def __init__(
self,
input_case: Union[Path, str],
layers: Tuple = (),
perforation_handling_strategy: str = "bottom_point",
):
super().__init__()
self._input_case: Path = Path(input_case)
self._eclsum = EclSum(str(self._input_case))
self._init = EclFile(str(self._input_case.with_suffix(".INIT")))
self._grid = EclGrid(str(self._input_case.with_suffix(".EGRID")))
self._restart = EclFile(str(self._input_case.with_suffix(".UNRST")))
self._init = EclInitFile(self._grid,
str(self._input_case.with_suffix(".INIT")))
self._wells = compdat.df(EclFiles(str(self._input_case)))
self._layers = layers
self._perforation_handling_strategy: str = perforation_handling_strategy
def test_load(self):
g = EclGrid(self.grid_file)
f = EclInitFile(g, self.init_file)
head = f["INTEHEAD"][0]
self.assertTrue(isinstance(head, EclKW))
porv = f["PORV"][0]
self.assertTrue(isinstance(porv, Ecl3DKW))
poro = f["PORO"][0]
self.assertTrue(isinstance(poro, Ecl3DKW))
class FlowData(FromSource):
"""
Flow data source class
Args:
input_case: Full path to eclipse case to load data from
layers: List with definition of isolated layers, if present.
"""
def __init__(
self,
input_case: Union[Path, str],
layers: Tuple = (),
):
super().__init__()
self._input_case: Path = Path(input_case)
self._eclsum = EclSum(str(self._input_case))
self._init = EclFile(str(self._input_case.with_suffix(".INIT")))
self._grid = EclGrid(str(self._input_case.with_suffix(".EGRID")))
self._restart = EclFile(str(self._input_case.with_suffix(".UNRST")))
self._init = EclInitFile(self._grid,
str(self._input_case.with_suffix(".INIT")))
self._wells = compdat.df(EclFiles(str(self._input_case)))
self._layers = layers
# pylint: disable=too-many-branches
def _well_connections(self,
perforation_handling_strategy: str) -> pd.DataFrame:
"""
Function to extract well connection coordinates from a Flow simulation including their
opening and closure time. The output of this function will be filtered based on the
configured perforation strategy.
Args:
perforation_handling_strategy: Strategy to be used when creating perforations.
Valid options are bottom_point, top_point, multiple, time_avg_open_location and
multiple_based_on_workovers.
Returns:
columns: WELL_NAME, X, Y, Z, DATE, OPEN, LAYER_ID
"""
if len(self._layers) > 0 and self._grid.nz is not self._layers[-1][-1]:
raise ValueError(
f"Number of layers from config ({self._layers[-1][-1]}) is not equal to "
f"number of layers from flow simulation ({self._grid.nz}).")
new_items = []
for _, row in self._wells.iterrows():
X, Y, Z = self._grid.get_xyz(ijk=(row["I"] - 1, row["J"] - 1,
row["K1"] - 1))
if len(self._layers) > 0:
for count, (i, j) in enumerate(self._layers):
if row["K1"] in range(i, j + 1):
layer_id = count
break
else:
layer_id = 0
new_row = {
"WELL_NAME": row["WELL"],
"IJK": (
row["I"] - 1,
row["J"] - 1,
row["K1"] - 1,
),
"X": X,
"Y": Y,
"Z": Z,
"DATE": row["DATE"],
"OPEN": bool(row["OP/SH"] == "OPEN"),
"LAYER_ID": layer_id,
}
new_items.append(new_row)
df = pd.DataFrame(
new_items,
columns=[
"WELL_NAME", "IJK", "X", "Y", "Z", "DATE", "OPEN", "LAYER_ID"
],
)
df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d").dt.date
try:
perforation_strategy_method = getattr(
perforation_strategy, perforation_handling_strategy)
except AttributeError as attribute_error:
raise NotImplementedError(
f"The perforation handling strategy {perforation_handling_strategy} is unknown."
