Patrick Duff

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Patrick Duff
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Patrick Duff is a fifth year Ph.D. student studying geology at the University of South Carolina specializing in the evolution of rifted continental margins. In pursuing his studies, Patrick emphasize the integration of geological and geophysical data sets across spatial scales, as well as a balance between purely scientific and applied aspects. For example, he uses geologic field mapping and potential field and seismic data sets to study how tectonic inheritance can drive rifted margin segmentation over hundreds of kilometers, as well as using vibracores and GRP to study how the interplay of auto- and allogenic sedimentary processes control facies heterogeneity in a single modern barrier island or strand-plain deposit. Patrick is interested in patterns of rift-related mafic magmatism, but also in evaluating how maficigneous rock bodies might serve as a reservoir for CO2 sequestration. Patrick is a strong believer in collaborative, multi-disciplinary research, and in inspiring a new diverse generation of earth scientists.

2019 Near Surface Research Award Recipient

Understanding Shallow Marine Clastic Reservoir Heterogeneity from Modern Analogues Resolved by GPR and Drone Imagery

Abstractː Clastic deposition in the continental and near shore environment is widely recognized as complex, and is controlled by allogenic processes such as eustasy and tectonics and autogenic processes influenced by fluvial discharge and morphologic inheritance. The dynamic nature of the processes that drive fluvial to shallow marine deposition results in deposits that exhibit abrupt lateral and vertical discontinuities in facies that juxtapose porous and permeable units with impermeable ones. The complex geometries and stacking patterns that form the depositional architecture of preserved continental and near shore depositional systems represent significant obstacles to applying many of the conceptual and stochastic models utilized in exploration and reservoir modeling. Close examination of modern fluvial to shallow marine deposits of reservoir quality sand can assist in predicting the occurrence of these deposits in the subsurface, as well as understanding their internal structure.

To establish a proof of concept, a series of vibracore samples, GPR reflection lines, and rotary-wing drone surveys will be acquired on the coastal plain of South Carolina to investigate the internal structure and stratigraphy of strand-plain/barrier-island sand deposits, to define the morphological expression of these features, and to estimate their reservoir characteristics. A 3D GPR reflection grid, as well as a common midpoint survey and borehole survey, will be collected in 100 and 200 MHz within a paleoshoreface deposit at the University of South Carolina’s Baruch Institute for coastal science research. These near-surface geophysical data will be integrated with vibracore data and drone imagery to resolve the geometry and internal structure of the deposit, including bedform types and scales, as well as internal surfaces, such as unconformity, flooding, and accretionary surfaces. Stratigraphic units are differentiable on the basis of vibracores, as well as radar sections, with observed changes in radar velocity and facies correlative to structural and/or stratigraphic interfaces. Raw subsurface data will be processed to develop data products such as a depth converted GPR volume, a GPR velocity log for the borehole, digital core logs and photos for the continuous cored interval within the GPR volume, and a DEM and digital orthomosaic of the land surface. These data products will be used to construct a 3D near-surface model of the deposit using Schlumberger Petrel software. The results will establish the suitability of the imaged deposit as a reservoir analogue for ancient equivalents. The high-resolution imaging (centimeter scale) of modern deposits made possible by GPR and fixed-wing drones will allow the 3D near-surface model to be used to condition subsurface reservoir models by defining internal geometries not imaged by seismic data and assisting the estimation of reservoir heterogeneity as well as internal surfaces, such as unconformity, flooding, and accretionary surfaces.

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Patrick Duff
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