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Integration of NMR, MICP, and Quantitative Digital Petrography for Improved Understanding of Carbonate Pore System Complexity

Abstract

Carbonate systems develop complex pore structures that impact reservoir performance prediction and well productivity. Although such pore structures are initially controlled by depositional fabrics, modification due to diagenetic alterations often make it difficult to characterize porosity-permeability relationships, pore type distribution away from well control, and association with displacement parameters. One common approach to address such complexity utilizes multiple datasets to generate petrophysical rock types that capture the depositional and diagenetic fabrics responsible for a given pore type, as well as displacement properties including capillary pressure, relative permeability, water saturation, and wettability. One challenge often present when characterizing carbonate rock types is accurately correlating pore types determined from core to wireline logs. Being able to link core data to wireline logs provides a significant uplift in characterizing and distributing the total carbonate pore system and addressing how it impacts fluid flow within a reservoir.

In order to develop a core data set to correlate with wireline logs, we collected conventional core from aquifers of San Salvador Island, Bahamas (Holocene and Pleistocene) and Qatar (Eocene) to compare how varying mineralogy in rocks impacts pore types. By developing an integrated approach that correlates core plug-based nuclear magnetic resonance (NMR), mercury injection capillary pressure (MICP), and quantitative digital petrography (QDP), we are able to demonstrate that a relationship exists between pore volume (NMR), pore throat radii (MICP), and pore area (QDP). These relationships are portable from core measurements to wireline log measurements where appropriate logs are collected and represent a method for core to log calibration of the carbonate pore system. This understanding of the total carbonate pore system should provide key insight on how reservoirs will flow and guide decisions related to field development and reservoir management.