A major challenge today is the development of carbonate reservoirs. They represent the most significant reservoir formation in the middle-east gulf region.
In carbonate reservoirs, heterogeneity is usually driven by both depositional and diagenetic patterns. The complex diagenetic history which prevails in these reservoirs influences the final static and dynamic reservoir properties. Dolomitisation is one of the most crucial diagenetic phases because of the way in which it constrains the permeability behaviour of the field. Detailed diagenetic research has shown that various genetic dolomite types exist, including evaporative, mixed evolved sea-water - freshwater and late thermobaric. Defining static rock-type can therefore be problematic due to the highly imbricate diagenetic phases and their various distributions and extensions. A further major difficulty is the spatial distribution of rock-types in reservoir models, each linked to different sedimentological and diagenetic processes and each with their own, but interdependent, geometrical characteristics.
In order to optimise the current production of reservoirs as well as future field development plans it is therefore necessary to understand the main diagenetic phases and develop approaches which effectively incorporate them into the static reservoir model.
This paper presents a synthetic modelling workflow applied to a large oolitic ramp derived from outcrop and field observations. This new approach is based on nested stochastic simulations, geologically-driven and derived from the relationships between dolomite type, stratigraphic position, palaeogeographic position, depositional facies, and proximity to fault/fracture zones. In addition to the initial depositional model, three diagenetic phases are modelled successively in order to restore the complex spatial relationship existing between diagenetic alterations.
The outcome of this modelling workflow, after a stage testing coherence with PC and Kr values and dynamic rock-typing definition, is fed into a dynamic simulator in order to assess the potential impact on field behaviour, production profiles and well productivity.