diff --git a/dev-requirements.txt b/dev-requirements.txt
index 2c7a267..ecd111a 100644
--- a/dev-requirements.txt
+++ b/dev-requirements.txt
@@ -3,5 +3,6 @@ mypy
pylint
pytest-cov
pytest-xdist
+ruff
sphinx
sphinx-rtd-theme
diff --git a/docs/_images/hello_world_3_plopm.png b/docs/_images/hello_world_3_plopm.png
deleted file mode 100644
index 1c72568..0000000
Binary files a/docs/_images/hello_world_3_plopm.png and /dev/null differ
diff --git a/docs/_sources/configuration_file.rst.txt b/docs/_sources/configuration_file.rst.txt
index 70049c6..509f3d0 100644
--- a/docs/_sources/configuration_file.rst.txt
+++ b/docs/_sources/configuration_file.rst.txt
@@ -18,10 +18,10 @@ The first input parameter is:
:linenos:
# Set mpirun, the full path to the flow executable, and simulator flags (except --output-dir)
- flow = "flow --newton-min-iterations=1"
+ flow_command = "flow --newton-min-iterations=1"
If **flow** is not in your path, then write the full path to the executable, as well as adding mpirun
-if this is supported in your machine (e.g., flow = "mpirun -np 8 /Users/dmar/Github/opm/build/opm-simulators/bin/flow -\-newton-min-iterations=1").
+if this is supported in your machine (e.g., flow_command = "mpirun -np 8 /Users/dmar/Github/opm/build/opm-simulators/bin/flow -\-newton-min-iterations=1").
The next entries define the following parameters:
@@ -30,32 +30,31 @@ The next entries define the following parameters:
:lineno-start: 4
# Set the model parameters
- field = "norne" # Geological model (norne or drogon)
- mode = "ert" # Mode to run (single-run, files, or ert)
- X = [0,2,0,2,2,0,2,0,2,0,2,0,2,2,0,2,0,2,2,0,2,0,2,2,0,2,0,2,2,0,2,0,2,0,2,0,2,2,0,2,2,0,2,2,2,2,0] # Array of x-coarsening
- Y = [0,2,0,2,2,0,2,0,2,2,0,2,0,2,2,0,2,0,2,2,0,2,0,2,2,0,2,0,2,2,0,2,0,2,2,0,2,0,2,2,0,2,0,2,2,0,2,0,2,2,0,2,0,2,2,0,2,0,2,2,0,2,0,2,0,2,0,2,2,0,2,0,2,2,0,2,0,2,2,0,2,0,2,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,2,2,2,2,2,2,2,2,0] # Array of y-coarsening
- Z = [0,0,2,0,0,2,2,2,2,2,0,2,2,2,2,2,0,0,2,0,2,2,0] # Array of z-coarsening
- net = 2 # Number of ensembles
- mep = 2 # Maximum number of ensembles running in parallel
- mrt = 600 # Maximum runtime in seconds of a realization. A value of 0 means unlimited runtime.
- mrn = 2 # Minimum number of realizations that must have succeeded for the simulation to be regarded as a success.
- rds = 7 # Set a specific seed for reproducibility. A value of 0 means no seed.
- obs = "observations_training" # Name of the observation file for the hm ('observations_training', 'observations_test', or 'observations_complete')
- deck = 0 # Select which coarser deck to use: 0 -> default one or 1 -> LET sat functions
- letsatn = 1 # For norne: for the LET coarser deck, select: 0 -> SATNUM=1, 1 -> SATNUM is computed from Sandve et al 2022, 2 -> #SATNUM=#Cells.
