Estimation of NMR log parameters from conventional well log data using a committee machine with intelligent systems: A case study from the Iranian part of the South Pars gas field, Persian Gulf Basin

Nuclear Magnetic Resonance (NMR) log provides useful information for petrophysical study of the hydrocarbon bearing intervals. Free fluid porosity (effective porosity), rock permeability and bound fluid volume (BFV) could be obtained by processing and interpretation of NMR data. The present study proposes an improved strategy to make a quantitative correlation between the NMR log parameters and conventional well logs by integration of different intelligent systems using the concept of committee machine. The proposed committee machine with intelligent systems (CMIS) combines the results of Fuzzy Logic (FL), Neuro-Fuzzy (NF) and Neural Network (NN) algorithms for overall estimation of the NMR log parameters from conventional well log data. It assigns a weight factor to each of the individual intelligent algorithms showing its contribution in overall prediction. The weight factors are derived in two ways: simple averaging and weighted averaging. In the weighted averaging method a genetic algorithm (GA) was employed to obtain the optimal contribution of each algorithm in construction of the CMIS. The proposed methodology was applied to the South Pars gas field, Persian Gulf Basin. The petrophysical logs from two wells were used for constructing the intelligent models and a third well from the field was used to evaluate the reliability of the developed models. The results indicate the higher performance of the GA optimized model over the individual intelligent systems performing alone.