) from attribute_error
return perforation_strategy_method(df).sort_values(["DATE"])
def _well_logs(self) -> pd.DataFrame:
"""
Function to extract well log information from a Flow simulation.
Returns:
columns: WELL_NAME, X, Y, Z, PERM (mD), PORO (-)
"""
coords: List = []
for well_name in self._wells["WELL"].unique():
unique_connections = self._wells[self._wells["WELL"] ==
well_name].drop_duplicates(
subset=["I", "J", "K1", "K2"])
for _, connection in unique_connections.iterrows():
ijk = (connection["I"] - 1, connection["J"] - 1,
connection["K1"] - 1)
xyz = self._grid.get_xyz(ijk=ijk)
perm_kw = self._init.iget_named_kw("PERMX", 0)
poro_kw = self._init.iget_named_kw("PORO", 0)
coords.append([
well_name,
*xyz,
perm_kw[self._grid.cell(i=ijk[0], j=ijk[1],
k=ijk[2]).active_index],
poro_kw[self._grid.cell(i=ijk[0], j=ijk[1],
k=ijk[2]).active_index],
])
return pd.DataFrame(
coords, columns=["WELL_NAME", "X", "Y", "Z", "PERM", "PORO"])
def _production_data(self) -> pd.DataFrame:
"""
Function to read production data for all producers and injectors from an
Flow simulation. The simulation is required to write out the
following vectors to the summary file: WOPR, WGPR, WWPR, WBHP, WTHP, WGIR, WWIR
Returns:
A DataFrame with a DateTimeIndex and the following columns:
- date equal to index
- WELL_NAME Well name as used in Flow
- WOPR Well Oil Production Rate
- WGPR Well Gas Production Rate
- WWPR Well Water Production Rate
- WOPT Well Cumulative Oil Production
- WGPT Well Cumulative Gas Production
- WWPT Well Cumulative Water Production
- WBHP Well Bottom Hole Pressure
- WTHP Well Tubing Head Pressure
- WGIR Well Gas Injection Rate
- WWIR Well Water Injection Rate
- WSPR Well Salt Production Rate
- WSIR Well Salt Injection Rate
- WSPT Well Cumulative Salt Production
- WSIT Well Cumulative Salt Injection
- WTICHEA Well Injection Temperature
- WTPCHEA Well Production Temperature
- WSTAT Well status (OPEN, SHUT, STOP)
- TYPE Well Type: "OP", "GP", "WI", "GI"
- PHASE Main producing/injecting phase fluid: "OIL", "GAS", "WATER"
Todo:
* Remove depreciation warning suppression when solved in LibEcl.
* Improve robustness pf setting of Phase and Type.
"""
keys = [
"WOPR",
"WGPR",
"WWPR",
"WOPT",
"WGPT",
"WWPT",
"WBHP",
"WTHP",
"WGIR",
"WWIR",
"WGIT",
"WWIT",
"WSPR",
"WSIR",
"WSPT",
"WSIT",
"WTPCHEA",
"WTICHEA",
"WSTAT",
]
df_production_data = pd.DataFrame()
# Suppress a depreciation warning inside LibEcl
warnings.simplefilter("ignore", category=DeprecationWarning)
with warnings.catch_warnings():
for well_name in self._eclsum.wells():
df = pd.DataFrame()
df["date"] = self._eclsum.report_dates
df["date"] = pd.to_datetime(df["date"])
df.set_index("date", inplace=True)
for prod_key in keys:
try:
df[f"{prod_key}"] = self._eclsum.get_values(
f"{prod_key}:{well_name}", report_only=True)
except KeyError:
