NMR-Based Study of the Pore Types’ Contribution to the Elastic Response of the Reservoir Rock

Seismic data and nuclear magnetic resonance (NMR) are two of the highly trustable kinds information in hydrocarbon reservoir engineering. Reservoir fluids influence elastic wave velocity also determine NMR response reservoir. The current study investigates different pore types, i.e., micro, meso, macropores’ contribution to using laboratory experiments on coal core samples under fluid saturations. Once a meaningful relationship was observed lab, idea applied field scale transverse relaxation time (T2) curves were synthesized artificially. This task done by dividing area T2 curve into eight porosity bins estimating each bin’s value from seismic attributes neural networks (NN). Moreover, functionality statistical ensembles, Bag LSBoost, investigated as an alternative tool conventional estimation techniques petrophysical characteristics; results compared with those deep learning network. Herein, permeability used target benchmark assess reliability models. final indicated that incremental curve, this could be attributes. proved selected ensembles reliable tools characterization reservoirs.