Evaluating Data Conditioning Accuracy of MPS Algorithms and the Impact on Flow Modeling
Abstract/Contents
- Abstract
- This report aims at evaluating the modeling accuracy of Multi Point Statistics (MPS) algorithms, namely, Snesim (Guardino and Srivastav, 1993 and Strebelle, 2002), Real Time Post Processing (RTPP) and Early Stage Resimulation (ESRS) (Suzuki and Strebelle, 2006). In practice, Snesim is one of the most effective tools available to model complex reservoir heterogeneity. Nevertheless, in some cases, it fails to produce connected channels. The latter methods, RTPP and ESRS, were developed to overcome the limitations of Snesim, thereby, improving modeling accuracy. In order to estimate the efficiency of the algorithms in terms of modeling accuracy, the training patterns reproduction and hard data conditioning of each of the algorithms has been compared. While training pattern reproduction is examined using unconditional simulation, conditional simulations are employed to study hard data conditioning. It is observed that all the three algorithms introduce artifacts such as straightening of channels and lower degree of certainty of finding sand, particularly when conditioned to well data. Flow simulation studies in the report quantitatively demonstrate the impact of hard data conditioning on flow response.
Description
| Type of resource | text |
|---|---|
| Date created | June 2008 |
Creators/Contributors
| Author | Saripally, Indira |
|---|---|
| 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
- Saripally, Indira. (2008). Evaluating Data Conditioning Accuracy of MPS Algorithms and the Impact on Flow Modeling. Stanford Digital Repository. Available at: https://purl.stanford.edu/yb610xy6614
Collection
Master's Theses, Doerr School of Sustainability
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