Automatic Geobodies Detection from Seismic Using Minimum Message Length Clustering
Abstract/Contents
- Abstract
- The use of seismic data is an important aspect of geostatistical reservoir characterization. Seismic data brings important information about the lateral continuity of the reservoir, in addition to the vertical variation observed in wells.Typical to recent geostatistics is to work on seismic impedance data that has been inverted from a migrated amplitude dataset. This inversion needs to solve generally a non linear and non-unique problem. In this paper, we present an approach for automatically recognizing seismic patterns from seismic amplitude and relate them to facies avoiding the cost of the inversion.The methodology follows a two step approach: first the 3D seismic is sampled (scanned) with a co-located template obtaining realizations of local seismic amplitude variability. These realizations are clustered into classes with an unsupervised information-theory-based clustering technique. Secondly, the clusters are calibrated to facies observations obtained from wells. The result of this procedure is a 3D probability model for each reservoir facies.When using a big template for scanning, the dimensionality of the problem can get very high and we may want to apply a Principal Component Analysis (PCA) to the seismic realizations to reduce the dimension. Several examples demonstrate that the use of the PCA has to be made with caution because the reduction of the number of Principal Components used leads to a loss of information, being this loss difficultly quantified.We show that our approach is general, can take multiple seismic attributes and can deal with dimensionality reduction problems. After demonstration on a 2D synthetic dataset, we present a successful application on two actual 3D dataset.
Description
| Type of resource | text |
|---|---|
| Date created | June 2001 |
Creators/Contributors
| Author | Arroyo Garcia, Cristina |
|---|---|
| Primary advisor | Caers, Jef K. |
| 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
- Arroyo Garcia, Cristina. (2001). Automatic Geobodies Detection from Seismic Using Minimum Message Length Clustering. Stanford Digital Repository. Available at: https://purl.stanford.edu/hw668kc6604
Collection
Master's Theses, Doerr School of Sustainability
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- brannerlibrary@stanford.edu
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