df[f"{prod_key}"] = np.nan
# Set columns that have only exact zero values to np.nan
df.loc[:, (df == 0).all(axis=0)] = np.nan
df["WELL_NAME"] = well_name
df["PHASE"] = None
df.loc[df["WOPR"] > 0, "PHASE"] = "OIL"
df.loc[(df["WOPR"] == 0) & (df["WWPR"] > 0), "PHASE"] = "WATER"
df.loc[df["WWIR"] > 0, "PHASE"] = "WATER"
df.loc[df["WGIR"] > 0, "PHASE"] = "GAS"
df["TYPE"] = None
df.loc[df["WOPR"] > 0, "TYPE"] = "OP"
df.loc[(df["WOPR"] == 0) & (df["WWPR"] > 0), "TYPE"] = "WP"
df.loc[df["WWIR"] > 0, "TYPE"] = "WI"
df.loc[df["WGIR"] > 0, "TYPE"] = "GI"
# make sure the correct well type is set also when the well is shut in
df[["PHASE", "TYPE"]] = df[["PHASE",
"TYPE"]].fillna(method="backfill")
df[["PHASE", "TYPE"]] = df[["PHASE",
"TYPE"]].fillna(method="ffill")
df_production_data = df_production_data.append(df)
if df_production_data["WSTAT"].isna().all():
warnings.warn(
"No WSTAT:* summary vectors in input case - setting default well status to OPEN."
)
wstat_default = "OPEN"
else:
wstat_default = "STOP"
df_production_data["WSTAT"] = df_production_data["WSTAT"].map({
0:
wstat_default,
1:
"OPEN", # Producer OPEN
2:
"OPEN", # Injector OPEN
3:
"SHUT",
4:
"STOP",
5:
"SHUT", # PSHUT
6:
"STOP", # PSTOP
np.nan:
wstat_default,
})
# ensure that a type is assigned also if a well is never activated
df_production_data[["PHASE", "TYPE"]] = df_production_data[[
"PHASE", "TYPE"
]].fillna(method="backfill")
df_production_data[["PHASE", "TYPE"
]] = df_production_data[["PHASE", "TYPE"
]].fillna(method="ffill")
df_production_data["date"] = df_production_data.index
df_production_data["date"] = pd.to_datetime(
df_production_data["date"]).dt.date
return df_production_data
def _faults(self) -> pd.DataFrame:
"""
Function to read fault plane data using ecl2df.
Returns:
A dataframe with columns NAME, X, Y, Z with data for fault planes
"""
eclfile = EclFiles(self._input_case)
df_fault_keyword = faults.df(eclfile)
points = []
for _, row in df_fault_keyword.iterrows():
i = row["I"] - 1
j = row["J"] - 1
k = row["K"] - 1
points.append((row["NAME"], i, j, k))
if (row["FACE"] == "X" or row["FACE"] == "X+" or row["FACE"] == "I"
or row["FACE"] == "I+"):
points.append((row["NAME"], i + 1, j, k))
elif (row["FACE"] == "Y" or row["FACE"] == "Y+"
or row["FACE"] == "J" or row["FACE"] == "J+"):
points.append((row["NAME"], i, j + 1, k))
elif (row["FACE"] == "Z" or row["FACE"] == "Z+"
or row["FACE"] == "K" or row["FACE"] == "K+"):
points.append((row["NAME"], i, j, k + 1))
elif row["FACE"] == "X-" or row["FACE"] == "I-":
points.append((row["NAME"], i - 1, j, k))
elif row["FACE"] == "Y-" or row["FACE"] == "J-":
points.append((row["NAME"], i, j - 1, k))
elif row["FACE"] == "Z-" or row["FACE"] == "K-":
points.append((row["NAME"], i, j, k - 1))
else:
raise ValueError(
f"Could not interpret '{row['FACE']}' while reading the FAULTS keyword."
