Integrated Facies Modeling in Shelf Margin Carbonate Field

Abstract

A 125 km2 gas bearing, shelf margin carbonate is planned for development and production in the offshore field. The main static modeling challenge is to estimate the gas in place volume. This challenge is attributed to the wide and elongated structure of the field and is further amplified by the limited available well data. Moreover, based on nearby field analogue, the carbonate reservoir is expected to have high lateral and vertical heterogeneity. Hence, robust facies modeling is critical to determine depositional facies distribution and ultimately the calculation of the gas in place volume. The integration of various data is used to model the facies in gas-bearing carbonate reservoirs. In this facies modeling study, three wells log data and one core data are used in combination with seismic data. Additionally, shelf margin carbonate field conceptual geology is interpreted and visualized. The reservoir characteristic of the field from north to south can be interpreted as back reef, reef flat and reef front. Since the wave energy in the southern part is high and generally contains large fossil reefs, it creates better reservoir properties, such as porosity. The east to west reservoir distribution is simulated with better properties on the eastern area compared to the western area, as secondary porosity occurs on the eastern part, where it is interpreted as higher basement morphology and carbonate build up on the bottom of the reservoir. Two methodologies of facies modeling are introduced for distributing rock type in the field. As a guide for secondary variable facies modeling, two probability trend maps (trend map A & trend map B) are created based on the sketch of each rock type of shelf margin conceptual geology supported by seismic acoustic impedance trend maps extracted from 3D seismic cubes. The result of the two facies modeling is integrated in a static model as multiple realization volumetric to minimize uncertainty. As a result, the facies model has captured the possible scenarios to reduce subsurface uncertainty. The combination of the conceptual geology of trend map A and trend map B is an advantageous application for modeling an optimum facies model in shelf margin carbonate reservoir environment. Consequently, this methodology is enabling a more robust volumetric estimation in a high uncertainty.