Channel Recognition Using Feature-Based Geostatistics: A Submarine Channel Case Study
Abstract/Contents
- Abstract
- Channel recognition is of special importance for oil exploration and development. Traditional channel recognition approaches such as deterministic and stochastic methods often neglect the combined uncertain and deterministic aspect of seismic data. The channel recognition method described in this report is basically a stochastic method. However, this method takes advantage of the deterministic information provided by seismic and at the same time addresses the spatially varying quality and resolution of seismic data. In the first phase, by applying a feature-based method, we aim at detecting channel elements=pieces clearly visible from seismic data. In the second phase, we generate channel realizations constrained to the previously interpreted channel elements. The latter step is performed within the framework of mp-geostatistics. A case study of a submarine channel data set is presented. We conclude that the method is an improvement over the traditional stochastic approach.
Description
| Type of resource | text |
|---|---|
| Date created | May 2002 |
Creators/Contributors
| Author | Dong, Chunrong |
|---|---|
| Primary advisor | Caers, Jef |
| Degree granting institution | Stanford University, Department of Petroleum Engineering |
Subjects
| Subject | School of Earth Energy & Environmental Sciences |
|---|---|
| Genre | Thesis |
Bibliographic information
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Preferred citation
- Preferred Citation
- Dong, Chunrong. (2002). Channel Recognition Using Feature-Based Geostatistics: A Submarine Channel Case Study. Stanford Digital Repository. Available at: https://purl.stanford.edu/pb013td8253
Collection
Master's Theses, Doerr School of Sustainability
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