Rule-Based Reservoir Modeling by Integration of Multiple Information Sources: Learning Time-Varying Geologic Processes
Abstract/Contents
- Abstract
- Rule-based modeling methodology has been developed to improve the integration of geologic information into geostatistical reservoir models. Quantifying modeling rules significantly aid in building geologically accurate reservoir models and reproduce the intrinsic complexity of subsurface conditions. Especially when we face up to a complexity where field data and geological knowledge are both limited the way we utilized rules is fairly important. To expand the application of rule-based reservoir modeling in various field cases, we propose a systematic methodology of creating rules from other information sources. Physical geomorphic experiments and process-based models contain time series information we need for a reservoir model. Incorporating these two information sources facilitate the rule induction of rule-based modeling and therefore help capture the underlying uncertainty. Two examples are demonstrated in our study. A reference class from experiments is created for turbidite lobe system, while a realization of process-based models is used to mimic and simulate channel network patterns and their behaviors on a delta plain. In our study, we assume if an experiment is comparable to field data at a certain interpretation scale, the sedimentary processes and associated structures are informative and provide at least some references resulting in sedimentary features at the comparable scale. Ripley’s K-function is utilized to analyze and extract spatial clustering information of lobe elements at a given scale from experimental strata. We converted K function to modeling rules allowing us to integrate clustering patterns of turbidite lobes into surface-based models. Surface-based models successfully produce a clustered point behavior and a stratigraphic framework comparable to the chosen physical tank experiment. These models can be used to better assess subsurface spatial uncertainty under such a stochastic process framework constrained by experimental information. To facilitate the utilization of process-based models, an automated channel feature extraction tool is developed and able to adjust parameters for the optimal result. Multi-Scale Line Tracking Algorithm is embedded and shows robust and accurate extraction of channel networks from Delft3D models. Space colonization algorithm is proposed to capture the developmental processes of channel network and reproduce a network pattern. It is able to integrate theoretical knowledge and simulate a network coupling with feature extraction tool and. The overall methodology is able to efficiently simulate channel networks and their progradation through time given information from one or more realizations of process-based models.
Description
| Type of resource | text |
|---|---|
| Date created | March 2015 |
Creators/Contributors
| Author | Wang, Yinan |
|---|---|
| Primary advisor | Mukerji, Tapan |
| Degree granting institution | Stanford University, Department of Energy Resources Engineering |
Subjects
| Subject | School of Earth Energy & Environmental Sciences |
|---|---|
| Genre | Thesis |
Bibliographic information
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Preferred citation
- Preferred Citation
- Wang, Yinan. (2015). Rule-Based Reservoir Modeling by Integration of Multiple Information Sources: Learning Time-Varying Geologic Processes. Stanford Digital Repository. Available at: https://purl.stanford.edu/cd158rj5479
Collection
Master's Theses, Doerr School of Sustainability
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