Reservoir Characterization of the Haynesville Shale, Panola County, Texas Using Rock Physics Modeling and Partial Stack Seismic Inversion

Reservoir Characterization of the Haynesville Shale, Panola County, Texas Using Rock Physics Modeling and Partial Stack Seismic Inversion
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Total Pages : 186
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ISBN-10 : OCLC:893862895
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Rating : 4/5 (95 Downloads)

Book Synopsis Reservoir Characterization of the Haynesville Shale, Panola County, Texas Using Rock Physics Modeling and Partial Stack Seismic Inversion by : Sarah Bryson Coyle

Download or read book Reservoir Characterization of the Haynesville Shale, Panola County, Texas Using Rock Physics Modeling and Partial Stack Seismic Inversion written by Sarah Bryson Coyle and published by . This book was released on 2014 with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis investigates the relationship between elastic properties and rock properties of the Haynesville Shale using rock physics modeling, simultaneous seismic inversion, and grid searching. A workflow is developed in which a rock physics model is built and calibrated to well data in the Haynesville Shale and then applied to 3D seismic inversion data to predict porosity and mineralogy away from the borehole locations. The rock physics model describes the relationship between porosity, mineral composition, pore shape, and elastic stiffness using the anisotropic differential effective medium model. The calibrated rock physics model is used to generate a modeling space representing a range of mineral compositions and porosities with a calibrated mean pore shape. The model space is grid searched using objective functions to select a range of models that describe the inverted P-impedance, S-impedance, and density volumes. The selected models provide a range of possible rock properties (porosity and mineral composition) and an estimate of uncertainty. The mineral properties were mapped in three dimensions within the area of interest using this modeling technique and inversion workflow. This map of mineral content and porosity can be interpreted to predict the best areas for hydraulic fracturing.


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