- cporv = 0 # 0 -> no corrections for lost porv, 1 -> correct it on all cells
- initial = 0 # Initialization 0 -> Equil 1->INIT from fine-scale
- error = [0.1,0.1,0.1] # Error WWPR, WOPR, and WGPR
- minerror = [100,100,100000] # Minimum error of WWPR, WOPR, and WGPR
- date = 2005-03-01 # Last date to HM
- suffixes = ["PRT"] # Delete files with this suffix to save storage
+ model_name = "norne" # Geological model (norne or drogon)
+ execution_mode = "single-run" # Mode to run (single-run, files, or ert)
+ x_coarsening = [0,2,0,2,2,0,2,0,2,0,2,0,2,2,0,2,0,2,2,0,2,0,2,2,0,2,0,2,2,0,2,0,2,0,2,0,2,2,0,2,2,0,2,2,2,2,0] # Array of x-coarsening
+ y_coarsening = [0,2,0,2,2,0,2,0,2,2,0,2,0,2,2,0,2,0,2,2,0,2,0,2,2,0,2,0,2,2,0,2,0,2,2,0,2,0,2,2,0,2,0,2,2,0,2,0,2,2,0,2,0,2,2,0,2,0,2,2,0,2,0,2,0,2,0,2,2,0,2,0,2,2,0,2,0,2,2,0,2,0,2,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,2,2,2,2,2,2,2,2,0] # Array of y-coarsening
+ z_coarsening = [0,0,2,0,0,2,2,2,2,2,0,2,2,2,2,2,0,0,2,0,2,2,0] # Array of z-coarsening
+ ensemble_size = 2 # Number of ensembles
+ max_parallel_realizations = 2 # Maximum number of ensembles running in parallel
+ max_realization_runtime_seconds = 600 # Maximum runtime in seconds of a realization. A value of 0 means unlimited runtime.
+ min_successful_realizations = 2 # Minimum number of realizations that must have succeeded for the simulation to be regarded as a success.
+ random_seed = 7 # Set a specific seed for reproducibility. A value of 0 means no seed.
+ saturation_function_method = 0 # Select which coarser deck to use: 0 -> default one or 1 -> LET sat functions
+ satnum_generation_method = 1 # For norne: for the LET coarser deck, select: 0 -> SATNUM=1, 1 -> SATNUM is computed from Sandve et al 2022, 2 -> #SATNUM=#Cells.
+ pore_volume_correction = 0 # 0 -> no corrections for lost porv, 1 -> correct it on all cells
+ initialization_method = 0 # Initialization 0 -> Equil 1->INIT from fine-scale
+ observation_relative_errors = [0.1,0.1,0.1] # Error WWPR, WOPR, and WGPR
+ observation_minimum_errors = [100,100,100000] # Minimum error of WWPR, WOPR, and WGPR
+ history_matching_end_date = 2005-03-01 # Last date to HM
+ cleanup_file_suffixes = ["PRT"] # Delete files with this suffix to save storage
The single-run mode results in Flow running only one simulation and the information shown in
the terminal is the one from the Flow executable instead of the ERT one. The ert option calls the ERT
executable and the command values are given after in the same input file which are discussed later. The
files option only writes the needed input files for ERT.
-The X, Y, and Z inputs define which pillars are removed (value 2) in the x, y, and z direction respectively.
+The x-, y, and z-coarsening inputs define which pillars are removed (value 2) in the x, y, and z direction respectively.
.. figure:: figs/index.png
.. figure:: figs/index_plopm.png
@@ -70,8 +69,7 @@ Values of 0 do not remove the pilar.
The remaining lines define the number of ensembles, maximum number of ensembles running in parallel, the maximum runtime in seconds to stop a realization
(0 means unlimited time), the minimum number of realizations for the simulation to be regarded as a success,
-the random seed for reproducibility (0 means no seed), the observation file for the hm
-('observations_training.data', 'observations_test.data', or 'observations_complete.data'), to use the saturation
+the random seed for reproducibility (0 means no seed), to use the saturation
functions from opm-tests norne or to use the LET saturation functions, defining if each of the coarser cells is
considered as only one region, regions as in Sandve et al 2022 or as a different SATNUM region, and the cporv entry
sets if the ntg and poro properties on the cells on the boundary are modified to account for lost PV respect to the
@@ -85,7 +83,7 @@ If the ert option is activated, then the following input:
:lineno-start: 25
# Set the command line for the ert executable (gui, test_run, ensemble_experiment, ensemble_smoother, iterative_ensemble_smoother, and es_mda) and flags
- ert = "es_mda --weights 1"
+ ert_arguments = "es_mda --weights 1"
sets the type of ert option and command flags. Currently, the options supported are gui, test_run, ensemble_experiment, ensemble_smoother, iterative_ensemble_smoother, and es_mda.
