Integration of geomorphic experiment data in surface-based modeling : from characterization to simulation
Abstract/Contents
- Abstract
- Understanding the impact of geological dynamic processes on spatial heterogeneity of petrofacies is crucial for modeling reservoir performance. Surface-based method is a powerful static reservoir modeling technique that explicitly considers the influence of geological dynamic processes. Moreover, surface-based method is a framework that is capable of integrating various modeling techniques to generate realistic realizations. However, current algorithms of surface-based models rely on imprecise depositional rules and subjective decisions, which normally introduce a large number of empirical coefficients and thus increase workloads in reservoir uncertainty estimation. The motivation of this dissertation is to develop an improved algorithm of surface-based modeling that is more effective for uncertainty estimation. First, geomorphic experiments are proposed to be the knowledge database of depositional rules and a quantitative treatment is devised to extract rules from records of such physical experiments. Erosion on shale drapes in deepwater environments is a key parameter to the sand body connectivity in a reservoir. Proper erosion rules on shale drapes require intermediate topography of the depositional process, which is infeasible to measure from real depositional systems. To study this issue, we developed a quantitative workflow based on geomorphic experiment in which the intermediate topographic conditions are available. Data from an experiment of a delta basin with recorded intermediate topography is used to demonstrate this workflow. Second, a statistical data mining solution is provided to identify similarity between a geomorphic experiment and a real depositional system; thus small experiments are linked to specific real depositional systems. Applications of static reservoir modeling always pursue models in analogy to the real scale system of a reservoir, yet no hydrodynamic or stratigraphic method has been developed. A solution is provided to identify one experiment that is the most similar to a given real system from a set of optional experiments based on comparison of two lobe stacking patterns. Conventionally, lobate bodies in experiments can be identified hierarchically from small scales to large scales based on decisions of the interpreter. The proposed solution provides an automatic method to quantify lobe hierarchies and to choose lobate stacking patterns at various scales of interpretation. This solution is demonstrated with real data. Finally, quantitative criteria for comparing hierarchies of lobes is proposed to assess hierarchical similarity between realizations of various algorithms. Based on the proposed comparison criteria, we demonstrate that a simple algorithm of lobe migration mechanism can be functionally equivalent to the conventional complex algorithm. The new implementation is also designed to honor prior information from geomorphic experiments. Based on these quantitative criteria, we demonstrate that realizations of the new algorithm are hierarchically similar to the input pattern. Thus, the relationship between the input pattern and realizations in our algorithm is analogous to the relationship between training images and realizations of multiple point statistics simulations.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2014 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Xu, Siyao |
|---|---|
| Associated with | Stanford University, Interdisciplinary Program of Earth, Energy and Environmental Sciences. |
| Primary advisor | Mukerji, Tapan, 1965- |
| Thesis advisor | Mukerji, Tapan, 1965- |
| Thesis advisor | Caers, Jef |
| Thesis advisor | Hilley, George E |
| Advisor | Caers, Jef |
| Advisor | Hilley, George E |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Siyao Xu. |
|---|---|
| Note | Submitted to the Interdisciplinary Program of Earth, Energy and Environmental Sciences. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2014. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2014 by Siyao Xu
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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