Project Report

This research will investigate the applicability of a geostatistical model to petroleum reservoirs having different geological conditions. Creating established methods and using models that work on multiple reservoirs, will decrease the amount of uncertainty when modeling reservoir characteristics. This project seeks to investigate the efficiency of a geostatistical modeling approach to the integration of geophysical data from well logging and core experiments under diverse geologic and depositional settings of the reservoir. This research will use various geostatistical methods, including variogram, sequential Gaussian simulation and likelihood for the modeling. Using said modeling approach for a second reservoir characterization is unique as normally a new, separate procedure is created for each reservoir. A specific, geostatistical modeling approach with applications beyond a single reservoir will lessen the time spent building new procedures and serve as a base or standard for future work. Carbonate reservoirs have been major targets for gas and oil exploration and carbon sequestration. Investigating and determining the capabilities of carbonate rock formations as reservoirs and the amount of petroleum contained in them is accomplished using geophysics and geo-statistics. Geophysicists use data from well logging and core experiments to examine lithology and rock characteristics and then analyze porosity and permeability relations in the carbonate formations. Resolution, spatial coverage and the number of measured parameters occur at three separate scales to provide subsurface lithology and structural information. This research implements a scaling process to verify the efficiency and accuracy of a porosity model using a likelihood-based upscaling method for core and plug data and then sequential Gaussian simulation for reservoir sized scaling. Ultimately a new geostatistical analysis method emerged that utilizes a new correlation algorithm between the different data resolutions of core and plug data. Scientists studying spatially varying data in reservoir rocks, whether hydrological or petroleum, should be able to implement these methods to better model subsurface fluid flow.

National Science Foundation (NSF)
Office of International and Integrative Activities (IIA)
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Program Officer
Carter Kimsey
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Burch Katrina
Kansas City
United States
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