Confer to the `ERT 
pycopm.utils.coarsening module
+Coarsen corner-point grids and aggregate reservoir properties.
+The module supports deck-based coarsening and the TOML workflows used to +generate reduced Norne and Drogon models.
+-
+
- +class pycopm.utils.coarsening.CoarseningMaps(x: NDArray, y: NDArray, z: NDArray, cell_groups: NDArray, coarsened_axes: str, matrix_mask: NDArray, vertical_transfer_enabled: bool, reference_to_coarse: list[int] = <factory>, nnc_text: str = 'NNC\n', coarse_tranx: NDArray = <factory>, coarse_trany: NDArray = <factory>, dual_tranx: NDArray = <factory>, dual_trany: NDArray = <factory>, dual_defaults: dict[str, float]=<factory>) +
Bases:
+objectStore mappings and intermediate values used during coarsening.
+-
+
- +cell_groups: NDArray +
One-based coarse-cell identifier for each original cell, flattened in +
+(z, y, x)order.
-
+
- +coarse_tranx: NDArray +
Horizontal x-direction transmissibilities for the matrix or +single-porosity coarse grid.
+
-
+
- +coarse_trany: NDArray +
Horizontal y-direction transmissibilities for the matrix or +single-porosity coarse grid.
+
-
+
- +coarsened_axes: str +
Concatenated names of the coarsened axes, for example
+"xz".
-
+
- +dual_defaults: dict[str, float] +
Default property values inserted into separator rows of the extended +dual-porosity grid.
+
-
+
- +dual_tranx: NDArray +
Horizontal x-direction transmissibilities for the fracture continuum of +a dual-porosity grid.
+
-
+
- +dual_trany: NDArray +
Horizontal y-direction transmissibilities for the fracture continuum of +a dual-porosity grid.
+
-
+
- +matrix_mask: NDArray +
Per-cell mask separating matrix cells (one) from fracture or non-net +cells (zero) in dual-porosity models.
+
-
+
- +nnc_text: str +
NNC include-file content accumulated while mapping non-neighbouring and +matrix-fracture connections.
+
-
+
- +reference_to_coarse: list[int] +
Coarse-cell identifier for each reference-grid cell, populated while +properties are coarsened.
+
-
+
- +vertical_transfer_enabled: bool +
Whether vertical matrix-fracture transfer connections are retained.
+
-
+
- +x: NDArray +
Axis array marking boundaries removed by coarsening in the x direction. +Values greater than one identify intervals merged with the preceding +interval.
+
-
+
- +y: NDArray +
Axis array marking boundaries removed by coarsening in the y direction. +Values greater than one identify intervals merged with the preceding +interval.
+
-
+
- +z: NDArray +
Axis array marking boundaries removed by coarsening in the z direction. +Values greater than one identify intervals merged with the preceding +interval.
+
-
+
- +pycopm.utils.coarsening._collect_removed_zcorn_indices(dck: ConfigViaDeck, coa_z: NDArray, removal_indices: list[int]) list[int] +
Add the ZCORN indices removed by vertical coarsening.
+
-
+
- +pycopm.utils.coarsening._compact_permeability_properties(dck: ConfigViaDeck, permx: NDArray, permy: NDArray, permz: NDArray, modified_deck: list[str]) None +
Use COPY and MULTIPLY if PERMY and PERMZ can be generated from PERMX.
+
-
+
- +pycopm.utils.coarsening._find_active_neighbors(dck: ConfigViaDeck, neighbor_indices: list[int], cluster_id: int, distance: int, offset: int, ijk: list) list[int] +
Find active neighbouring cells for pore-volume redistribution.
+
-
+
- +pycopm.utils.coarsening._find_include_statement(modified_deck: list[str], include_line: str) tuple[int, int] +
Return the list interval containing an INCLUDE statement.
