Generation of Multiple History Match Models Using Multistart Optimization
Abstract/Contents
- Abstract
- Uncertainty in the geological model presents a key challenge in development decisions. Production data from the field are acquired only at limited locations and are sparse. Time-lapse seismic data is available field-wide but has limited resolution. In addition, increasingly production logging data is being recorded in wells, which provide information regarding vertical heterogeneity between wells. The available data set is still sparse for accurately modeling spatial distribution of reservoir properties. Hence, multiple geological realizations can exists which match the given production history and generate varying forecast, all of which should be analyzed for decision-making. In my research thesis, two optimization algorithms have been tested for generating multiple history- matched geological models. The reservoir inversion problem has been formulated using optimization technique, with an objective of minimizing the variance between observations and output of numerical models using one, two and all three datasets as described above. Optimization is carried out in reduced model space. Model reduction is achieved by spatial principal component analysis (PCA), where optimization search space is projected to a subspace of much smaller dimension. Local optimizers often tend to find solutions faster than global methods, though they can be trapped in local minima. Randomly generated multiple initial points can be optimized in parallel to locate multiple models matching history. Hook-Jeeves direct search (HJDS) algorithm, simultaneous perturbation stochastic approximation (SPSA) algorithm has been used for optimization and results are compared with rejection sampler. The minima points identified through optimization represent geological models that are consistent with the production history of the field. The methodology has been tested on three different synthetic case studies with both categorical variable and continuous variables The optimization process locates geological models that are consistent with production history but present a varying forecast which can help in decision analysis.
Description
| Type of resource | text |
|---|---|
| Date created | June 2012 |
Creators/Contributors
| Author | Choudhary, Manish K. |
|---|---|
| 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
- Choudhary, Manish K. (2012). Generation of Multiple History Match Models Using Multistart Optimization. Stanford Digital Repository. Available at: https://purl.stanford.edu/cv601tz9723
Collection
Master's Theses, Doerr School of Sustainability
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