Test field with FORWARD_INIT:False

test-data/ert/heat_equation/config_forward_init_false.ert

@@ -0,0 +1,26 @@
+-- By default, NumPy utilizes multiple threads, which is beneficial for parallelizable computations.
+-- However, the heat equation implementation in this case does not benefit from parallel execution within a single realization.
+-- When ERT runs multiple realizations, each one by default uses multiple threads for NumPy operations,
+-- leading to resource contention and slower overall execution.
+-- Setting these thread counts to 1 ensures each realization uses minimal resources,
+-- allowing more realizations to run concurrently and significantly speeding up the entire experiment.
+SETENV  MKL_NUM_THREADS 1
+SETENV  NUMEXPR_NUM_THREADS 1
+SETENV  OMP_NUM_THREADS 1
+
+QUEUE_SYSTEM LOCAL
+QUEUE_OPTION LOCAL MAX_RUNNING 10
+
+RANDOM_SEED 11223344
+
+NUM_REALIZATIONS 10
+GRID CASE.EGRID
+
+OBS_CONFIG observations
+
+FIELD COND PARAMETER cond.bgrdecl INIT_FILES:cond_%d.bgrdecl FORWARD_INIT:False
+
+GEN_DATA MY_RESPONSE RESULT_FILE:gen_data_%d.out REPORT_STEPS:10,71,132,193,255,316,377,438 INPUT_FORMAT:ASCII
+
+INSTALL_JOB heat_equation HEAT_EQUATION
+SIMULATION_JOB heat_equation <IENS> <ITER>

test-data/ert/heat_equation/generate_files.py

@@ -8,6 +8,7 @@
 import numpy as np
 import numpy.typing as npt
 import pandas as pd
+import resfo
 import xtgeo
 from definition import Coordinate, obs_coordinates, obs_times
 from heat_equation import heat_equation, sample_prior_conductivity
@@ -71,6 +72,24 @@ def make_observations(
     return d
 
 
+def generate_priors():
+    """Generates and saves 10 random conductivity field realizations.
+
+    Uses a prior sampling function to create conductivity fields,
+    then saves each field to a separate .bgrdecl file in ECLIPSE format
+    using Fortran-style ordering. Used for testing when FORWARD_INIT
+    is disabled.
+    """
+
+    rng = np.random.default_rng()
+    for i in range(10):
+        cond = sample_prior_conductivity(ensemble_size=1, nx=nx, rng=rng)
+        resfo.write(
+            f"cond_{i}.bgrdecl",
+            [("COND    ", cond.flatten(order="F").astype(np.float32))],
+        )
+
+
 if __name__ == "__main__":
     create_egrid_file()
 
@@ -122,3 +141,5 @@ def make_observations(
         with open(f"obs_{obs_time}.txt", "w", encoding="utf-8") as fobs:
             df = d.iloc[d.index.get_level_values("k") == obs_time]
             fobs.write(df.sort_index().to_csv(header=False, index=False, sep=" "))
+
+    generate_priors()

tests/ert/conftest.py

@@ -202,6 +202,13 @@ def copy_snake_oil_case(copy_case):
         fh.write("QUEUE_OPTION LOCAL MAX_RUNNING 12\n")
 
 
+@pytest.fixture()
+def copy_heat_equation(copy_case):
+    copy_case("heat_equation")
+    with open("config.ert", "a", encoding="utf-8") as fh:
+        fh.write("QUEUE_OPTION LOCAL MAX_RUNNING 12\n")
+
+
 @pytest.fixture(
     name="copy_snake_oil_case_storage",
     params=[

tests/ert/ui_tests/cli/test_field_parameter.py

@@ -7,6 +7,7 @@
 import numpy as np
 import numpy.testing
 import polars as pl
+import pytest
 import resfo
 import xtgeo
 
@@ -332,3 +333,42 @@ def test_field_param_update_using_heat_equation_zero_var_params_and_adaptive_loc
         )
         # Check that generalized variance is reduced by update step.
         assert np.trace(prior_covariance) > np.trace(posterior_covariance)
+
+
+@pytest.mark.usefixtures("copy_heat_equation")
+def test_foward_init_false():
+    config = ErtConfig.from_file("config_forward_init_false.ert")
+    run_cli(
+        ENSEMBLE_SMOOTHER_MODE,
+        "--disable-monitor",
+        "config_forward_init_false.ert",
+        "--experiment-name",
+        "es-test",
+    )
+
+    with open_storage(config.ens_path) as storage:
+        experiment = storage.get_experiment_by_name("es-test")
+        prior = experiment.get_ensemble_by_name("iter-0")
+        posterior = experiment.get_ensemble_by_name("iter-1")
+
+        param_config = config.ensemble_config.parameter_configs["COND"]
+
+        prior_result = prior.load_parameters("COND")["values"]
+        prior_covariance = np.cov(
+            prior_result.values.reshape(
+                prior.ensemble_size, param_config.nx * param_config.ny * param_config.nz
+            ),
+            rowvar=False,
+        )
+
+        posterior_result = posterior.load_parameters("COND")["values"]
+        posterior_covariance = np.cov(
+            posterior_result.values.reshape(
+                posterior.ensemble_size,
+                param_config.nx * param_config.ny * param_config.nz,
+            ),
+            rowvar=False,
+        )
+
+        # Check that generalized variance is reduced by update step.
+        assert np.trace(prior_covariance) > np.trace(posterior_covariance)