+
-
+
- +pycopm.utils.coarsening._global_index_to_ijk(dck: ConfigViaDeck, global_index: int) tuple[int, int, int] +
Return the i, j, and k indices from a zero-based global cell index.
+
-
+
- +pycopm.utils.coarsening._group_maximum_zero_based(values: NDArray, groups: NDArray, number_groups: int) NDArray +
-
+
- +pycopm.utils.coarsening._group_minimum_zero_based(values: NDArray, groups: NDArray, number_groups: int) NDArray +
-
+
- +pycopm.utils.coarsening._group_sum_zero_based(values: NDArray, groups: NDArray, number_groups: int) NDArray +
-
+
- +pycopm.utils.coarsening._grouped_count(values: NDArray, groups: NDArray, size: int | None = None) NDArray +
Return the number of non-NaN values for each one-based group.
+
-
+
- +pycopm.utils.coarsening._grouped_first(values: NDArray, groups: NDArray, size: int | None = None) NDArray +
Return the first non-NaN value for each one-based group.
+
-
+
- +pycopm.utils.coarsening._grouped_last(values: NDArray, groups: NDArray, size: int | None = None) NDArray +
Return the last non-NaN value for each one-based group.
+
-
+
- +pycopm.utils.coarsening._grouped_max(values: NDArray, groups: NDArray, size: int | None = None) NDArray +
Return the maximum value for each one-based group, ignoring NaNs.
+
-
+
- +pycopm.utils.coarsening._grouped_mean(values: NDArray, groups: NDArray, size: int | None = None) NDArray +
Return the mean value for each one-based group, ignoring NaNs.
+
-
+
- +pycopm.utils.coarsening._grouped_min(values: NDArray, groups: NDArray, size: int | None = None) NDArray +
Return the minimum value for each one-based group, ignoring NaNs.
+
-
+
- +pycopm.utils.coarsening._grouped_mode(values: NDArray, group_codes: NDArray, number_groups: int) NDArray +
Return the smallest mode for each zero-based group, ignoring NaNs.
+
-
+
- +pycopm.utils.coarsening._grouped_sum(values: NDArray, groups: NDArray, size: int | None = None) NDArray +
Return the sum of values for each one-based group.
+
-
+
- +pycopm.utils.coarsening._interleave_dual_property(property_values: NDArray, dual_values: NDArray, nx: int, nz: int, default_value: float = 0) NDArray +
Interleave property and dual-property layers with separator rows.
+
-
+
- +pycopm.utils.coarsening._read_satnum(cfg: ConfigViaTOML, actnum: NDArray, nxyz: int, satnum_opm: NDArray) NDArray +
Read or generate fine-grid SATNUM values.
+-
+
- Parameters: +
-
+
cfg – TOML configuration controlling the SATNUM source.
+actnum – Fine-grid active-cell mask.
+nxyz – Number of fine-grid cells.
+satnum_opm – SATNUM values read from the reference INIT file.
+
+- Returns: +
SATNUM value for every fine-grid cell.
+
+- Return type: +
NDArray
+
+
-
+
- +pycopm.utils.coarsening.build_dual_porosity_grid(dck: ConfigViaDeck, coarsening: CoarseningMaps, cr: NDArray, zc: NDArray) tuple[NDArray, NDArray] +
Extend a coarsened grid with a second porosity continuum.
+The matrix and fracture grids are separated in the j direction, and their +connections are added to
+coarsening.nnc_text.-
+
- Parameters: +
-
+
dck – Deck configuration for the coarsened model.
+coarsening – Coarsening data containing continuum masks and transmissibilities.
+cr – Coarsened
COORDandZCORNarrays.
+zc – Coarsened
COORDandZCORNarrays.
+
+- Returns: +
Extended dual-porosity grid arrays.
+
+- Return type: +
coord, zcorn
+
+
-
+
- +pycopm.utils.coarsening.coarsen_and_write_properties(cfg: ConfigViaTOML, coa_map: NDArray) int +
Aggregate and write properties for a TOML-generated model.
