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pyESMDA - Python Ensemble Smoother with Multiple Data Assimilation

integration_instructionsResearch softwarekeyboard_double_arrow_right Software 10 Jul 2022 English Publisher:Zenodo
Authors: Antoine Collet;

doi: 10.5281/zenodo.7425670 , 10.5281/zenodo.8212662 , 10.5281/zenodo.7425669 , 10.5281/zenodo.7426958 , 10.5281/zenodo.8207822

pyESMDA - Python Ensemble Smoother with Multiple Data Assimilation

Abstract

pyesmda is an open-source, pure python, and object-oriented library that provides a user friendly implementation of one of the most popular ensemble based method for parameters estimation and data assimilation: the Ensemble Smoother with Multiple Data Assimilations (ES-MDA) algorithm, introduced by Emerick and Reynolds [1-2]. References [1] Emerick, A. A. and A. C. Reynolds, Ensemble smoother with multiple data assimilation, Computers & Geosciences, 2012. [2] Emerick, A. A. and A. C. Reynolds. (2013). History-Matching Production and Seismic Data in a Real Field Case Using the Ensemble Smoother With Multiple Data Assimilation. Society of Petroleum Engineers - SPE Reservoir Simulation Symposium 1. 2. 10.2118/163675-MS.

available on pypi: https://pypi.org/project/pyesmda/

Related Organizations
Keywords

esmda, inversion, ensemble smoother, inverse problem, parameter estimation, es-mda, stochastic-optimization

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
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