A Workflow to Account for Uncertainty in Well-Log Data in 3D Geostatistical Reservoir Modeling
Abstract/Contents
- Abstract
- Traditionally well-log data are used as hard data in reservoir modeling using geostatistics; this means that we apply a constraint that needs to be honored exactly by the reservoir model. However, if a well-log is not reliable because of the log quality then the model will be constrained by non-reliable data. One could then argue in more general terms that in most practical cases hard data on either facies or petrophysical properties rarely exist because well log data requires processing and interpretation before reaching the geostatistical modeling stage. The traditional method approach using Kriging with error variance assumes that the error covariance is known and assumed uncorrelated. Another traditional approach, the Bayesian modeling, often relies on the assumption of Gaussianity in the likelihood of error model. In order to overcome these limitations, a data pre-posterior iterative algorithm approach is proposed. Several realizations of the uncertain well log data are generated taking into account the reliability of the well log measurements and they serve as input for the reservoir modeler to loosen the well constraints while at the same time honoring the geological spatial continuity. We present an application of this workflow to a real reservoir case study.
Description
| Type of resource | text |
|---|---|
| Date created | June 2007 |
Creators/Contributors
| Author | Akamine Ramirez, Jose Yenko |
|---|---|
| Primary advisor | Caers, Jef |
| 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
- Akamine Ramirez, Jose Yenko. (2007). A Workflow to Account for Uncertainty in Well-Log Data in 3D Geostatistical Reservoir Modeling. Stanford Digital Repository. Available at: https://purl.stanford.edu/jy305wh2817
Collection
Master's Theses, Doerr School of Sustainability
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