Effective porosity estimation from 3D seismic reflection data : Marco Polo Field, Gulf of Mexico

dc.contributor.advisorSen, Mrinal K.
dc.creatorYoung, Gregory Russell
dc.description.abstractSeismic inversion is a geophysical tool that uses geologically constrained physical models to infer elastic interval properties (e.g., P-velocity, S-velocity, and density) from seismic interface data (e.g., reflection amplitude and moveout). This thesis focuses on one such application to pre-stack seismic data from the Marco Polo Field, deepwater Gulf of Mexico. I compare the usefulness of post-, partial-, and pre-stack seismic inversion methods for estimating effective porosity by 1) inverting the Marco Polo Field seismic data with post-, partial-, and pre-stack seismic inversion methods, 2) transforming the estimated elastic parameters into effective porosity via fluid-dependent transformations derived from borehole data, and 3) interpreting all results in terms of inverse theory, rock physics, and the Marco Polo Field geology. I derive fluid-dependent transformations that are calibrated to the Marco Polo Field by cross-plotting measured elastic parameters (i.e., well logs) against petrophysical logs for gas-, oil-, and brine-saturated intervals in the wells. Cross-plot analysis indicates that density is best-suited for estimating effective porosity because the steep gradient of the density-to-effective porosity transformation implies minimal error magnification during mapping from the elastic parameter domain to the reservoir characteristic domain. The shallow gradient of the P-impedance-to-effective porosity transformation gradient results in substantial error magnification. Although density is the choice parameter for estimating effective porosity, deterministic linear seismic inversion methods have historically failed to resolve density. Post-stack inversion methods, which parameterize the model space with the single parameter P-impedance, provide no information about density. Partial-stack inversion methods fail to resolve density from angle-dependent amplitude variations because the NMO corrected gathers, upon which partial-stack inversions rely, have a separate null space that contains the necessary information to separate density from velocity within the impedance estimates. Pre-stack seismic inversion methods utilize the full waveform data (i.e., amplitude and moveout) and show great potential for estimating mass density. However, pre-stack seismic inversions are viewed as impractical due to high computation costs, and they are typically only applied to a few CMP's or over small time windows. Using the Miocene submarine fan system in the Marco Polo Field as a case study, I invert the 3D pre-stack seismic volume with a computationally efficient pre-stack seismic inversion algorithm, and I demonstrate that only full-waveform nonlinear pre-stack inversion accurately resolves density and estimates effective porosity within the deepwater system. The density and effective porosity estimates accurately tie with a type well with VSP, and they both correctly model the a priori structural and stratigraphic information in the Marco Polo Field. Moreover, the pre-stack inversion algorithm is able to resolve all model parameters from nearly flat initial models.en_US
dc.description.departmentEarth and Planetary Sciencesen_US
dc.relation.ispartofUT Electronic Theses and Dissertationsen_US
dc.rightsCopyright © is held by the author. Presentation of this material on the Libraries' web site by University Libraries, The University of Texas at Austin was made possible under a limited license grant from the author who has retained all copyrights in the works.en_US
dc.subject.lcshGeology--Mexico, Gulf of
dc.subject.lcshGeology--Mexico, Gulf of--Marco Polo Field
dc.titleEffective porosity estimation from 3D seismic reflection data : Marco Polo Field, Gulf of Mexicoen_US
thesis.degree.departmentGeological Sciencesen_US
thesis.degree.disciplineGeological Sciencesen_US
thesis.degree.grantorUniversity of Texas at Austinen_US
thesis.degree.nameMaster of Science in Geological Sciencesen_US

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