Abstract
Non-matrix features in the margin area of a carbonate reservoir contribute significantly to field in-place volumes, production rates, and recovery. These features vary in size and shape, but exhibit distinct behavior compared to tight matrix rock. Nonmatrix features such as karst can provide significant storage capacity, and well-connected fractures provide high deliverability. These characteristics make production from the margin area attractive. However, extreme heterogeneity of non-matrix features increases uncertainty and requires complex analysis involving data interpretation, advanced characterization, and sophisticated modeling in order to estimate future production performance.
During the initial field development stage, data interpretation is frequently limited by data acquisition capabilities. Data collection associated with non-matrix features can be challenging, often leading to less than optimal data sets. The scale difference among data gathering techniques further increases the gap between interpretation and full-field modeling. Data integration, along with the use of analogs and concepts, helps to bridge data gaps and improves confidence when modeling field-wide non-matrix heterogeneities.
Advanced approaches, such as dual porosity / dual permeability dynamic modeling enhance ability to evaluate both matrix and non-matrix properties. Once developed, the model is calibrated by comparing simulation results to available dynamic data. As part of the modeling workflow, uncertainty analysis is conducted to identify and evaluate key reservoir properties that could impact in-place volumes and ultimate field recovery. Through collaboration and integration of geoscience and reservoir engineering workflows, it is possible to create a well characterized and calibrated model that is capable of testing key uncertainties against a range of development concepts.
