def GetLEProduction(LEName, Wedge, WellorArea, Phase = 'Gas'):
from datetime import datetime
from Model import ModelLayer as m
from Controller import SummaryModule as s
import pandas as pd
#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
WellList = s.GetFullWellList(WellorArea)
LERowObj = m.LEData('', [LEName], WellList, [])
LEData, Success, Message = LERowObj.ReadTable()
if not Success:
Messages.append(Message)
elif len(LEData) > 0:
LEData = pd.DataFrame([vars(s) for s in LEData])
if Wedge:
prod_LE_rows = LEData.query('Wedge == @Wedge')
else:
prod_LE_rows = LEData
dates = prod_LE_rows['Date_Key'].unique()
prod_array = []
date_array = []
if not prod_LE_rows.empty:
if Phase == 'Gas':
for date in dates:
results = prod_LE_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_LE_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_LE_rows.query('Date_Key == @date')
prod_array.append(results['Oil_Production'].sum())
date_array.append(date)
ProductionDataObj = ProductionData(date_array, prod_array, Phase,'bbl')
else:
Messages.append('No Production data available with this criteria. ')
except Exception as ex:
Success = False
Messages.append('Error during collection of production data. ' + str(ex))
return ProductionDataObj, Success, Messages
def FracHatMitigation(LEName, EastWestFracHitRadius, NorthSouthFracHitRadius,
Update_User):
from Model import ModelLayer as m
from Model import QueryFile as qf
from Model import BPXDatabase as bpx
import pandas as pd
from datetime import datetime, timedelta
import numpy as np
Success = True
Messages = []
try:
#Find Frac Date of Upcoming Wells
#Get the beginning and end date of the LE being evaluated
LEDataObj = m.LEData('', [LEName], [], [])
LErows, Success, Message = LEDataObj.ReadTable()
if not Success:
Messages.append(Message)
elif len(LErows) < 1:
Messages.append('No LE Data exists for the given LE Name.')
else:
LErows_df = pd.DataFrame([vars(s) for s in LErows])
start_date = LErows_df['Date_Key'].min()
end_date = LErows_df['Date_Key'].max()
s_start_date = datetime.strftime(start_date, '%m/%d/%Y')
s_end_date = datetime.strftime(end_date, '%m/%d/%Y')
#Query the Drill Schedule (and potentially other data sources) for upcoming drills
new_drill_query = qf.GetActenumDrillScheduleData(
s_start_date, s_end_date)
DBObj = bpx.GetDBEnvironment('ProdEDH', 'OVERRIDE')
dso_results = DBObj.Query(new_drill_query)
if not Success:
Messages.append(Message)
else:
dso_df = dso_results[1]
for nd_idx, nd_row in dso_df.iterrows():
#Get Lateral and Longitude values
surface_lat = nd_row['SurfaceLatitude']
surface_long = nd_row['SurfaceLongitude']
if surface_long > 0:
surface_long = 0 - surface_long
bh_lat = nd_row['BottomHoleLatitude']
bh_long = nd_row['BottomHoleLongitude']
if bh_long > 0:
bh_long = 0 - bh_long
stages = nd_row['ExpectedStages']
name = nd_row['WellName']
frac_start = nd_row['StartFracDate']
frac_end = nd_row['EndFracDate']
#Get wells within certain distance
FracHitRadius = max(EastWestFracHitRadius,
NorthSouthFracHitRadius)
from_surface_query = qf.GetWellsWithinBearing(
surface_lat, surface_long, FracHitRadius)
from_bottom_query = qf.GetWellsWithinBearing(
bh_lat, bh_long, FracHitRadius)
surface_res = DBObj.Query(from_surface_query)
bh_res = DBObj.Query(from_bottom_query)
if not surface_res[1].empty:
all_res = surface_res[1]
if not bh_res[1].empty:
all_res = pd.merge(surface_res[1], bh_res[1])
elif not bh_res[1].empty:
all_res = bh_res[1]
else:
all_res = pd.DataFrame()
stages = int(stages)
#Get interpolated bearings
interpolated_bearings = InterpolateBearing(
[bh_lat, bh_long], [surface_lat, surface_long], stages
) #reversed since frac will begin at bottom hole location
interpolated_dates = InterpolateDates(
frac_start, frac_end, stages)
interp = np.column_stack(
[interpolated_bearings, interpolated_dates])
if all_res.empty:
Messages.append(
'No wells within given frac hit radius from ' +
name + ' to apply multipliers.')
