1887
  1. Home
  2. Conferences
  3. Conference Proceedings
  4. Second EAGE Workshop on Machine Learning
  5. Conference paper

Abstract

Summary

In this paper we propose Bayesian history matching workflow with the application of machine learning algorithms and discuss its implementation to synthetic and real hydrodynamic models. Since the traditional Bayesian approach is computationally intensive we suggest substituting simulator runs with a machine-learning algorithm to estimate the objective function value for generated models. For this purpose, we consider Kernel Ridge Regression trained on prior models obtained from the Latin Hypercube Sampling procedure. The workflow allows to reduce the uncertainty of model parameters and to indicate objective function sensitivity to model parameters.

© EAGE Publications BV
Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.202132014
2021-03-08
2024-04-26

  • From This Site

    /content/papers/10.3997/2214-4609.202132014
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Journal -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Maschio, C., and Schiozer, D. J.
    [2014] Bayesian history matching using artificial neural network and Markov Chain Monte Carlo. Journal of Petroleum science and Engineering, 123, 62–71.
     [Google Scholar]
  2. Oliver, D. S., Reynolds, A. C., and Liu, N.
    [2008] Inverse theory for petroleum reservoir characterization and history matching.
     [Google Scholar]
  3. Sun, W., and Durlofsky, L. J.
    [2017] A new data-space inversion procedure for efficient uncertainty quantification in subsurface flow problems. Mathematical Geosciences, 49(6), 679–715.
     [Google Scholar]
  4. Zhang, Y., Duchi, J., and Wainwright, M.
    [2013] Divide and conquer kernel ridge regression.Conference on learning theory, 592–617.
     [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.202132014
Loading
Bayesian history matching with machine learning objective function estimator
Conference Proceedings 2021, 1 (2021); https://doi.org/10.3997/2214-4609.202132014
/content/papers/10.3997/2214-4609.202132014
/content/papers/10.3997/2214-4609.202132014
Loading

Data & Media loading...

Most Cited This Month Most Cited RSS feed

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