Enhanced Lithofacies Description and Petrophysical Rock Typing in Fluvial Sand Reservoirs Through Core-Log Data Integration: Case Study of the Gharif Reservoir, Southern Central Oman

Abstract Lithofacies description and petrophysical rock typing (RRTs) are critical in reservoir characterization as they provide an understanding of the spatial distribution of rock types and their petrophysical properties within the reservoir. Reservoir rock typing is a fundamental characterization element for integrated reservoir evaluation. RRTs are typically described at core, and then tied to logs and in some cases may be constrained with conceptual geologic models. The integration of core and log data is a robust approach in achieving comprehensive reservoir characterization. This study delves into this integration for a non-marine fluvial reservoir, aiming to furnish a detailed lithofacies description and establish petrophysical rock types. The study initiates with core lithofacies description, wherein 428 Laser Particle Size Analysis (LPSA), Thin Sections, and 421 X-Ray Diffraction (XRD) analyses contribute to defining eleven lithofacies within the core domain. Subsequently, the core data are integrated into Petrophysical Rock types (PRTs) through the examination of various electrical log combinations. This integration process involves grouping core-based resolution lithofacies to log scale studying log response and lithofacies similarity to generate PRTs. The outcome typically involves a reduction from core lithofacies to a more limited number of PRTs resolvable at the log scale. A proprietary supervised and unsupervised machine learning guided cluster analysis process is used to generate and calibrate the PRTs to core data. The output is a continuous log scale facies model solely using electrical log inputs for prediction (Gamma Ray Thorium /THOR, Radioactive Bulk Density/RHOB, Neutron Porosity/NPHIL, Photoelectric Effect Factor/XPEF) for successful consistent prediction and propagation away from core calibration where prolific electrical log measurements exist, and no core/rock data is present. A confident generation and prediction of PRTs at log scale allows components of petrophysical characterizations to be driven by lithofacies, ultimately providing better understanding of the reservoir. The reservoir under study corresponds to the proximal southeast part and the medial-distal northwest part of an Early-Middle Permian Gharif Formation, representing a non-marine fluvial deposit. It exhibits heterogeneity and layering, primarily comprising stacked fluvial channel deposits interspersed with flood-plain shales featuring paleosols. The variability in channel amalgamation rates is directly influenced by cyclic base-level variations, thereby dictating reservoir geometry. In conclusion, this paper presents a comprehensive methodology for describing lithofacies and establishing petrophysical rock types in non-marine fluvial reservoirs through core-log data integration. By providing valuable insights into reservoir behavior and enhancing the accuracy of reservoir characterization, this approach contributes significantly to more effective reservoir management. Through the detailed integration of core and log data, it offers a robust framework applicable to similar geological settings, aiding in better understanding and optimizing the exploitation of such reservoirs.