Optimization of Shale Resource Development Using Reduced-Physics Surrogate Models
Abstract/Contents
- Abstract
- The economics of oil and gas field development can be improved significantly by using computational optimization to guide operations. In this work, we present a general workflow for applying optimization to the development of shale gas reservoirs. Starting with a detailed full-physics simulation model, which includes heterogeneous geology, highly-resolved fracture networks, dual-porosity, dual-permeability regions, and gas desorption, the approach first entails the generation of a much simpler, and much more computationally efficient, reduced-physics surrogate model. The reduced-physics model is tuned using a procedure akin to history matching to provide results in close agreement with the full-physics model. The surrogate model is then used for field development optimization. During the course of the optimization, the surrogate model is periodically retrained to match the full-physics representation of the current best solution. In the optimizations considered here, we apply a direct search technique (generalized pattern search) and seek to determine the optimal locations, lengths, and number of fracture stages for a set of horizontal wells. In two examples, involving three-dimensional models with heterogenous properties representative of the Barnett Shale, optimization is shown to provide field development scenarios with net present values that are more than double those of base case designs.
Description
| Type of resource | text |
|---|---|
| Date created | May 2012 |
Creators/Contributors
| Author | Wilson, Kurt Caylor |
|---|---|
| Primary advisor | Durlofsky, Louis |
| 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
- Wilson, Kurt Caylor. (2012). Optimization of Shale Resource Development Using Reduced-Physics Surrogate Models. Stanford Digital Repository. Available at: https://purl.stanford.edu/zh266tg6451
Collection
Master's Theses, Doerr School of Sustainability
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