Quantitative Assessment of Karst Pore Volume in Carbonate Reservoirs Using Discrete Karst Networks
Evaluating uncertainty in karst pore volume is a current industry challenge that is critical for field development planning and optimizing recovery. Hydrocarbon pore volume in karst can be significant in large super giant fields. Although a wide variety of karst features and the geological processes that describe their morphology has previously been described in many studies, understanding exactly how to translate this knowledge of karst into practical guidelines for the assessment of pore volume in carbonate reservoirs remains an industry challenge. We present a robust model-assisted characterization workflow that integrates well data, seismic data (if available), drilling data, geological concepts from modern and ancient outcrop analogs, and the application of Discrete Fracture Network technology, to explicitly model karst features. These Discrete Karst Network (DKN) models serve as powerful visualization and communication tools in addition to quantifying the karst pore volume. The model-assisted characterization workflow presented is specifically designed for the rapid evaluation of multiple viable geologic scenarios in recognition of the inherent uncertainty in karst morphology, fill, and sampling bias. DKNs rely on a karst intensity property that honors well data and is distributed in a full field model to reflect the conceptual models of different karst styles. These results are populated with reservoir properties for volumetric predictions. The DKN approach also has the ability to simultaneously model karst and fractures to determine effective reservoir properties for Dual Porosity, Dual Permeability flow simulations. We present nomograms to facilitate fast practical estimates of karst abundance and porosity, as well as cave area estimates from volumes lost while drilling to help condition the model inputs. A synthetic reservoir case study with varying degrees of karst that is interpreted to be coastal in origin is used to demonstrate the workflow.
AAPG Datapages/Search and Discovery Article #90350 © 2019 AAPG Annual Convention and Exhibition, San Antonio, Texas, May 19-22, 2019