Iterative-Ensemble-Smoother

Version 1.0.0 (29.9 KB) by Xiadong Luo
An iterative ensemble smoother (iES) based on regularized Levenburg-Marquardt, see SPE-176023-PA, https://doi.org/10.2118/176023-PA
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An iterative ensemble smoother (iES) based on regularized Levenburg-Marquardt, see the paper "Iterative Ensemble Smoother as an Approximate Solution to a Regularized Minimum-Average-Cost Problem: Theory and Applications ", by Luo et al., SPE-176023-PA, https://doi.org/10.2118/176023-PA .

This depository contains an MATLAB implementation of the aforementioned iES, which is most of the time used in ensemble-based reservoir data assimilation (also known as history matching) problems. Our main purpose here is to indicate how this iES is actually implemented in our in-house history matching workflow. For this purpose, here we apply this iES to estimate initial conditions of the Lorentzen 96 model (rather than parameters of reservoir models).

Cite As

Luo, Xiaodong, et al. “Iterative Ensemble Smoother as an Approximate Solution to a Regularized Minimum-Average-Cost Problem: Theory and Applications.” SPE Journal, vol. 20, no. 05, Society of Petroleum Engineers (SPE), Oct. 2015, pp. 0962–82, doi:10.2118/176023-pa.

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1.0.0

To view or report issues in this GitHub add-on, visit the GitHub Repository.
To view or report issues in this GitHub add-on, visit the GitHub Repository.