Integrated Workflow Applied to the Characterization of a Carbonate Fractured Reservoir: Qarn Alam Field

Abstract

Abstract This paper presents an innovative integrated workflow applied to the characterization of a carbonate fractured reservoir in order to generate an effective 3D Matrix Block Size (MBS) distribution based on all available data: geological, geophysical and dynamic production data. The MBS is a key factor determining heating and thereby recovery efficiency during steam flooding in a fractured reservoir. The MBS model allows simulating the complex flow and establishing reservoir management strategies that will optimize oil recovery and facility sizing. The major difficulty in developing a 3D MBS model is the ability to account for the all the information pertinent to natural fractures in the field and develop an understanding of what geological characteristics are linked to the occurrence of fractures. Until recently most fractured reservoir modeling tools were limited to simple discrete statistical models. A new approach in fractured reservoir characterization, using primarily artificial intelligence tools, is presented in this paper. The methodology is based on the assumption that there is a complex relationship between a large number of potential geologic drivers (structure, faults, matrix characteristics etc.) and fractures. The combination of both the continuum fracture modeling (CFM) and discrete fracture network (DFN) modeling provides a quantitative framework for MBS distribution estimation, geological concepts and data integration. The application of this integrated workflow to the Qarn Alam field is presented in this paper. Introduction The Qarn Alam Field of Central Oman contains an estimated STOIIP of 185 million m3 of heavy oil, which is planned to be developed with a full-field steam injection EOR project. Despite the fact that the oil production from the main reservoir, the Shuaiba, is known to occur via a fracture/karst network, the vast majority of the oil is stored in the matrix. The porosity and permeability network of the matrix is often a function of the distribution of depositional rock facies. If significantly different facies are present in a reservoir, their spatial arrangement has to be understood and modeled adequately, in order to understand hydrocarbon distribution and recovery. Critical for the success of a full field steam injection EOR project is a proper understanding of the subsurface geology, most importantly the distribution of matrix porosity and permeability and the occurrence and distribution of hydraulically conductive fractures. This paper illustrates the combination of CFM and DFN modeling to model the fracture distribution and the MBS distribution.