Reservoir Rock Typing: Integration of Geological Attributes and Petrophysical Properties: Case Study from Yamama Reservoir

Abstract

AbstractTo build full-field static and dynamic models for supporting appraisal and development activities in F-Field in the southern part of Iraq, a comprehensive reservoir rock typing study for the Yamama carbonate reservoir was carried out by integrating geological information and petrophysical properties. This process involved first the integration of geological information (MICP, CT, thin section) to find the link between pore-throat sizes and petrographic information. The relationships were cross-checked by core descriptions with the depositional background information. After involving porosity and permeability from routine core analysis (RCAL), the rock types were identified and further grouped in the cross-plots. Because not all the plugs with permeability and porosity have MICP & thin section information, it was necessary to apply empirical rock typing methods, like rock fabrics number (RFN), flow zone indicator (FZI). Winland’s R35, to determine which method/classification could provide a better approach to classify the reservoir rock types (RRTs) with geological meanings based on core descriptions and petrographic description, which includes diagenetic overprints.The FZI method shows a better match with Yamama reservoir Rock Typing. Sw-Height functions for each RRT were also created by using the Lamda method, and the parameters were tuned by wireline log-derived Sw. During the initialization of the dynamic model, the relative consistency of Sw distribution between static and dynamic models was reached. The relative permeability from special core analysis (SCAL) was assigned for each RRT. During the history matching process, a good pressure and production rate match was achieved.This paper presents a RRT workflow for the typical matrix-based carbonate reservoirs with relatively limited core analysis data. By integrating the geological attributes and petrophysical properties, a better history match was achieved in the dynamic modelling, which could be used in forecasts and future reservoir management activities.