Øyvind Eide

Write parameter sampling and storage in python

1

This function: https://github.com/equinor/ert/blob/b916432a99686340ba62c7121f32868a02299ad7/libres/lib/enkf/enkf_state.cpp#L100 is responsible for [sampling ](https://github.com/equinor/ert/blob/b916432a99686340ba62c7121f32868a02299ad7/libres/lib/enkf/enkf_state.cpp#L123)the prior and [storing ](https://github.com/equinor/ert/blob/b916432a99686340ba62c7121f32868a02299ad7/libres/lib/enkf/enkf_state.cpp#L124)it. We would like to move this logic into python, split up sampling and saving of parameters.

Move enkf_node implementation SUMMARY to python

2

We have been doing refactoring of the callbacks, and should be ready to save data differently from before. This includes removing `enkf_node` implementation `SUMMARY` which is loading responses from the...

Save configuration information in base_run_model

2

The settings used for updates, scaling factors, etc. should be persisted when we run an experiment.

Get information about parameter/response saving to fs

3

Currently we are passing EnsembleConfig whenever we are saving and loading to `fs`, we should extract that data and make storage responsible instead.

We would like to be able to write directly from ert to dark storage which is currently read only. That will require some refactoring of the way ert stores data...

This might not be used by anyone, and should check if it could be removed. It is a slight variation of `GEN_KW`, but unsure if anyone is actually using it.

**Issue** Resolves #my_issue **Approach** _Short description of the approach_ ## Pre review checklist - [ ] Added appropriate release note label - [ ] PR title captures the intent of...

Move deactivation of observation due to missing forward model

2

It seems a bit strange to deactivate an observation when fetching it if the forward model has failed, that means the observation knows too much. Moving that knowledge. **Issue** Resolves...

Remove analysis update writing to storage while updating

4

Today the update algorithm will save and load parameters to storage while updating. We should replace this with keeping them in memory, and only writing once the update is completed.

After refactoring the analysis module this should no longer be relevant and can be removed: https://github.com/equinor/ert/blob/49f1617944e38a6a53fa2836316324f2f67711e6/res/enkf/analysis_config.py#L254