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Prediction of multiphase flow properties from nuclear magnetic resonance imaging

Karaman, Türker
In this study a hybrid Pore Network (PN) model that simulates two-phase (water-oil) drainage and imbibition mechanisms is developed. The developed model produces Nuclear Magnetic Resonance (NMR) T2 relaxation times using correlations available in the literature. The developed PN was calibrated using experimental relative permeability data obtained for Berea Sandstone, Kuzey Marmara Limestone, Yeniköy Dolostone and Dolomitic Limestone core plugs. Pore network body and throat parameters were obtained from serial computerized tomography scans and thin section images. It was observed that pore body and throat sizes were not statistically correlated. It was also observed that the developed PN model can be used to model different displacement mechanisms. By using the synthetic data obtained from PN model, an Artificial Neural Network (ANN) model was developed and tested. It has been observed that the developed ANN tool can be used to estimate oil water relative permeability data very well (with less than 0.05 mean square error) given a T2 signal. It was finally concluded that the developed tools can be used to obtain multiphase flow functions directly from an NMR well log such as Combinable Magnetic Resonance (CMR).