1887

Abstract

Summary

In this manuscript, we present and compare the following optimization methods: ensemble optimization, a Sequential Monte Carlo Optimization method, and Powell’s derivative-free optimization. We also introduce a framework for comparing the computational time used by the different methods. The production optimization comparison is evaluated using the Drogon reservoir model prepared by Equinor. The results show that the ensemble-based optimization gives the highest objective value and is the fastest in computational time. However, Powell’s method requires fewer number of function evaluations.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.202510924
2025-06-02
2026-02-10
Loading full text...

Full text loading...

References

  1. Chang, Y., Lorentzen, R.J., Nævdal, G. and Feng, T. [2019] OLYMPUS optimization under geological uncertainty. Computational Geosciences, 24(6), 2027–2042.
    [Google Scholar]
  2. Duan, J.C., Li, S. and Xu, Y. [2023] Sequential Monte Carlo optimization and statistical inference. WIREs Computational Statistics, 15(3), e1598.
    [Google Scholar]
  3. Equinor ASA [2024] Webviz Subsurface Example. https://webviz-subsurface-example.azurewebsites.net/. Accessed: 2024-12-16.
    [Google Scholar]
  4. Oliver, D.S. [2024] Robust Optimization Using the Mean Model with Bias Correction. Mathematical Geosciences.
    [Google Scholar]
  5. Ragonneau, T.M. and Zhang, Z. [2023] PDFO: a cross-platform package for Powell's derivative-free optimization solvers. arXiv.
    [Google Scholar]
/content/papers/10.3997/2214-4609.202510924
Loading
/content/papers/10.3997/2214-4609.202510924
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error