About This Item

Share This Item

The AAPG/Datapages Combined Publications Database

Environmental Geosciences (DEG)

Abstract

DOI:10.1306/eg.04080909008

Assessing spatial uncertainty in reservoir characterization for carbon sequestration planning using public well-log data: A case study

Erik R. Venteris,1 Kristin M. Carter2

1Ohio Department of Natural Resources, Division of Geological Survey, 2045 Morse Rd., Bldg C-1, Columbus, Ohio 43229; [email protected]
2Pennsylvania Geological Survey, 400 Waterfront Dr., Pittsburgh, Pennsylvania 15222; [email protected]

AUTHORS

Erik Venteris, a senior geologist for the Ohio Department of Natural Resources, Division of Geological Survey, has worked on various carbon sequestration projects involving soil-based and geology-based approaches and has been collaborating with the MRCSP since 2004. His interests include applying statistics and geostatistics to earth science problems. Erik holds a Ph.D. in soil science and an M.S. degree in geology from the Ohio State University, and a B.S. degree in geology from Western Illinois University.

Kristin Carter joined the Pennsylvania Geological Survey in 2001 and currently serves as the chief of the Carbon Sequestration Section. Kristin researches oil, gas, and subsurface geology in Pennsylvania and surrounding states, particularly as they relate to geologic carbon sequestration opportunities. Kristin received an M.S. degree in geological sciences from Lehigh University and a B.S. degree in geology and environmental science from Allegheny College.

ACKNOWLEDGEMENTS

The authors thank both Christopher Laughrey (Pennsylvania Geological Survey) and James Castle (Clemson University) for their insightful and constructive comments regarding the petrophysics of the Medina play in northwestern Pennsylvania. In addition, we thank Nathan Yancheff, Kelly Sager, and Brooke Molde, three of the Pennsylvania Geological Survey's summer student workers, who helped to gather and interpret geologic data in the study area. Gary Weismann is thanked for providing a thoughtful review of the original manuscript. Acknowledgement is also made to the Midwest Regional Carbon Sequestration Partnership (MRCSP), managed by Battelle Memorial Institute and funded in large part by the U.S. Department of Energy. As part of the MRCSP, the Ohio and Pennsylvania Geological Surveys have been able to gather, interpret, and evaluate reservoir and geostatistical data relative to the geologic sequestration of several subsurface units in the Appalachian Basin, including the Medina Group/“Clinton” Sandstone.

ABSTRACT

Mapping and characterization of potential geologic reservoirs are key components in planning carbon dioxide (CO2) injection projects. The geometry of target and confining layers is vital to ensure that the injected CO2 remains in a supercritical state and is confined to the target layer. Also, maps of injection volume (porosity) are necessary to estimate sequestration capacity at undrilled locations. Our study uses publicly filed geophysical logs and geostatistical modeling methods to investigate the reliability of spatial prediction for oil and gas plays in the Medina Group (sandstone and shale facies) in northwestern Pennsylvania. Specifically, the modeling focused on two targets: the Grimsby Formation and Whirlpool Sandstone. For each layer, thousands of data points were available to Previous HitmodelNext Hit structure and thickness but only hundreds were available to support volumetric modeling because of the rarity of density-porosity logs in the public records. Geostatistical analysis based on this data resulted in accurate structure models, less accurate isopach models, and inconsistent models of pore volume. Of the two layers studied, only the Whirlpool Sandstone data provided for a useful spatial Previous HitmodelTop of pore volume. Where reliable models for spatial prediction are absent, the best predictor available for unsampled locations is the mean value of the data, and potential sequestration sites should be planned as close as possible to existing wells with volumetric data.

Pay-Per-View Purchase Options

The article is available through a document delivery service. Explain these Purchase Options.

Watermarked PDF Document: $14
Open PDF Document: $24