Gaussian realizations of a subsurface reservoir model for computational research

<a href="https://embed.stanford.edu/iframe/?url=https%3A%2F%2Fpurl.stanford.edu%2Fps247qy3964" class="su-underline">Show Content</a>

Abstract/Contents

Abstract: This data set contains 400 realizations of a subsurface reservoir example of size 28x30. The models were used in a 2016 paper for the comparison of Gauss--Newton and Levenberg--Marquardt algorithms in estimating the model parameters through solving the associated nonlinear inverse problem.

Type of resource	software, multimedia
Date created	2016

Author	Shirangi, M. G.

Subject	Inverse problems
Subject	Data assimilation
Subject	Model calibration
Subject	Computational optimization
Subject	Gauss-Newton
Subject	Levenberg-Marquardt
Subject	Estimation
Subject	Forecasting
Subject	Singular value decomposition
Subject	Uncertainty quantification
Genre	Dataset

Related Publication	Shirangi, M. G., Emerick, A. A. (2016). An improved TSVD-based Levenberg-Marquardt algorithm for history matching and comparison with Gauss-Newton. Journal of Petroleum Science and Engineering, 143, 258-271. http://dx.doi.org/10.1016/j.petrol.2016.02.026
Location	https://purl.stanford.edu/ps247qy3964

Use and reproduction: User agrees that, where applicable, content will not be used to identify or to otherwise infringe the privacy or confidentiality rights of individuals. Content distributed via the Stanford Digital Repository may be subject to additional license and use restrictions applied by the depositor.
License: This work is licensed under a Creative Commons Attribution 3.0 Unported license (CC BY).

Preferred Citation: Shirangi, M. G., Emerick, A. A. (2016). An improved TSVD-based Levenberg–Marquardt algorithm for history matching and comparison with Gauss–Newton. Journal of Petroleum Science and Engineering, 143, 258-271.

Stanford Research Data

Loading usage metrics...