize the individual contributions of each one. To do this, the methodology used conventional well logs, that is, gamma ray, resistivity, neutron porosity, density and sonic logs. The wells are in an Albian carbonate reservoir in Campos Basin, Southeastern Brazil. The responses were compared with the Schlumberger free fluid porosity and the lateral permeability, both derived from Nuclear Magnetic Resonance log in the same wells. The results indicate that Artificial Neural Network performed better when compared with Fuzzy Logic, but this last was essential in the success of Simple Average and Genetic Algorithm estimates, which presented better results than these techniques individually. However, each approach showed a good fit with the parameters of the Nuclear Magnetic Resonance log, confirming the utility of the present methodology, in the case when there are only conventional logs, in the studied wells.