Exploring Direct Sampling and Iterative Spatial Resampling in History Matching
Abstract/Contents
- Abstract
- We explore multiple methods to history match a porosity field by using both production data and a secondary data set. In this paper, seismic impedance is used as secondary data but any data set with identical dimensions as the primary data can be used. The task is formulated as an inverse problem where the production data is estimated through an upscaled ow simulator. Co-located permeability values are obtained though an empirical relationship from the well logs. The secondary seismic data is treated in two ways. Firstly, it is used as conditioning data to further constrain the porosity simulation. This means that the production data is the only data that has an objective/mist function. In the second method, porosity and seismic impedance are simulated simultaneously. This corresponds to an objective function with combined production and seismic data. If the second method is used, a more constrained porosity field is obtained in which channels follow the seismic more closely, whereas when the first method is used the channels are slightly thinner and more spread out. Because increased amount of conditioning data yields a more constrained a posteriori distribution it is likely that the optimization takes requires less steps to reach an optimum compared with the case where both seismic and production data is matched. In addition, frequency information is also incorporated into the objective function. However, because this information seems to conflict with the previous information, the convergence to an optimal point is decreased. This is a problem that could be mitigated with the right choice of the training image is the geospatial simulation.
Description
| Type of resource | text |
|---|---|
| Date created | June 2010 |
Creators/Contributors
| Author | Haugen, Matz |
|---|---|
| Primary advisor | Mukerji, Tapan |
| 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
- Haugen, Matz. (2010). Exploring Direct Sampling and Iterative Spatial Resampling in History Matching. Stanford Digital Repository. Available at: https://purl.stanford.edu/dn958js5816
Collection
Master's Theses, Doerr School of Sustainability
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