Detecting Fluid-Included Lithofacies from AVO Data Using Rock Physics and Minimum Message Length Clustering
Abstract/Contents
- Abstract
- As a preliminary study for modeling offset-dependent seismic data (AVO) for time-lapse monitoring of water or stream flood production, a synthetic offset-dependent reflectivity and amplitude were generated statically and interpreted with CPU-efficient Minimum Message Length (MML) clustering by avoiding inversion. AVO seismic data generation involves petrophysical modeling, AVO forward modeling and seismic forward modeling. Bearing the uncertainty in AVO forward modeling in mind, we don't intend to "strictly" mimic a real geological scenario, but generate a set of data which is appropriate for AVO analysis by conducting AVO feasibility study.The interpretation method relies on information-based clustering technique: Minimum Message Length (MML). It consists of first scanning the reflectivity or amplitude with a pre-defined window to obtain realizations of windowed seismic feature. Next a MML clustering algorithm is applied to search for similarities within the windowed amplitude features. Finally it results in a probabilistic classification result to inform fluid-lithofacies presence stochastically.Taking advantage of AVO's potential as promising multiple seismic attributes in detecting hydrocarbon, and of MML's role as an alternative for seismic inversion, this work can be further extended to time-lapse pre-stack seismic interpretation to bring efficient interpretation seismic interpretation tools to production and assess dynamic behavior of producing hydrocarbon reservoirs
Description
| Type of resource | text |
|---|---|
| Date created | May 2003 |
Creators/Contributors
| Author | Xu, Ying |
|---|---|
| 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
- Xu, Ying. (2003). Detecting Fluid-Included Lithofacies from AVO Data Using Rock Physics and Minimum Message Length Clustering. Stanford Digital Repository. Available at: https://purl.stanford.edu/qy164rw6883
Collection
Master's Theses, Doerr School of Sustainability
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