| dc.description.abstract | Carbonate rocks are well known for their highly complex petrophysical behaviors due to their intrinsically heterogeneous pore geometry and wide range of pore sizes. As a result, both effective characterization of carbonate pore systems and the prediction of fluid transport in carbonate reservoirs, remains challenging. This thesis focuses on using nuclear magnetic resonance (NMR) and complex conductivity to quantify carbonate pore structure and gain insights into fluid flow and lithology of carbonate reservoir rocks at the core and log scales. In the laboratory study, integrated NMR and complex conductivity data are used to characterize porosity, pore size distribution, and surface area-to-pore volume ratio, in grainstones, packstones, and mudstones from carbonate reservoirs in Kansas. Carbonate samples with varying pore types and depositional texture are characterized according to NMR porosity, log-mean of transverse relaxation time (T2) value T2ML, real conductivity σ', and imaginary conductivity σ". Widely used petrophysical relationships derived from NMR and complex conductivity data also are assessed, and alternative relationships appropriate for carbonate samples at laboratory scale are proposed. Furthermore, to test the proposed petrophysical relationships at a larger spatial scale, and to exploit the potential of borehole NMR data, this study analyzes NMR well log data from Wellington, KS. This study focus on the uses of NMR longitudinal and transverse relaxation time ratio (T1/T2) in electrofacies characterization. Through multivariate analysis of a suite of logs (e.g., sonic slowness, photoelectric factor, etc.), the results show that T1/T2 ratio is uncorrelated with other logs which makes it a potentially independent indicator for rock typing. The data bear on the accuracy of predicted electrofacies using T1/T2 ratio, and how factors such as lithology and fluid could impact the T1/T2 ratio. Extending beyond experimental observations, this work assesses and proposes new electrical and NMR petrophysical models, analyzes the factors controlling the variation within NMR logging data, and harnesses the complete NMR logging information to improve carbonate reservoir characterization. This work demonstrates the potential of combining NMR and electrical methods to advance understandings of fluid distribution and fluid flow in complex carbonate reservoirs. | |