)
df_faults = pd.DataFrame.from_records(points,
columns=["NAME", "I", "J", "K"])
if not df_faults.empty:
df_faults[["X", "Y", "Z"]] = pd.DataFrame(
df_faults.apply(
lambda row: list(
self._grid.get_xyz(ijk=(row["I"], row["J"], row["K"]))
),
axis=1,
).values.tolist())
return df_faults.drop(["I", "J", "K"], axis=1)
def grid_cell_bounding_boxes(self, layer_id: int) -> np.ndarray:
"""
Function to get the bounding box (x, y and z min + max) for all grid cells
Args:
layer_id: The FlowNet layer id to be used to create the bounding box.
Returns:
A (active grid cells x 6) numpy array with columns [ xmin, xmax, ymin, ymax, zmin, zmax ]
filtered on layer_id if not None.
"""
if self._layers:
(k_min,
k_max) = tuple(map(operator.sub, self._layers[layer_id], (1, 1)))
else:
(k_min, k_max) = (0, self._grid.nz)
cells = [
cell for cell in self._grid.cells(active=True)
if (k_min <= cell.k <= k_max)
]
xyz = np.empty((8 * len(cells), 3))
for n_cell, cell in enumerate(cells):
for n_corner, corner in enumerate(cell.corners):
xyz[n_cell * 8 + n_corner, :] = corner
xmin = xyz[:, 0].reshape(-1, 8).min(axis=1)
xmax = xyz[:, 0].reshape(-1, 8).max(axis=1)
ymin = xyz[:, 1].reshape(-1, 8).min(axis=1)
ymax = xyz[:, 1].reshape(-1, 8).max(axis=1)
zmin = xyz[:, 2].reshape(-1, 8).min(axis=1)
zmax = xyz[:, 2].reshape(-1, 8).max(axis=1)
return np.vstack([xmin, xmax, ymin, ymax, zmin, zmax]).T
def _get_start_date(self):
return self._eclsum.start_date
def init(self, name: str) -> np.ndarray:
"""array with 'name' regions"""
return self._init[name][0]
def get_unique_regions(self, name: str) -> np.ndarray:
"""array with unique 'name' regions"""
return np.unique(self._init[name][0])
def get_well_connections(
self, perforation_handling_strategy: str) -> pd.DataFrame:
"""
Function to get dataframe with all well connection coordinates,
filtered based on the perforation_handling_strategy.
Args:
perforation_handling_strategy: Strategy to be used when creating perforations.
Valid options are bottom_point, top_point, multiple,
time_avg_open_location and multiple_based_on_workovers.
Returns:
Dataframe with all well connection coordinates,
filtered based on the perforation_handling_strategy.
Columns: WELL_NAME, X, Y, Z, DATE, OPEN, LAYER_ID
"""
return self._well_connections(
perforation_handling_strategy=perforation_handling_strategy)
def bulk_volume_per_flownet_cell_based_on_voronoi_of_input_model(
self, network: NetworkModel) -> np.ndarray:
"""Generate bulk volume distribution per grid cell in the FlowNet model based on the geometrical
distribution of the volume in the original (full field) simulation model. I.e., the original model's
volume will be distributed over the FlowNet's tubes by assigning original model grid cell
volumes to the nearest FlowNet tube cell midpoint. Finally, the volume distributed to all cells in a tube
will be summed and evenly redistributed over the tube.
Args:
network: FlowNet network instance.
Returns:
An array with volumes per flownetcell.
