def ImportAriesByScenario(scenarioName,
start_date,
end_date,
Area,
CorpID=['ALL']):
from Model import BPXDatabase as bpxdb
from Model import QueryFile as qf
import pandas as pd
#Create ODS and EDW objects
Success = True
Messages = []
combined_df = pd.DataFrame()
try:
ODSobj = bpxdb.GetDBEnvironment('ProdODS', 'OVERRIDE')
EDWobj = bpxdb.GetDBEnvironment('ProdEDW', 'OVERRIDE')
scenario_query = qf.ScenarioQuery(scenarioName, CorpID, start_date,
end_date)
results = ODSobj.Query(scenario_query)
#Extract APINumbers from the results and concatenate into an 'IN' sql clause
corpid_list = []
for result in results[0]:
if not result['CorpID'] in corpid_list:
corpid_list.append(result['CorpID'])
if None in corpid_list:
corpid_list.remove(None)
key_query = qf.EDWKeyQueryFromCorpID(corpid_list, Area)
key_results = EDWobj.Query(key_query)
#Join the key_results to the Aries results
combined_df = pd.merge(results[1],
key_results[1],
on='CorpID',
how='right')
except Exception as ex:
Messages.append('Error on Import from Aries. ' + str(ex))
Success = False
return combined_df, Success, Messages
def GetWellandCorpID(WellName, CorpID):
from Model import QueryFile as qf
from Model import BPXDatabase as bpx
#Check CorpID if Wellname is passed
if not CorpID and WellName:
CorpID_query = qf.EDWKeyQueryFromWellName([WellName])
EDWObj = bpx.GetDBEnvironment('ProdEDW', 'OVERRIDE')
res, res_df = EDWObj.Query(CorpID_query)
if not res_df.empty:
CorpID = res_df['CorpID'].values[0]
#Check WellName if CorpID not passed
if not WellName and CorpID:
WellName_Query = qf.EDWKeyQueryFromCorpID([CorpID], '')
EDWObj = bpx.GetDBEnvironment('ProdEDW', 'OVERRIDE')
res, res_df = EDWObj.Query(WellName_Query)
if not res_df.empty:
WellName = res_df['WellName'].values[0]
return WellName, CorpID
def WriteLEFromTemplate(all_data_df,
InterpolationMethod,
LEName,
LE_Date,
Update_User,
IDCol='WellName'):
from datetime import datetime, date
import pandas as pd
from Model import QueryFile as qf
from Model import BPXDatabase as bpx
from Model import ImportUtility as i
Success = True
Messages = []
results = []
try:
#Data Frame must be the same structure as the output from the 'Read From Excel Function
#'CorpID', 'WellName', 'Wedge', 'Date', 'Gas', 'Oil', 'Water', 'OilNF', 'GasNF'
wellname = ''
if not Success:
Messages.append(Message)
if IDCol == 'CorpID':
corpid_list = list(all_data_df['CorpID'].unique())
corpid_query = qf.EDWKeyQueryFromCorpID(corpid_list)
corpid_results, corpid_df = bpx.GetDBEnvironment(
'ProdEDW', 'OVERRIDE').Query(corpid_query)
well_list = list(corpid_df['WellName'].unique())
well_query = 'CorpID == @corpid'
else:
well_list = list(all_data_df['WellName'].unique())
well_query = 'WellName == @wellname'
well_list = [i for i in well_list if i]
for wellname in well_list:
wellname, corpid = i.GetWellandCorpID(wellname, '')
if not corpid:
corpid = wellname
data_df = all_data_df.query(well_query)
row_count = 1
if not data_df.empty:
df_previous_row = (0, data_df.iloc[1])
for idx, df_row in data_df.iterrows():
if InterpolationMethod == 'MonthlyRates':
if row_count == 1:
df_next_row = data_df.iloc[row_count]
results = InterpolateDailyRatesFromMonthlyRates(
CurrentMonthVal=df_row,
NextMonthVal=df_next_row,
GasProduction='Gas',
OilProduction='Oil')
elif row_count != data_df.shape[0] and row_count != 1:
df_next_row = data_df.iloc[row_count]
results = InterpolateDailyRatesFromMonthlyRates(
CurrentMonthVal=df_row,
NextMonthVal=df_next_row,
PreviousMonthVal=df_previous_row,
GasProduction='Gas',
OilProduction='Oil')
elif row_count == data_df.shape[0]:
results = InterpolateDailyRatesFromMonthlyRates(
CurrentMonthVal=df_row,
PreviousMonthVal=df_previous_row,
GasProduction='Gas',
OilProduction='Oil')
elif InterpolationMethod == 'MonthlyVolume':
if row_count == 1:
df_next_row = data_df.iloc[row_count]
results = InterpolateDailyRatesFromMonthlyVolumes(
CurrentMonthVal=df_row[1],
NextMonthVal=df_next_row)
else:
results = InterpolateDailyRatesFromMonthlyVolumes(
CurrentMonthVal=df_row[1],
PreviousMonthVal=df_previous_row[1])
elif InterpolationMethod == 'None':
results = ConvertNonInterpolatedResults(df_row)
Wedge, Message = i.GetWedgeData(corpid, True)
Success, Message = WriteInterpolatedLEToDB(
LEName, wellname, corpid, '', Wedge, LE_Date,
Update_User, results)
if not Success:
Messages.append(Message)
df_previous_row = df_row
row_count = row_count + 1
except Exception as ex:
Success = False
Messages.append('Error during the writing of the LE from template. ' +
str(ex))
return Success, Messages
