Make regional multipliers for bulkvolume, porosity and permeability

CHANGELOG.md

@@ -5,6 +5,7 @@ This project adheres to [Semantic Versioning](https://semver.org/).
 ## Unreleased
 
 ### Added
+- [#404](https://github.com/equinor/flownet/pull/404) Added possibility for regional multipliers for permeability, porosity and bulkvolume multiplier. Current implementation allows for defining either one global multiplier, or a regional multipliers based on a region parameter extracted from an existing simulation model (typically FIPNUM, EQLNUM, SATNUM etc). The regional multiplier will be in addition to the per tube multipliers. New keys in config yaml are: porosity_regional_scheme (global, individual or regions_from_sim), porosity_regional (define prior same way as for other model parameters) and porosity_parameter_from_sim_model (name of region parameter in simulation model). The same three keys exists for permeability and bulkvolume_mult.
 - [#383](https://github.com/equinor/flownet/pull/383) Added option to either define a prior distribution for KRWMAX directly by using krwmax in the config yaml, or to let KRWMAX be calculated as KRWEND + delta. To do the latter, set krwmax_add_to_krwend to true, and then the prior distribution definition in the config yaml for krwmax will be interpreted as a prior distribution for the delta value to be added to KRWEND to get the KRWMAX.
 - [#386](https://github.com/equinor/flownet/pull/386) Expose FlowNet timeout to user.
 - [#356](https://github.com/equinor/flownet/pull/356) Added option to distribute the original volume over the FlowNet tubes in a Voronoi-diagram style. I.e., areas with a high density of FlowNet tubes get a lower volume per tube.

src/flownet/ahm/_run_ahm.py

@@ -1,5 +1,5 @@
 import argparse
-from typing import Union, List, Optional, Dict
+from typing import Union, List, Optional, Dict, Tuple
 import json
 import pathlib
 from operator import add
@@ -198,6 +198,78 @@ def _get_distribution(
     return df
 
 
+def _get_regional_distribution(
+    parameters: Union[str, List[str]],
+    parameters_config: dict,
+    network: NetworkModel,
+    field_data: FlowData,
+    ti2ci: pd.DataFrame,
+) -> Tuple[pd.DataFrame, pd.DataFrame]:
+    """
+    Create:
+        1) The distribution with min, mean, base, stddev, max for one or more regional parameter
+            multipliers
+        2) A dataframe linking region model index to cell model index for each regional multiplier
+
+    Args:
+        parameters: which parameter(s) should be outputted in the dataframe
+        parameters_config: the parameters definition from the configuration file
+        network: FlowNet network instance
+        field_data: FlowData class with information from simulation model data source
+        ti2ci: A dataframe with index equal to tube model index, and one column which equals cell indices.
+
+    Returns:
+        tuple: a tuple containing:
+            ci2ri (pd.DataFrame): A dataframe with index equal to cell model index, and one column containing
+                region index for each of the requested regional multiplier(s).
+            df_dist_values (pd.DataFrame): A dataframe with distributions for the requested regional multiplier(s)
+    """
+    if not isinstance(parameters, list):
+        parameters = [parameters]
+
+    df = pd.DataFrame(index=ti2ci.index.unique())
+    df_dist_values = pd.DataFrame(index=[0])
+    ci2ri = pd.DataFrame(index=network.grid.index)
+
+    for parameter in parameters:
+        if (
+            getattr(parameters_config, parameter + "_regional_scheme")
+            == "regions_from_sim"
+        ):
+            df[parameter + "_regional"] = pd.DataFrame(
+                _from_regions_to_flow_tubes(
+                    network,
+                    field_data,
+                    ti2ci,
+                    getattr(parameters_config, parameter + "_parameter_from_sim_model"),
+                )
+            )
+        elif getattr(parameters_config, parameter + "_regional_scheme") == "global":
+            df[parameter + "_regional"] = pd.DataFrame(
+                [1] * len(network.grid.model.unique())
+            )
+
+        ci2ri_index = ti2ci.index.values.copy()
+        for idx in np.unique(ti2ci.index.values):
+            ci2ri_index[ti2ci.index.values == idx] = df.loc[
+                idx, parameter + "_regional"
+            ]
+        ci2ri[parameter + "_regional"] = ci2ri_index
+
+        parameter_config_regional = getattr(parameters_config, parameter + "_regional")
+        df_dist_values[f"minimum_{parameter}_regional"] = parameter_config_regional.min
+        df_dist_values[f"maximum_{parameter}_regional"] = parameter_config_regional.max
+        df_dist_values[f"mean_{parameter}_regional"] = parameter_config_regional.mean
+        df_dist_values[f"base_{parameter}_regional"] = parameter_config_regional.base
+        df_dist_values[
+            f"stddev_{parameter}_regional"
+        ] = parameter_config_regional.stddev
+        df_dist_values[
+            f"distribution_{parameter}_regional"
+        ] = parameter_config_regional.distribution
+    return ci2ri, df_dist_values
+
+
 def _constrain_using_well_logs(
     porv_poro_trans_dist_values: pd.DataFrame,
     data: np.ndarray,
@@ -771,6 +843,23 @@ def run_flownet_history_matching(
 
     equil_dist_values = equil_dist_values[equil_dist_values.isnull().sum(axis=1) < 5]
 
+    #########################################
+    #  Region volume multipliers            #
+    #########################################
+
+    regional_parameters = [
+        param
+        for param in {"bulkvolume_mult", "porosity", "permeability"}
+        if getattr(config.model_parameters, param + "_regional_scheme") != "individual"
+    ]
+    (ci2ri, regional_porv_poro_trans_dist_values,) = _get_regional_distribution(
+        regional_parameters,
+        config.model_parameters,
+        network,
+        field_data,
+        ti2ci,
+    )
+
     #########################################
     # Fault transmissibility                #
     #########################################
@@ -832,7 +921,9 @@ def run_flownet_history_matching(
     parameters = [
         PorvPoroTrans(
             porv_poro_trans_dist_values,
+            regional_porv_poro_trans_dist_values,
             ti2ci,
+            ci2ri,
             network,
             config.flownet.min_permeability,
         ),