Pore-throat structures in a carbonate reservoir were classified into ten petrophysical facies representing coarse, medium, or fine throat types based on Mercury Injection Capillary Pressure (MICP) data from 77 core samples, directly reflecting distinct flow capacities. Using Nuclear Magnetic Resonance (NMR) data from 20 samples, an artificial neural network (ANN) model was developed with four conventional logs, namely Gamma Ray (GR), Deep Laterolog Resistivity (RD), Density (DEN), and Compensated Neutron Log (CNL), as inputs to predict the T2 spectrum continuously. A cumulative pore-throat size distribution matching method was then used to transform predicted T2 spectra into capillary pressure curves. The resulting pore-throat parameters show excellent agreement with core measurements, with relative errors for key parameters—such as median pore-throat radius (R50) and sorting coefficient (Sp)—below 15%. This approach extends discrete core data to continuous wellbore profiles, enabling pore-throat prediction and facies classification in intervals lacking MICP data. It effectively identifies dominant flow channels and tight interlayers, with facies validated by thin-section petrography, providing a robust basis for evaluating highly heterogeneous carbonate reservoirs.
Hou et al. (2026) studied this question.