Inference of Sub-Resolution Stacking Patterns from Seismic Data in Spatially Coupled Models
Abstract/Contents
- Abstract
- Geological modeling and characterization of energy reservoirs greatly relies on the interpretation of seismic data due to its extensive spatial availability. Owing to many factors, including subsurface complexities, heterogeneity and limited seismic resolution, accurate description of subsurface geological configuration becomes a major challenge, giving rise to tremendous uncertainty in reservoir evaluation. The ability to reason about arrangements of thin sub-seismic scale beds is particularly important in the development of strongly heterogeneous unconventional formations due to complex completion strategies. This work is focused on quantitative interpretation of individual seismic traces and extraction of information relevant to recognition of various geological parasequences. The proposed workflow involves stochastic modeling of stratigraphic sequences comprising thin subresolution beds, and subsequent application of a generative statistical model to invert for those sequences from the respective acoustic responses.
Description
| Type of resource | text |
|---|---|
| Date created | July 2019 |
Creators/Contributors
| Author | Muradov, Riyad |
|---|---|
| Primary advisor | Mukerji, Tapan |
| Degree granting institution | Stanford University, Department of Energy Resources Engineering |
Subjects
| Subject | School of Earth Energy & Environmental Sciences |
|---|---|
| Subject | inverse problem |
| Subject | reservoir characterization |
| Subject | hidden markov model |
| Genre | Thesis |
Bibliographic information
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- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
Preferred citation
- Preferred Citation
Muradov, Riyad (2019). Inference of Sub-Resolution Stacking Patterns from Seismic
Data in Spatially Coupled Models. Stanford Digital Repository. Available at: https://purl.stanford.edu/km001pf4033
Collection
Master's Theses, Doerr School of Sustainability
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- Contact
- rmuradov@stanford.edu
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