History Matching by Joint Perturbation of Facies Distribution and Net-to-Gross
Abstract/Contents
- Abstract
- This thesis presents a history matching methodology for constraining geological reservoir models to production data under a given training image-based geological model constraint. The key problem addressed in this paper arises in the situation where some aspects of geological model such as the facies anisotropy direction or net-to-gross are unknown or uncertain. The original method employed in this thesis, developed in Caers (2002), relies on a perturbation of the local facies distribution model, perturbing an initial realization iteratively until history match is achieved. The property of the perturbation is such that a geological concept expressed in a training image is preserved. The magnitude of perturbation is found through solving a simple optimization problem. However, the present method does not account for possible inconsistencies between production data and the geological concept. What if the estimates of the facies net-to-gross or orientation are different from the actual reservoir? Such inconsistencies might result in an unsatisfactory history match. In this thesis, we develop a history-matching algorithm that, during the history matching process, corrects and updates some aspects of geostatistical model such as the net-to-gross ratio. The result is often a faster history match but also a quantification of net-to-gross uncertainty reduction by integrating production data.
Description
| Type of resource | text |
|---|---|
| Date created | June 2003 |
Creators/Contributors
| Author | Kim, Junrae |
|---|---|
| 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
- Kim, Junrae. (2003). History Matching by Joint Perturbation of Facies Distribution and Net-to-Gross. Stanford Digital Repository. Available at: https://purl.stanford.edu/zv303mp7402
Collection
Master's Theses, Doerr School of Sustainability
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