A Bayesian framework for quantifying fault network uncertainty using a marked point process model
Abstract/Contents
- Abstract
- Faults are one of the building-blocks for subsurface modeling studies. Incomplete observations of subsurface fault networks lead to uncertainty pertaining to location, geometry and existence of faults. In practice, gaps in incomplete fault network observations are filled based on tectonic knowledge and structural interpreter's intuition pertaining to fault relationships. Modeling fault network uncertainty with realistic models that represent tectonic knowledge is still a challenge. Although methods that address specific sources of fault network uncertainty and complexities of fault modeling exists, a unifying framework is still lacking. In this paper, a rigorous approach to quantify fault network uncertainty is proposed. Fault pattern and intensity information pertaining to fault networks are expressed by means of a marked point process, namely a marked Strauss point process. Fault network information represented using marked Strauss point process is constrained to fault surface observations (complete or partial) within a Bayesian framework. A structural prior model is defined to quantitatively express fault patterns, geometries and relationships within the Bayesian framework. Structural relationships between faults, in particular age-based fault abutting relations, are represented with an implicit, level-set based approach to represent abutting relations between fault surfaces. A Markov Chain Monte Carlo sampler is used to sample posterior fault network realizations that reflect tectonic knowledge and match fault observations. We apply the methodology to a field study from Nankai Trough and Kumano Basin. In this illustrative study, the target for uncertainty quantification is a deep site with attenuated seismic data with only partially visible faults and many faults missing from the survey or interpretation. A structural prior model is built from shallow analog sites that are believed to have undergone similar tectonics compared to the site of study. Fault network uncertainty for the field is quantified with fault network realizations that are conditioned to structural rules, tectonic information and partially observed fault surfaces. The proposed methodology generates realistic fault network models conditioned to data and a conceptual model of the underlying tectonics. In the second part of the thesis, an approach to incorporate fault network uncertainty in fluid flow problems is proposed. The main challenge is creating multiple structural frameworks and creating deformable grids prior to fluid flow simulation. Proxy-based workflow are presented in order to choose fault network realizations that result in most dissimilar fluid-flow responses. Thus, the computational load of evaluating exhaustive set of fault network realizations is reduced to a select few. The proxy-based approach is illustrated using a field case.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2017 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Aydin, Orhun |
|---|---|
| Associated with | Stanford University, Department of Energy Resources Engineering. |
| Primary advisor | Caers, Jef |
| Thesis advisor | Caers, Jef |
| Thesis advisor | Aydin, Atilla |
| Thesis advisor | Mukerji, Tapan, 1965- |
| Advisor | Aydin, Atilla |
| Advisor | Mukerji, Tapan, 1965- |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Orhun Aydin. |
|---|---|
| Note | Submitted to the Department of Energy Resources Engineering. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2017. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2017 by Orhun Aydin
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