Type of Document Dissertation Author Kalla, Subhash Author's Email Address email@example.com URN etd-11122008-144747 Title Reservoir Characterization Using Seismic Inversion Data Degree Doctor of Philosophy (Ph.D.) Department Petroleum Engineering Advisory Committee
Advisor Name Title White, Christopher D Committee Chair Kam, Seung Committee Member Lorenzo, Juan M Committee Member Sears, Stephen O Committee Member Mohammad, Louay N Dean's Representative Keywords
- seismic downscaling
- exact constraints
- reservoir characterization
- truncated Gaussian Bayesian method
- data integration
- selecting realizations
- inexact constraints
- sequential sampling
- stacking patterns
Date of Defense 2008-10-31 Availability unrestricted Abstract
Reservoir architecture may be inferred from analogs and geologic concepts, seismic surveys, and well data. Stochastically inverted seismic data are uninformative about meter-scale features, but aid downscaling by constraining coarse-scale interval properties such as total thickness and average porosity. Well data reveal detailed facies and vertical trends (and may indicate lateral trends), but cannot specify intrawell stratal geometry. Consistent geomodels can be generated for flow simulation by systematically considering the precision and density of different data. Because seismic inversion, conceptual stacking, and lateral variability of the facies are uncertain, stochastic ensembles of geomodels are needed to capture variability.
In this research, geomodels integrate stochastic seismic inversions. At each trace, constraints represent means and variances for the inexact constraint algorithms, or can be posed as exact constraints. These models also include stratigraphy (a stacking framework from prior geomodels), well data (core and wireline logs to constrain meter-scale structure at the wells), and geostatistics (for correlated variability). These elements are combined in a Bayesian framework.
This geomodeling process creates prior models with plausible bedding geometries and facies successions. These prior models of stacking are updated, using well and seismic data to generate the posterior model. Markov Chain Monte Carlo methods sample the posteriors. Plausible subseismic features are introduced into flow models, whilst avoiding overtuning to seismic data or conceptual geologic models. Fully integrated cornerpoint flow models are created, and methods for screening and simulation studies are discussed. The updating constraints on total thickness and average porosity need not be from a seismic survey: any spatially dense estimates of these properties may be used.
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