Carbonate reservoirs are inherently complex in their nature. This complexity is due to a combination of depositional rock fabric textures and diagenetic modification of the rocks. Post-depositional processes can modify the original petrophysical properties (e.g. permeability, irreducible water saturation and relative permeability) and result in a disconnection between original depositional rock fabric and current reservoir properties.
Pore types are a critical element of rock types since they exert a dominant control over petrophysical properties and fluid flow. Their proper definition is especially important in complex carbonates with multiple pore systems. Several papers, however, restrict pore typing to MICP groups without transferring to log domain necessary for reliable earth modeling.
A procedure has been developed to describe the dominant pore types occurring within a carbonate reservoir based on the interpretation of standard core data, mercury injection capillary pressure data and wireline log data. This procedure incorporates the following components: sample selection methodology, data acquisition, data quality control and corrections, parameterization of the MICP curves using Gaussian decomposition, clustering, extrapolation of MICP derived pore types groups (PTGs) to all core plug samples, and lastly prediction in the log-domain.
The workflow described here is unique in that it describes the process from sample selection through log-scale prediction, PTGs are defined independently of the original depositional geology, parameters which describe the whole MICP curve shape are utilized, and objective clustering is used to remove subjective decisions.
In this paper, we will describe the proposed workflow and present a case study from a carbonate field where characterizing PTGs in this way provided a better understanding of controls on rock properties and fluid flow than was achieved by looking at the depositional facies alone.