#Loop through all the well results
for ex_idx, ex_row in all_res.iterrows():
corpid = ex_row['UWI']
wellname = ex_row['WELL_NAME']
dates = []
ex_surface_lat = float(ex_row['SurfaceLatitude'])
ex_surface_long = float(ex_row['SurfaceLongitude'])
if ex_surface_long > 0:
ex_surface_long = 0 - ex_surface_long
ex_bh_lat = float(ex_row['BottomHoleLatitude'])
ex_bh_long = float(ex_row['BottomHoleLongitude'])
if ex_bh_long > 0:
ex_bh_long = 0 - ex_bh_long
ex_interp_bearings = InterpolateBearing(
[ex_surface_lat, ex_surface_long],
[ex_bh_lat, ex_bh_long], stages)
# cycle through each item in the interpolated array and calculate distance to points in existing well
for point in interp:
for bearing in ex_interp_bearings:
distance = CalculateDistanceFromBearings(
[point[0], point[1]], bearing)
azimuth = CalculateAzimuthFromBearings(
[point[0], point[1]], bearing)
if (azimuth >= 25 and azimuth <= 155) or (
azimuth >= 205 and azimuth <= 335):
#Evaluate against EastWestFrac radius
if distance < EastWestFracHitRadius:
dates.append(point[2])
else:
if distance < NorthSouthFracHitRadius:
dates.append(point[2])
dates = list(dict.fromkeys(dates))
#Go through the dates and add the ramp up, ramp up schedule dates and multipliers
if len(dates) > 0:
ramp_up = [1, 1, 0.5]
ramp_down = [0.5, 0.75, 1]
min_date = min(dates)
max_date = max(dates)
ramp_down_dates = [(min_date - timedelta(days=3)),
(min_date - timedelta(days=2)),
(min_date - timedelta(days=1))]
ramp_up_dates = [(max_date + timedelta(days=3)),
(max_date + timedelta(days=2)),
(max_date + timedelta(days=1))]
shut_ins = [0] * len(dates)
all_multipliers = []
all_multipliers.extend(ramp_up)
all_multipliers.extend(shut_ins)
all_multipliers.extend(ramp_down)
all_dates = []
all_dates.extend(ramp_up_dates)
all_dates.extend(dates)
all_dates.extend(ramp_down_dates)
combined_date_multiplier = np.column_stack(
[all_dates, all_multipliers])
for date_multiplier in combined_date_multiplier:
#Search DB for frac hit multiplier row
date = datetime.strftime(
date_multiplier[0], '%m/%d/%Y')
fhm_obj = m.FracHitMultipliers(
'', [LEName], [corpid], [date])
fhm_rows, Success, Message = fhm_obj.ReadTable(
)
if Success and len(fhm_rows) > 0:
#if the row exists, then update
row = fhm_rows[0]
row.Multiplier = date_multiplier[1]
Success, Message = row.Update(
Update_User, datetime.now())
if not Success:
Messages.extend(Message)
elif Success and len(fhm_rows) == 0:
Messages.append('No entry exists for ' +
wellname + ' on ' + date +
' for LE ' + LEName + '. ')
else:
Messages.extend(Message)
except Exception as ex:
Success = False
Messages.append(
'Error during the automatic application of frac hit mitigation multipliers. '
+ str(ex))
return Success, Messages
def CalculateSummaryInfo(LEName, ForecastName, SummaryName, SummaryDate,
Update_User):
from Model import ModelLayer as m
from Model import BPXDatabase as bpx
from Model import ImportUtility as i
import numpy as np
import pandas as pd
from datetime import datetime
from datetime import timedelta
import calendar
from Controller import DataInterface as di
Success = True
Messages = []
try:
#Get netting factor for each well
#Calculate the Monthly AveragMBOED
print('Gathering wedge information...')
LEHeaderObj = m.LEHeader('', [], [], [LEName], [])
Rows, Success, Message = LEHeaderObj.ReadTable()
LEDataRowObj = m.LEData('', [LEName], [], [])
DataRows, Success, Message = LEDataRowObj.ReadTable()
if not Success or len(DataRows) == 0:
if not Message:
Message = 'No LE found in the database.'
