Python Ensemble Smoother with Multiple Data Assimilation
Project description
pyESMDA
Python Ensemble Smoother with Multiple Data Assimilation
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 Assimilation (ES-MDA) algorithm, introduced by Emerick and Reynolds [1-2].
Thanks to its simple formulation, ES-MDA of Emerick and Reynolds (2012) is perhaps the most used iterative form of the ensemble smoother in geoscience applications.
Free software: MIT license
Documentation: https://pyesmda.readthedocs.io.
How to Cite
Software/Code citation for pyESMDA:
Antoine Collet. (2022). pyESMDA - Python Ensemble Smoother with Multiple Data Assimilation (v0.3.2). Zenodo. https://doi.org/10.5281/zenodo.7425670
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.
Changelog
0.3.3 (2022-12-12)
!PR27 STYLE: Add a DOI number from zenodo and correct typos.
0.3.2 (2022-10-07)
!PR21 FIX: design - some static methods should be moved to a utils.py file.
0.3.1 (2022-08-12)
!PR20 Fix ESMDA-RS documentation and change the cov_m_prior input parameter to its diagonal std_m_prior to be consistent with the implementation and be less memory consuming.
0.3.0 (2022-08-12)
!PR15 Implement ESMDA-RS (restricted step) which provides an automatic estimation of the inflation parameter and determines when to stop (number of assimilations) on the fly.
!PR14 Add keyword is_forecast_for_last_assimilation to choose whether to compute the predictions for the ensemble obtained at the last assimilation step. The default is True.
!PR13 Implementation: Faster analyse step by avoiding matrix inversion.
!PR12 Add a seed parameter for the random number generation seed in the prediction perturbation step. To avoid confusion , cov_d has been renamed cov_obs.
!PR11 Implement the covariance localization. Introduces the correlation matrices dd_correlation_matrix and md_correlation_matrix. To avoid confusion , cov_d has been renamed cov_obs.
!PR10 Implement the parameters auto-covariance inflation. Add the estimation of the parameters auto-covariance matrix. The parameter alpha becomes cov_obs_inflation_factors.
0.2.0 (2022-07-23)
!PR6 The parameter stdev_d becomes cov_d.
!PR5 The parameter n_assimilation becomes n_assimilations.
!PR4 The parameter stdev_m is removed.
!PR3 Type hints are now used in the library.
!PR2 Add the possibility to save the history of m and d. This introduces a new knew keyword (boolean) for the constructor save_ensembles_history. Note that the m_mean attribute is depreciated and two new attributes are introduced: m_history, d_history respectively to access the successive parameter and predictions ensemble.
0.1.0 (2021-11-28)
First release on PyPI.
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