A Disruptive Approach for Rock Typing Prediction and Modeling in Carbonate Reservoirs

Carbonate reservoirs, despite their global significance in hydrocarbon production, present a persistent challenge to reservoir characterization due to their intrinsic heterogeneity. This complexity stems from a combination of depositional variability, diagenetic overprint, and the presence of diverse pore systems, which disrupt conventional petrophysical relationships. In this context, rock typing—the classification of reservoir rocks into groups with internally consistent flow and storage properties —emerges as a critical step in bridging geological description and dynamic reservoir modeling. The necessity of rock typing in carbonate systems is not merely academic; it is vital. Unlike siliciclastic reservoirs, where depositional facies often correlate well with petrophysical behavior, carbonate rocks exhibit a wide spectrum of pore types and diagenetic alterations that hamper such correlations. Effective rock typing enables the grouping of rocks with similar petrophysical responses, thereby facilitating the assignment of porosity-permeability relationships, capillary pressure functions, and relative permeability curves within reservoir models (Lucia, 1995; Jennings & Lucia, 2003). The central challenge involves transferring rock type information from core-scale observations to reservoir-scale models. Core data offer detailed insights into petrophysical properties, but their spatial coverage is limited, and extending these findings to threedimensional reservoir grids can lead to significant bias and mistakes. Additionally, the spatial distribution of rock types is governed by geological processes that may not be well defined due to limited available data, particularly in areas or contexts where data are sparse.