Messages.append(Message)
else:
well_list = []
for row in Rows:
well_list.append(row.WellName)
NettingObj = m.GasNetting('', well_list, [])
NettingRows, Success, Message = NettingObj.ReadTable()
if isinstance(SummaryDate, str):
today = pd.to_datetime(SummaryDate)
elif isinstance(SummaryDate, datetime):
today = SummaryDate
else:
today = datetime.today()
first_of_month = datetime(today.year, today.month, 1)
first_of_year = datetime(today.year, 1, 1)
quarter_start = pd.to_datetime(today -
pd.tseries.offsets.QuarterBegin(
startingMonth=1)).date()
quarter_end = pd.to_datetime(today + pd.tseries.offsets.QuarterEnd(
startingMonth=3)).date()
next_quarter_start = quarter_end + timedelta(days=1)
month_end = pd.to_datetime(today +
pd.tseries.offsets.MonthEnd(1)).date()
tomorrow = today + timedelta(days=1)
year_end = datetime(today.year, 12, 31)
Rows = pd.DataFrame([vars(s) for s in Rows])
DataRows = pd.DataFrame([vars(s) for s in DataRows])
#This will be the end of the LE forecast plus one day.
next_day_date = max(DataRows['Date_Key']) + timedelta(days=1)
le_start_date = min(DataRows['Date_Key'])
le_end_date = max(DataRows['Date_Key'])
#Create a dataframe that calculates total and also line item by wedge
#loop through each wedge
wedge_list = Rows['Wedge'].unique()
wedge_totals = []
for wedge in wedge_list: #Eventually, may need to add oil and water in this as other phases are implemented
wedge_total = {}
wedge_rows = Rows.query('Wedge == @wedge')
#Gather well list for this particular wedge and obtain:
# 1.) Actuals up to current day
# 2.) LE Forecast values to end of month
# - if LE Forecast does not extend to end of month, send warning message and get GFO values to end of year
# 3.) GFO Forecast values from first of next month to year end.
# 4.) Average all of the values and multiply by Netting Value and Frac Hit Multipliers for that forecast.
# - If no frac hit multipliers exist, default to 1. (send message)
# - If no Netting Values exist, default to 1. (send message)
print('Importing wedge actuals...')
corpIDs = wedge_rows['CorpID'].unique()
corpIDs = list(corpIDs)
all_corpids = i.GetFullWellList(corpIDs)
ytd_actuals_df, Success, Messages = i.ImportActuals(
all_corpids, first_of_year, today, LEName)
first_of_month_date_only = datetime.date(first_of_month)
today_date_only = datetime.date(today)
mtd_actuals_df = ytd_actuals_df.query(
'Date_Key >= @first_of_month_date_only and Date_Key < @today_date_only'
)
qtd_actuals_df = ytd_actuals_df.query(
'Date_Key >= @quarter_start and Date_Key < @today_date_only'
)
now = datetime.now()
days_in_month = calendar.monthrange(now.year, now.month)[1]
days_in_quarter = (quarter_end - quarter_start).days
days_in_year = 365
print('Gathering wedge GFO data for \'' + wedge + '\' ...')
gfo_annually_wedge_df, Success, Message = GetGFOValues(
ForecastName, corpIDs, first_of_year, year_end)
if not Success:
Messages.append(Message)
else:
gfo_monthly_wedge_df = gfo_annually_wedge_df.query(
'Date_Key >= @first_of_month and Date_Key <= @month_end'
)
gfo_quarterly_wedge_df = gfo_annually_wedge_df.query(
'Date_Key >= @quarter_start and Date_Key <= @quarter_end'
)
if Success:
#Generate Daily Netting Values based on table entries for each CorpID
GasNettingObj = m.GasNetting('', [], all_corpids)
GasNettingValues, Success, Message = GasNettingObj.ReadTable(
)
if len(GasNettingValues) > 0:
GasNettingValues = pd.DataFrame(
[vars(s) for s in GasNettingValues])
Gas_Daily_Nf = GenerateDailyNF(GasNettingValues,
first_of_year, year_end)
Gas_Daily_Nf.rename(columns={'NettingValue': 'GasNF'},
inplace=True)
else:
Gas_Daily_Nf = pd.DataFrame(
columns=['GasNF', 'Date_Key', 'CorpID'])
OilNettingObj = m.OilNetting('', [], all_corpids)
OilNettingValues, Success, Message = OilNettingObj.ReadTable(
)
if len(OilNettingValues) > 0:
OilNettingValues = pd.DataFrame(
[vars(s) for s in OilNettingValues])
Oil_Daily_Nf = GenerateDailyNF(OilNettingValues,
first_of_year, year_end)
Oil_Daily_Nf.rename(columns={'NettingValue': 'OilNF'},
inplace=True)
else:
Oil_Daily_Nf = pd.DataFrame(
columns=['OilNF', 'Date_Key', 'CorpID'])
#Merge the NF tables
daily_Nf = pd.merge(Gas_Daily_Nf,
Oil_Daily_Nf,
left_on=['Date_Key', 'CorpID'],
right_on=['Date_Key', 'CorpID'],
how='outer')
daily_Nf.fillna(0, inplace=True)
summary = []
if not Success:
Messages.append(Message)
else:
MultipliersObj = m.FracHitMultipliers(
'', [LEName], [], [])
MultiplierRows, Success, Message = MultipliersObj.ReadTable(
)
if MultiplierRows:
MultiplierRows = pd.DataFrame(
[vars(s) for s in MultiplierRows])
print('Calculating wedge summary information for \'' +
wedge + '\' ...')
