Abstract
ABSTRACTThis work highlights the application of an adaptive neuro-fuzzy inference system (ANFIS) for predictions of NMR log parameters including free flowing porosity (FFP) and permeability by using field log data. The input parameters of model were neutron porosity, sonic transit time, bulk density, and electrical resistivity. The outputs of model were also permeability and FFP values. The ANFIS model was trained by using hybrid method. Results showed that the developed model is effective in prediction of field NMR log data. Outcomes of this study can be used in areas of petroleum engineering where accurate and immediate predictions of logging data are required.
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