History Matching of the Norne Full Field Model Using an Iterative Ensemble Smoother - (SPE-164902)

Chen, Y.; Oliver, Dean S.

doi:10.3997/2214-4609.20130160

Cited by 8 publications

(3 citation statements)

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“…The new EnRML algorithm produces results that are identical to the old formulation, at least up to round-off and truncation errors, and for N − 1 ≤ M. Therefore, since there are already a large number of studies of EnRML with reservoir cases (e.g. Chen and Oliver, 2013a;Emerick and Reynolds, 2013b), adding to this does not seem necessary.…”

Section: Benchmark Experimentsmentioning

confidence: 94%

Revising the stochastic iterative ensemble smoother

Raanes

Stordal

Evensen

2019

Nonlin. Processes Geophys.

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Abstract. Ensemble randomized maximum likelihood (EnRML) is an iterative (stochastic) ensemble smoother, used for large and nonlinear inverse problems, such as history matching and data assimilation. Its current formulation is overly complicated and has issues with computational costs, noise, and covariance localization, even causing some practitioners to omit crucial prior information. This paper resolves these difficulties and streamlines the algorithm without changing its output. These simplifications are achieved through the careful treatment of the linearizations and subspaces. For example, it is shown (a) how ensemble linearizations relate to average sensitivity and (b) that the ensemble does not lose rank during updates. The paper also draws significantly on the theory of the (deterministic) iterative ensemble Kalman smoother (IEnKS). Comparative benchmarks are obtained with the Lorenz 96 model with these two smoothers and the ensemble smoother using multiple data assimilation (ES-MDA).

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Section: Benchmark Experimentsmentioning

confidence: 94%

Revising the stochastic iterative ensemble smoother

Raanes

Stordal

Evensen

2019

Nonlin. Processes Geophys.

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“…Therefore, since there are already a large number of studies of EnRML with reservoir cases [e.g. Chen and Oliver, 2013a;Emerick and Reynolds, 2013b], adding to this does not seem necessary.…”

Section: Benchmark Experimentsmentioning

confidence: 99%

Revising the stochastic iterative ensemble smoother

Raanes¹,

Evensen²,

Stordal³

2019

Preprint

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Ensemble randomized maximum likelihood (EnRML) is an iterative (stochastic) ensemble smoother, used for large and nonlinear inverse problems, such as history matching and data assimilation. Its current formulation is overly complicated and has issues with computational costs, noise, and covariance localization, even causing some practitioners to omit crucial prior information. This paper resolves these difficulties and streamlines the algorithm, without changing its output. These simplifications are achieved through the careful treatment of the linearizations and subspaces. For example, it is shown (a) how ensemble linearizations relate to average sensitivity, and (b) that the ensemble does not lose rank during updates. The paper also draws significantly on the theory of the (deterministic) iterative ensemble Kalman smoother (IEnKS). Comparative benchmarks are obtained with the Lorenz-96 model with these two smoothers and the ensemble smoother using multiple data assimilation (ES-MDA).

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“…After the introduction of the Ensemble Randomized Maximum Likelihood (EnRML) (CHEN; OLIVER, 2012) and the Ensemble Smoother with Multiple Data Assimilations (ES-MDA) (EMERICK; REYNOLDS, 2013), the applications of iterative ensemble smoothers have become possible in large-scale field reservoirs. Some examples of field applications are found in Oliver (2013a) andEmerick (2016). Here, we recall the class of methods based on EnRML and ES-MDA as Iterative Ensemble Smoother (IES) methods.…”

Section: Ensemble-based Methodsmentioning

confidence: 99%

Conjunto suavizado iterativo com localização adaptativa e parametrização com aprendizado profundo em reservatórios carbonáticos.

Ranazzi

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I am deeply grateful to my advisor, Prof. Marcio Sampaio, for his continuous support, guidance, and unwavering commitment to my academic and professional development. His expertise, invaluable insights, and willingness to share his knowledge have significantly influenced the outcome of this research. I would like to extend my sincere thanks to the Energy Production Innovation Center (EPIC), FAPESP and Equinor for their generous financial support throughout the course of this research. Their investment in this project has enabled me to pursue in-depth investigations and achieve meaningful results. I am grateful to the UNISIM group for providing the simulation models that were crucial for conducting this study. Their contribution has greatly enhanced the accuracy and reliability of the research outcomes. Additionally, I would like to acknowledge the Computer Modelling Group (CMG) for providing the essential licenses of the reservoir flow simulator. Lastly, I want to express my gratitude to the Polytechnic School of the University of São Paulo (USP) and the Laboratory of Petroleum Reservoir Simulation and Management (LASG) for providing the necessary infrastructure and resources that facilitated the progress of this research. Their commitment to academic excellence and dedication to fostering a conducive research environment have been invaluable. "Explore the world. Nearly everything is really interesting if you go into it deeply enough."

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History Matching of the Norne Full Field Model Using an Iterative Ensemble Smoother - (SPE-164902)

Cited by 8 publications

References 0 publications

Revising the stochastic iterative ensemble smoother

Revising the stochastic iterative ensemble smoother

Revising the stochastic iterative ensemble smoother

Conjunto suavizado iterativo com localização adaptativa e parametrização com aprendizado profundo em reservatórios carbonáticos.

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