"""
# pylint: disable=too-many-locals
flownet_cell_midpoints = np.array(network.cell_midpoints).T
model_cell_mid_points = np.array(
[cell.coordinate for cell in self._grid.cells(active=True)])
model_cell_volume = [
(cell.volume *
self._init.iget_named_kw("NTG", 0)[cell.active_index])
for cell in self._grid.cells(active=True)
]
# Number of tubes and tube cells
properties_per_cell = pd.DataFrame(
pd.DataFrame(data=network.grid.index,
index=network.grid.model).index)
number_of_tubes = properties_per_cell.groupby(["model"]).ngroups
cell_volumes = np.zeros(len(properties_per_cell["model"].values))
# Identify the index of the last (inactive) cell of each tube which will have 0 volume
inactive_cells = np.zeros(number_of_tubes)
for i in range(number_of_tubes):
inactive_cells[i] = (properties_per_cell.reset_index().groupby(
["model"]).groups[i][-1])
# depths should be a list of depths provided by the user. it may also be empty
depths = network.volume_layering.copy()
# Add 0 depth level and arrange from deep to shallow)
depths.append(0)
depths = list(set(depths))
depths.sort(reverse=True)
# Perform mapping of volumes between two depth levels
for index, depth in enumerate(depths):
if index == 0:
# Add a very deep dummy level
depth_range = [1.0e10, depth]
else:
depth_range = [depths[index - 1], depth]
tube_cell_volumes = np.zeros(len(flownet_cell_midpoints))
# Identify cells located between the current lower and upper depths levels
flownet_indices = [
idx for idx, val in enumerate(network.cell_midpoints[2])
if (depth_range[0] >= val > depth_range[1])
]
model_indices = [
idx for idx, val in enumerate(model_cell_mid_points[:, 2])
if (depth_range[0] >= val > depth_range[1])
]
# Determine nearest flow tube cell for each cell in the original model
tree = KDTree(flownet_cell_midpoints[flownet_indices, :])
_, matched_indices = tree.query(
model_cell_mid_points[model_indices], k=[1])
# Assign each reservoir model volume to a flow tube
for idx, val in enumerate(matched_indices):
tube_cell_volumes[flownet_indices[
val[0]]] += model_cell_volume[model_indices[idx]]
# Compute the total volumes per tube section between the current depth levels
properties_per_cell["distributed_volume"] = tube_cell_volumes
tube_volumes = properties_per_cell.groupby(by="model").sum().values
# Evenly distribute tube volumes over the tube cells between the current depth levels
for tube in range(number_of_tubes):
indices = [
i for i, x in enumerate(
network.grid.model.iloc[flownet_indices].values.tolist(
))
if x == tube and flownet_indices[i] not in inactive_cells
]
for _, idx in enumerate(indices):
cell_volumes[flownet_indices[
idx]] += tube_volumes[tube] / len(indices)
return cell_volumes
@property
def faults(self) -> pd.DataFrame:
"""dataframe with all fault data"""
return self._faults()
@property
def production(self) -> pd.DataFrame:
"""dataframe with all production data"""
return self._production_data()
@property
def well_logs(self) -> pd.DataFrame:
"""dataframe with all well log"""
return self._well_logs()
@property
def grid(self) -> EclGrid:
"""the simulation grid with properties"""
return self._grid
@property
def layers(self) -> Union[Tuple[Tuple[int, int]], Tuple]:
"""Get the list of top and bottom k-indeces of a the orignal model that represents a FlowNet layer"""
return self._layers
class FlowData(FromSource):
"""
Flow data source class
Args:
input_case: Full path to eclipse case to load data from
layers: List with definition of isolated layers, if present.
perforation_handling_strategy: How to deal with perforations per well.
('bottom_point', 'top_point', 'multiple')
"""
def __init__(
self,
input_case: Union[Path, str],
layers: Tuple = (),
perforation_handling_strategy: str = "bottom_point",
):
super().__init__()
self._input_case: Path = Path(input_case)
self._eclsum = EclSum(str(self._input_case))
self._init = EclFile(str(self._input_case.with_suffix(".INIT")))
self._grid = EclGrid(str(self._input_case.with_suffix(".EGRID")))
self._restart = EclFile(str(self._input_case.with_suffix(".UNRST")))
self._init = EclInitFile(self._grid,
str(self._input_case.with_suffix(".INIT")))
self._wells = compdat.df(EclFiles(str(self._input_case)))
self._layers = layers
self._perforation_handling_strategy: str = perforation_handling_strategy
# pylint: disable=too-many-branches
def _well_connections(self) -> pd.DataFrame:
"""
Function to extract well connection coordinates from a Flow simulation including their
opening and closure time. The output of this function will be filtered based on the
configured perforation strategy.