+-
+
- Parameters: +
-
+
cfg – TOML configuration and reference-case settings.
+coa_map – One-based fine-to-coarse cell mapping.
+
+- Returns: +
Highest generated SATNUM value, used as the number of saturation tables.
+
+- Return type: +
int
+
+
-
+
- +pycopm.utils.coarsening.coarsen_corner_point_grid(dck: ConfigViaDeck, coarsening: CoarseningMaps) tuple[NDArray, NDArray] +
Remove selected pillars and ZCORN surfaces from the grid.
+-
+
- Parameters: +
-
+
dck – Deck configuration containing the original corner-point grid.
+coarsening – Axis mappings defining the removed rows, columns, and layers.
+
+- Returns: +
Coarsened arrays when dual porosity is enabled; otherwise empty arrays.
+
+- Return type: +
coord, zcorn
+
+
-
+
- +pycopm.utils.coarsening.coarsen_properties(dck: ConfigViaDeck, coarsening: CoarseningMaps, modified_deck: list[str], wellcind: list[int]) tuple[NDArray, NDArray, NDArray] +
Aggregate reservoir properties onto the coarsened grid.
+Continuous properties use their configured or property-specific aggregation; +discrete properties use
+min,max, ormode. The function writes +property include files and updates output pore volume and active cells.-
+
- Parameters: +
-
+
dck – Deck configuration and source INIT or restart properties.
+coarsening – Cell groups and masks created by
create_coarsening_maps().
+modified_deck – Deck lines updated with generated property includes.
+wellcind – Coarse-cell indices containing well completions.
+
+- Returns: +
Activity summaries and the mask used to remove depth-jump cells.
+
+- Return type: +
cluster_minimum, cluster_maximum, removal_mask
+
+
-
+
- +pycopm.utils.coarsening.create_coarsening_map(cfg: ConfigViaTOML) NDArray +
Map each fine-grid cell to a one-based coarse-cell identifier.
+The output dimensions and original-to-output axis mappings in
+cfgare also +updated.-
+
- Parameters: +
cfg – TOML configuration containing the axis coarsening arrays.
+
+- Returns: +
One-based coarse-cell identifier for every fine-grid cell.
+
+- Return type: +
NDArray
+
+
-
+
- +pycopm.utils.coarsening.create_coarsening_maps(dck: ConfigViaDeck, cmdargs) CoarseningMaps +
Create axis mappings and assign original cells to coarse cells.
+-
+
- Parameters: +
-
+
dck – Deck configuration whose output dimensions are updated.
+cmdargs – Command arguments containing
coarsening,x_coarsening, +y_coarsening, andz_coarsening.
+
+- Returns: +
Axis mappings, cell groups, and dual-porosity masks.
+
+- Return type: +
- + +
-
+
- +pycopm.utils.coarsening.map_nnc_transmissibilities(dck: ConfigViaDeck, coarsening: CoarseningMaps) None +
Map original non-neighbouring transmissibilities to the coarse grid.
+Connections that become Cartesian neighbours are accumulated in
+TRANXor +TRANY; remaining connections are written as NNC records.-
+
- Parameters: +
-
+
dck – Deck configuration and source NNC data.
+coarsening – Coarse mapping updated with transmissibilities and NNC text.
+
+
-
+
- +pycopm.utils.coarsening.redistribute_removed_pore_volume(dck: ConfigViaDeck, con: NDArray, cluster_minimum: NDArray, cluster_maximum: NDArray, removal_mask: NDArray) None +
Redistribute pore volume from removed coarse cells.
+Pore volume is divided among the nearest active neighbours without changing +the total pore volume.
+-
+
- Parameters: +
-
+
dck – Deck configuration whose
output_porvis updated.
+con – One-based coarse-cell identifier for each original cell.
+cluster_minimum – Aggregated activity values used to identify changed clusters.
+cluster_maximum – Aggregated activity values used to identify changed clusters.
+removal_mask – Mask identifying retained coarse cells.
+
+