def GetGFOValues(ForecastName, WellList, StartDate, EndDate):
from Model import ModelLayer as m
import pandas as pd
from datetime import datetime
from datetime import date
Success = True
Messages = []
GFO_df = []
try:
#NEW Column in Header: Netting Factor. Query For this value and append to the returned GFO_df
ForecastObj = m.ForecastData('', [ForecastName], WellList, [])
ForecastHdrObj = m.ForecastHeader('', [], WellList, [ForecastName])
Rows, Success, Message = ForecastObj.ReadTable()
HdrRows, Success, Message = ForecastHdrObj.ReadTable()
if not Success:
Messages.append(Message)
else:
GFO_df = pd.DataFrame([vars(s) for s in Rows])
GFO_hdr_df = pd.DataFrame([vars(s) for s in HdrRows])
if isinstance(StartDate, date):
StartDate = datetime.combine(StartDate, datetime.min.time())
if isinstance(EndDate, date):
EndDate = datetime.combine(EndDate, datetime.min.time())
GFO_df = GFO_df.query(
'Date_Key >= @StartDate and Date_Key <= @EndDate')
pd.merge(GFO_df, GFO_hdr_df, on='CorpID')
except Exception as ex:
Success = False
Messages.append('Error attempting to obtain Forecast Values. ' +
str(ex))
return GFO_df, Success, Messages
def GetForecastProduction(ForecastName, Wedge, WellorArea, Phase = 'Gas'):
from datetime import datetime
from Model import ModelLayer as m
import pandas as pd
from Model import BPXDatabase as bpx
from Model import QueryFile as qf
#Get the production values from the LE table by the input criteria
Success = True
Messages = []
ProductionDataObj = []
try:
#Get all CorpIDs from Well or Area passed
well_list = []
corpid_list = []
if WellorArea:
well_list.append(WellorArea)
EDWobj = bpx.GetDBEnvironment('ProdEDW', 'OVERRIDE')
corpid_query = qf.EDWKeyQueryFromWellName(well_list)
corpid_list = EDWobj.Query(corpid_query)
corpid_list = list(corpid_list[1]['CorpID'])
if Wedge:
wedge_list, Success, Message = GetWellorAreaByWedge(Wedge)
if not Success:
Messages.append(Message)
else:
well_list.extend(wedge_list)
ForecastDataRowObj = m.ForecastData('', [ForecastName], corpid_list, [])
ForecastData, Success, Message = ForecastDataRowObj.ReadTable()
if not Success:
Messages.append(Message)
else:
prod_Forecast_rows = pd.DataFrame([vars(s) for s in ForecastData])
dates = prod_Forecast_rows['Date_Key'].unique()
prod_array = []
date_array = []
if not prod_Forecast_rows.empty:
if Phase == 'Gas':
for date in dates:
results = prod_Forecast_rows.query('Date_Key == @date')
prod_array.append(results['Gas_Production'].sum())
date_array.append(date)
ProductionDataObj = ProductionData(date_array, prod_array, Phase,'scf')
elif Phase == 'Water':
for date in dates:
results = prod_Forecast_rows.query('Date_Key == @date')
prod_array.append(results['Water_Production'].sum())
date_array.append(date)
ProductionDataObj = ProductionData(date_array, prod_array, Phase,'bbl')
elif Phase == 'Oil':
for date in dates:
results = prod_Forecast_rows.query('Date_Key == @date')
prod_array.append(results['Oil_Production'].sum())
date_array.append(date)
ProductionDataObj = ProductionData(date_array, prod_array, Phase,'bbl')
except Exception as ex:
Success = False
Messages.append('Error during collection of production data. ' + str(ex))
return ProductionDataObj, Success, Messages