Returns:
columns: WELL_NAME, X, Y, Z, DATE, OPEN, LAYER_ID
"""
if len(self._layers) > 0 and self._grid.nz is not self._layers[-1][-1]:
raise ValueError(
f"Number of layers from config ({self._layers[-1][-1]}) is not equal to "
f"number of layers from flow simulation ({self._grid.nz}).")
new_items = []
for _, row in self._wells.iterrows():
X, Y, Z = self._grid.get_xyz(ijk=(row["I"] - 1, row["J"] - 1,
row["K1"] - 1))
if len(self._layers) > 0:
for count, (i, j) in enumerate(self._layers):
if row["K1"] in range(i, j + 1):
layer_id = count
break
else:
layer_id = 0
new_row = {
"WELL_NAME": row["WELL"],
"IJK": (
row["I"] - 1,
row["J"] - 1,
row["K1"] - 1,
),
"X": X,
"Y": Y,
"Z": Z,
"DATE": row["DATE"],
"OPEN": bool(row["OP/SH"] == "OPEN"),
"LAYER_ID": layer_id,
}
new_items.append(new_row)
df = pd.DataFrame(
new_items,
columns=[
"WELL_NAME", "IJK", "X", "Y", "Z", "DATE", "OPEN", "LAYER_ID"
],
)
df["DATE"] = pd.to_datetime(df["DATE"], format="%Y-%m-%d").dt.date
try:
perforation_strategy_method = getattr(
perforation_strategy, self._perforation_handling_strategy)
except AttributeError as attribute_error:
raise NotImplementedError(
f"The perforation handling strategy {self._perforation_handling_strategy} is unknown."
) from attribute_error
return perforation_strategy_method(df).sort_values(["DATE"])
def _well_logs(self) -> pd.DataFrame:
"""
Function to extract well log information from a Flow simulation.
Returns:
columns: WELL_NAME, X, Y, Z, PERM (mD), PORO (-)
"""
coords: List = []
for well_name in self._wells["WELL"].unique():
unique_connections = self._wells[self._wells["WELL"] ==
well_name].drop_duplicates(
subset=["I", "J", "K1", "K2"])
for _, connection in unique_connections.iterrows():
ijk = (connection["I"] - 1, connection["J"] - 1,
connection["K1"] - 1)
xyz = self._grid.get_xyz(ijk=ijk)
perm_kw = self._init.iget_named_kw("PERMX", 0)
poro_kw = self._init.iget_named_kw("PORO", 0)
coords.append([
well_name,
*xyz,
perm_kw[self._grid.cell(i=ijk[0], j=ijk[1],
k=ijk[2]).active_index],
poro_kw[self._grid.cell(i=ijk[0], j=ijk[1],
k=ijk[2]).active_index],
])
return pd.DataFrame(
coords, columns=["WELL_NAME", "X", "Y", "Z", "PERM", "PORO"])
def _production_data(self) -> pd.DataFrame:
"""
Function to read production data for all producers and injectors from an
Flow simulation. The simulation is required to write out the
following vectors to the summary file: WOPR, WGPR, WWPR, WBHP, WTHP, WGIR, WWIR
Returns:
A DataFrame with a DateTimeIndex and the following columns:
- date equal to index
- WELL_NAME Well name as used in Flow
- WOPR Well Oil Production Rate
- WGPR Well Gas Production Rate
- WWPR Well Water Production Rate
- WOPT Well Cumulative Oil Production
- WGPT Well Cumulative Gas Production Rate
- WWPT Well Cumulative Water Production Rate
- WBHP Well Bottom Hole Pressure
- WTHP Well Tubing Head Pressure
- WGIR Well Gas Injection Rate
- WWIR Well Water Injection Rate
- WSTAT Well status (OPEN, SHUT, STOP)
- TYPE Well Type: "OP", "GP", "WI", "GI"
- PHASE Main producing/injecting phase fluid: "OIL", "GAS", "WATER"
Todo:
* Remove depreciation warning suppression when solved in LibEcl.
