Geologic uncertainties in faulted basin models with application to the Norwegian North Sea
Abstract/Contents
- Abstract
- Basin modeling is a fundamental physics-governed tool that helps estimate subsurface exploration risks through simulation of basin history in geological time and quantifying the amount of petroleum, or other fluid, e.g., CO2, accumulated in the trap. A common deterministic approach to basin modeling is to define all the required input variables using expert knowledge, geological analogs, log data, or common sense and to produce one or several deterministic basin modeling scenarios. Purely deterministic scenarios may include overlooked hidden geological uncertainties, lack of understanding of the uncertainty, or can be biased due to overconfidence in the ability to estimate the uncertainty. This work explores the semi-deterministic approach to unlocking the impact of several sources of uncertainty typical for faulted hydrocarbon fields in Viking Graben, the Norwegian North Sea, on high-resolution 3D basin modeling results. We simulate multiple high-resolution basin models of the same field using a different combination of uncertain parameters and analyze the simulation results with the help of the multidimensional scaling (MDS) technique. This approach allows us to examine the effect of different combinations of uncertainty parameters on simulation results. Our approach facilitates quick analysis of multiple basin models at once and helps highlight models with such a combination of uncertain parameters, which match geological conceptions of the study area. In addition to exploring the geological uncertainties in 3D basin modeling in Viking Graben, we highlight the problems specific to the current workflows on the seismic interpretation of faults in subsurface exploration. The fault model governs trap geometry, pressure compartmentalization, fluid contacts, and other vital exploration features at the reservoir and the basin scale. Thus, seismic fault interpretation uncertainties can impact basin modeling results. This work proposes a geologically constrained and machine learning-guided seismic fault interpretation workflow. It allows for segmenting structurally consistent faults from noisy automatic seismic fault interpretation output. This output can be a validated fault framework for basin modeling studies. The thesis includes five chapters: a first introduction chapter and three main chapters, followed by a conclusion chapter. Chapter 2 and Chapter 3 employ the MDS technique to analyze multiple basin model simulations with a combination of uncertain parameters. As a result, we choose models with a combination of uncertain parameters that match the study area's geological conceptions. Chapter 4 proposes a workflow to extract a structurally consistent fault model from automatic fault output, e.g., from machine learning using an Approximate Bayesian Framework and modified Hausdorff distance. Three main chapters are separately completed parts of one Ph.D. research combined with the joint scientific idea to explore factors affecting basin modeling results and exploration studies in faulted areas of the Norwegian North Sea.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2022; ©2022 |
| Publication date | 2022; 2022 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Aseev, Anatoly |
|---|---|
| Degree supervisor | Graham, S. A. (Stephan Alan), 1950- |
| Degree supervisor | Mukerji, Tapan, 1965- |
| Thesis advisor | Graham, S. A. (Stephan Alan), 1950- |
| Thesis advisor | Mukerji, Tapan, 1965- |
| Thesis advisor | Scheirer, Allegra Hosford |
| Degree committee member | Scheirer, Allegra Hosford |
| Associated with | Stanford University, Department of Geological Sciences |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Anatoly Aseev. |
|---|---|
| Note | Submitted to the Department of Geological Sciences. |
| Thesis | Thesis Ph.D. Stanford University 2022. |
| Location | https://purl.stanford.edu/rb312sn4077 |
Access conditions
- Copyright
- © 2022 by Anatoly Aseev
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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