Streamline Assisted History Matching of Naturally Fractured Reservoirs Using the Probability Perturbation Method
Abstract/Contents
- Abstract
- Naturally fractured reservoirs (NFRs) can display complicated flow behavior because of the extreme difference in rock properties between matrix and fracture. Various models are in use for flow simulation of NFRs, among which dual porosity is the simplest and the most commonly used. History matching (HM’ing)—which is adjusting model parameters to match historical production data—has always been a challenge for NFRs, due to the large number of parameters. Conventional HM’ing of NFRs has focused on the shape factor (, a multiplier in the matrix-fracture transfer function) as the primary HM’ing parameter. In this work, it is shown that flow behavior can be more sensitive to the spatial distribution of the fractured zones rather than the shape factor alone. Therefore, this work has focused on assisted HM’ing of NFRs by perturbing the spatial distribution of fractured zones in a reservoir rather than using the shape factor. The dual porosity/single permeability representation is selected because of its simplicity and computational efficiency, which makes it suitable for a HM’ing problem. In this work, the spatial distribution of the fractured zones is modeled by a binary indicator variable designating dual porosity or single porosity blocks (i.e. grid blocks containing significant fractures vs. grid blocks with few fractures). In HM’ing such models, it is important to constrain inverse solutions to spatial geological and/or geophysical information about the fracture network under study. To achieve this, we apply the probability perturbation method (PPM) to the binary variable (fractured zones) subject to geological constraints, until production history is matched to some accepted tolerance. The prior geological constraints are modeled through a training image representing conceptually the geometrical patterns of fracture swarms (areas of high fracture density and connectivity). To improve the convergence rate and to make this method feasible for simultaneous HM’ing of several wells, we use streamline simulation which is not only faster, but also provides specific information that helps making the stochastic approach of PPM more efficient. Most importantly, fracture densities around production wells (in their drainage area) can be inferred from their production mismatches. We demonstrate with practical examples how this method allows HM’ing of dual porosity models, while at the same time honoring geological data and fracture density maps derived from seismic data.
Description
| Type of resource | text |
|---|---|
| Date created | June 2008 |
Creators/Contributors
| Author | Fadaei, Sepehr |
|---|---|
| Primary advisor | Caers, Jef |
| Advisor | Thiele, Marco |
| Degree granting institution | Stanford University, Department of Energy Resources Engineering |
Subjects
| Subject | School of Earth Energy & Environmental Sciences |
|---|---|
| Genre | Thesis |
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Preferred citation
- Preferred Citation
- Fadaei, Sepehr. (2008). Streamline Assisted History Matching of Naturally Fractured Reservoirs Using the Probability Perturbation Method. Stanford Digital Repository. Available at: https://purl.stanford.edu/fn301wc3237
Collection
Master's Theses, Doerr School of Sustainability
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