* Improve robustness pf setting of Phase and Type.
"""
keys = [
"WOPR",
"WGPR",
"WWPR",
"WOPT",
"WGPT",
"WWPT",
"WBHP",
"WTHP",
"WGIR",
"WWIR",
"WGIT",
"WWIT",
"WSTAT",
]
df_production_data = pd.DataFrame()
# Suppress a depreciation warning inside LibEcl
warnings.simplefilter("ignore", category=DeprecationWarning)
with warnings.catch_warnings():
for well_name in self._eclsum.wells():
df = pd.DataFrame()
df["date"] = self._eclsum.report_dates
df["date"] = pd.to_datetime(df["date"])
df.set_index("date", inplace=True)
for prod_key in keys:
try:
df[f"{prod_key}"] = self._eclsum.get_values(
f"{prod_key}:{well_name}", report_only=True)
except KeyError:
df[f"{prod_key}"] = np.nan
# Set columns that have only exact zero values to np.nan
df.loc[:, (df == 0).all(axis=0)] = np.nan
df["WELL_NAME"] = well_name
df["PHASE"] = None
df.loc[df["WOPR"] > 0, "PHASE"] = "OIL"
df.loc[df["WWIR"] > 0, "PHASE"] = "WATER"
df.loc[df["WGIR"] > 0, "PHASE"] = "GAS"
df["TYPE"] = None
df.loc[df["WOPR"] > 0, "TYPE"] = "OP"
df.loc[df["WWIR"] > 0, "TYPE"] = "WI"
df.loc[df["WGIR"] > 0, "TYPE"] = "GI"
# make sure the correct well type is set also when the well is shut in
df[["PHASE", "TYPE"]] = df[["PHASE",
"TYPE"]].fillna(method="backfill")
df[["PHASE", "TYPE"]] = df[["PHASE",
"TYPE"]].fillna(method="ffill")
df_production_data = df_production_data.append(df)
if df_production_data["WSTAT"].isna().all():
warnings.warn(
"No WSTAT:* summary vectors in input case - setting default well status to OPEN."
)
wstat_default = "OPEN"
else:
wstat_default = "STOP"
df_production_data["WSTAT"] = df_production_data["WSTAT"].map({
0:
wstat_default,
1:
"OPEN", # Producer OPEN
2:
"OPEN", # Injector OPEN
3:
"SHUT",
4:
"STOP",
5:
"SHUT", # PSHUT
6:
"STOP", # PSTOP
np.nan:
wstat_default,
})
# ensure that a type is assigned also if a well is never activated
df_production_data[["PHASE", "TYPE"]] = df_production_data[[
"PHASE", "TYPE"
]].fillna(method="backfill")
df_production_data[["PHASE", "TYPE"
]] = df_production_data[["PHASE", "TYPE"
]].fillna(method="ffill")
df_production_data["date"] = df_production_data.index
df_production_data["date"] = pd.to_datetime(
df_production_data["date"]).dt.date
return df_production_data
def _faults(self) -> pd.DataFrame:
"""
Function to read fault plane data using ecl2df.
