def test_arg_loader(self):
with self.assertRaises(IOError):
arg = ArgLoader.load("arg1X")
arg_file = self.createTestPath("Statoil/config/with_GEN_DATA_RFT/wellpath/WI_1.txt")
with self.assertRaises(ValueError):
arg = ArgLoader.load(arg_file , column_names = ["Col1" , "Col2"])
arg = ArgLoader.load(arg_file , column_names = ["utm_x" , "utm_y" , "md" , "tvd"])
self.assertFloatEqual( arg["utm_x"][0] , 461317.620646)
def test_arg_loader(self):
with self.assertRaises(IOError):
arg = ArgLoader.load("arg1X")
arg_file = self.createTestPath(
"Statoil/config/with_GEN_DATA_RFT/wellpath/WI_1.txt")
with self.assertRaises(ValueError):
arg = ArgLoader.load(
arg_file,
column_names=["Col1", "Col2", "Col3", "COl5", "Col6"])
arg = ArgLoader.load(arg_file,
column_names=["utm_x", "utm_y", "md", "tvd"])
self.assertFloatEqual(arg["utm_x"][0], 461317.620646)
def run(self, output_file, trajectory_path , case_list=None, infer_iteration=True):
"""The run method will export the RFT's for all wells and all cases.
The successfull operation of this method hinges on two naming
conventions:
1. All the GEN_DATA RFT observations have key RFT_$WELL
2. The trajectory files are in $trajectory_path/$WELL.txt
"""
wells = set()
obs_pattern = "RFT_*"
enkf_obs = self.ert().getObservations()
obs_keys = enkf_obs.getMatchingKeys(obs_pattern , obs_type = EnkfObservationImplementationType.GEN_OBS)
cases = []
if case_list is not None:
cases = case_list.split(",")
if case_list is None or len(cases) == 0:
cases = [self.ert().getEnkfFsManager().getCurrentFileSystem().getCaseName()]
data_frame = pandas.DataFrame()
for index, case in enumerate(cases):
case = case.strip()
case_frame = pandas.DataFrame()
if not self.ert().getEnkfFsManager().caseExists(case):
raise UserWarning("The case '%s' does not exist!" % case)
if not self.ert().getEnkfFsManager().caseHasData(case):
raise UserWarning("The case '%s' does not have any data!" % case)
if infer_iteration:
iteration_number = self.inferIterationNumber(case)
else:
iteration_number = index
for obs_key in obs_keys:
well = obs_key.replace("RFT_","")
wells.add( well )
obs_vector = enkf_obs[obs_key]
data_key = obs_vector.getDataKey()
report_step = obs_vector.activeStep()
obs_node = obs_vector.getNode( report_step )
rft_data = GenDataCollector.loadGenData( self.ert() , case , data_key , report_step )
fs = self.ert().getEnkfFsManager().getFileSystem( case )
realizations = fs.realizationList( RealizationStateEnum.STATE_HAS_DATA )
# Trajectory
trajectory_file = os.path.join( trajectory_path , "%s.txt" % well)
trajectory = WellTrajectory( trajectory_file )
arg = ArgLoader.load( trajectory_file , column_names = ["utm_x" , "utm_y" , "md" , "tvd"])
tvd_arg = arg["tvd"]
data_size = len(tvd_arg)
# Observations
obs = numpy.empty(shape = (data_size , 2 ) , dtype=numpy.float64)
obs.fill( numpy.nan )
for (value,std,data_index) in obs_node:
obs[data_index,0] = value
obs[data_index,1] = std
real_data = pandas.DataFrame( index = ["Realization","Well"])
for iens in realizations:
realization_frame = pandas.DataFrame( data = {"TVD" : tvd_arg ,
"Pressure" : rft_data[iens],
"ObsValue" : obs[:,0],
"ObsStd" : obs[:,1]},
columns = ["TVD" , "Pressure" , "ObsValue" , "ObsStd"])
realization_frame["Realization"] = iens
realization_frame["Well"] = well
realization_frame["Case"] = case
realization_frame["Iteration"] = iteration_number
case_frame = pandas.concat( [case_frame , realization_frame] )
data_frame = pandas.concat([data_frame, case_frame])
data_frame.set_index(["Realization" , "Well" , "Case" , "Iteration"] , inplace = True)
data_frame.to_csv(output_file)
export_info = "Exported RFT information for wells: %s to: %s " % (", ".join(list(wells)) , output_file)
return export_info
obs_vector = enkf_obs[obs_key]
data_key = obs_vector.getDataKey()
report_step = obs_vector.activeStep()
obs_node = obs_vector.getNode(report_step)
rft_data = GenDataCollector.loadGenData(self.ert(), case, data_key, report_step)
fs = self.ert().getEnkfFsManager().getFileSystem(case)
realizations = fs.realizationList(RealizationStateEnum.STATE_HAS_DATA)
# Trajectory
trajectory_file = os.path.join(trajectory_path, "%s.txt" % well)
if not os.path.isfile(trajectory_file):
trajectory_file = os.path.join(trajectory_path, "%s_R.txt" % well)
trajectory = WellTrajectory(trajectory_file)
arg = ArgLoader.load(trajectory_file, column_names=["utm_x", "utm_y", "md", "tvd"])
tvd_arg = arg["tvd"]
data_size = len(tvd_arg)
# Observations
obs = numpy.empty(shape=(data_size, 2), dtype=numpy.float64)
obs.fill(numpy.nan)
for obs_index in range(len(obs_node)):
data_index = obs_node.getDataIndex(obs_index)
value = obs_node.getValue(obs_index)
std = obs_node.getStandardDeviation(obs_index)
obs[data_index, 0] = value
obs[data_index, 1] = std
for iens in realizations:
realization_frame = pandas.DataFrame(data={"TVD": tvd_arg,
