pyesmda 0.3.1

Python Ensemble Smoother with Multiple Data Assimilations

pyESMDA

Python Ensemble Smoother with Multiple Data Assimilations

This package is an object-oriented python implementation of the ES-MDA algorithm based on the work of 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.

The original python implementation was by Muhammad Iffan Hannanu (https://github.com/iffanh/Playground).

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

0.3.1 (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.