Returns:
A dataframe with columns NAME, X, Y, Z with data for fault planes
"""
eclfile = EclFiles(self._input_case)
df_fault_keyword = faults.df(eclfile)
points = []
for _, row in df_fault_keyword.iterrows():
i = row["I"] - 1
j = row["J"] - 1
k = row["K"] - 1
points.append((row["NAME"], i, j, k))
if row["FACE"] == "X" or row["FACE"] == "X+":
points.append((row["NAME"], i + 1, j, k))
elif row["FACE"] == "Y" or row["FACE"] == "Y+":
points.append((row["NAME"], i, j + 1, k))
elif row["FACE"] == "Z" or row["FACE"] == "Z+":
points.append((row["NAME"], i, j, k + 1))
elif row["FACE"] == "X-":
points.append((row["NAME"], i - 1, j, k))
elif row["FACE"] == "Y-":
points.append((row["NAME"], i, j - 1, k))
elif row["FACE"] == "Z-":
points.append((row["NAME"], i, j, k - 1))
else:
raise ValueError(
f"Could not interpret '{row['FACE']}' while reading the FAULTS keyword."
)
df_faults = pd.DataFrame.from_records(points,
columns=["NAME", "I", "J", "K"])
if not df_faults.empty:
df_faults[["X", "Y", "Z"]] = pd.DataFrame(
df_faults.apply(
lambda row: list(
self._grid.get_xyz(ijk=(row["I"], row["J"], row["K"]))
),
axis=1,
).values.tolist())
return df_faults.drop(["I", "J", "K"], axis=1)
def _grid_cell_bounding_boxes(self,
layer_id: Optional[int] = None
) -> np.ndarray:
"""
Function to get the bounding box (x, y and z min + max) for all grid cells
Args:
layer_id: The FlowNet layer id to be used to create the bounding box.
Returns:
A (active grid cells x 6) numpy array with columns [ xmin, xmax, ymin, ymax, zmin, zmax ]
filtered on layer_id if not None.
"""
if layer_id is not None:
(k_min,
k_max) = tuple(map(operator.sub, self._layers[layer_id], (1, 1)))
else:
(k_min, k_max) = (0, self._grid.nz)
cells = [
cell for cell in self._grid.cells(active=True)
if (k_min <= cell.k <= k_max)
]
xyz = np.empty((8 * len(cells), 3))
for n_cell, cell in enumerate(cells):
for n_corner, corner in enumerate(cell.corners):
xyz[n_cell * 8 + n_corner, :] = corner
xmin = xyz[:, 0].reshape(-1, 8).min(axis=1)
xmax = xyz[:, 0].reshape(-1, 8).max(axis=1)
ymin = xyz[:, 1].reshape(-1, 8).min(axis=1)
ymax = xyz[:, 1].reshape(-1, 8).max(axis=1)
zmin = xyz[:, 2].reshape(-1, 8).min(axis=1)
zmax = xyz[:, 2].reshape(-1, 8).max(axis=1)
return np.vstack([xmin, xmax, ymin, ymax, zmin, zmax]).T
def _get_start_date(self):
return self._eclsum.start_date
def init(self, name: str) -> np.ndarray:
"""array with 'name' regions"""
return self._init[name][0]
def get_unique_regions(self, name: str) -> np.ndarray:
"""array with unique 'name' regions"""
return np.unique(self._init[name][0])
@property
def grid_cell_bounding_boxes(self) -> np.ndarray:
"""Boundingboxes for all gridcells"""
return self._grid_cell_bounding_boxes()
@property
def faults(self) -> pd.DataFrame:
"""dataframe with all fault data"""
return self._faults()
@property
def production(self) -> pd.DataFrame:
"""dataframe with all production data"""
return self._production_data()
@property
def well_connections(self) -> pd.DataFrame:
"""dataframe with all well connection coordinates"""
return self._well_connections()
@property
def well_logs(self) -> pd.DataFrame:
"""dataframe with all well log"""
return self._well_logs()
@property
def grid(self) -> EclGrid:
"""the simulation grid with properties"""
return self._grid
@property
def layers(self) -> Union[Tuple[Tuple[int, int]], Tuple]:
"""Get the list of top and bottom k-indeces of a the orignal model that represents a FlowNet layer"""
return self._layers
def test_wrong_type(self):
g = EclGrid(self.grid_file)
with self.assertRaises(ValueError):
f = EclInitFile(g, self.grid_file)