count = 1
for corpID in corpIDs:
row = {}
well_df = Rows.query('CorpID == @corpID')
row['WellName'] = well_df['WellName'].iloc[0]
row['CorpID'] = corpID
row['Wedge'] = well_df['Wedge'].iloc[0]
area_corpids = i.GetFullWellList([corpID])
#Monthly data
#loop through actuals
well_mtd_actual_df = GetActualsFromWellorArea(
mtd_actuals_df, area_corpids)
well_ytd_actual_df = GetActualsFromWellorArea(
ytd_actuals_df, area_corpids)
well_qtd_actual_df = GetActualsFromWellorArea(
qtd_actuals_df, area_corpids)
#Get Netting Factors and Multipliers from Frac Hit Mitigation
well_multipliers_df = pd.DataFrame()
annual_default_multipliers_df = GenerateDefaultMultipliers(
1, first_of_year, year_end)
if not MultiplierRows.empty:
well_multipliers_df = MultiplierRows.query(
'CorpID == @corpID')
else:
Messages.append(
'No multiplier values found, using default value of 1.'
)
well_multipliers_df = annual_default_multipliers_df
well_mtd_actual_sum = SumDailyValues(
well_mtd_actual_df, daily_Nf,
annual_default_multipliers_df, 'MeasuredGas',
'MeasuredOil')
well_ytd_actual_sum = SumDailyValues(
well_ytd_actual_df, daily_Nf,
annual_default_multipliers_df, 'MeasuredGas',
'MeasuredOil')
well_qtd_actual_sum = SumDailyValues(
well_qtd_actual_df, daily_Nf,
annual_default_multipliers_df, 'MeasuredGas',
'MeasuredOil')
well_gfo_monthly_df = gfo_monthly_wedge_df.query(
'CorpID == @corpID')
well_gfo_quarterly_df = gfo_quarterly_wedge_df.query(
'CorpID == @corpID')
well_gfo_annually_df = gfo_annually_wedge_df.query(
'CorpID == @corpID')
well_forecast_name = well_df[
'ForecastGeneratedFrom'].iloc[0]
gfo_after_le_to_yearend_df, Success, Message = GetGFOValues(
well_forecast_name, corpIDs, next_day_date,
year_end)
gfo_after_le_to_month_df = gfo_after_le_to_yearend_df.query(
'Date_Key >= @next_day_date and Date_Key <= @month_end'
)
gfo_after_le_to_quarter_df = gfo_after_le_to_yearend_df.query(
'Date_Key >= @next_day_date and Date_Key <= @quarter_end'
)
well_gfo_after_le_month_df = gfo_after_le_to_month_df.query(
'CorpID == @corpID')
well_gfo_after_le_quarter_df = gfo_after_le_to_quarter_df.query(
'CorpID == @corpID')
well_gfo_after_le_annual_df = gfo_after_le_to_yearend_df.query(
'CorpID == @corpID')
#Get GFO Sums
well_gfo_monthly_sum_midmonth = GetMidMonthGFOValue(
well_gfo_monthly_df, 'Gas_Production',
'Oil_Production')
well_gfo_quarterly_sum_midmonth = GetMidMonthGFOValue(
well_gfo_quarterly_df, 'Gas_Production',
'Oil_Production')
well_gfo_annually_sum_midmonth = GetMidMonthGFOValue(
well_gfo_annually_df, 'Gas_Production',
'Oil_Production')
#Convert NF to method expected dataframe from forecast table
forecast_NF_dict = {}
# forecast_NF_dict['GasNF'] = well_gfo_after_le_annual_df['GasNF'].values
# forecast_NF_dict['OilNF'] = well_gfo_after_le_annual_df['OilNF'].values
forecast_NF_dict[
'Date_Key'] = well_gfo_after_le_annual_df[
'Date_Key'].values
forecast_NF_dict['CorpID'] = [
corpID
] * well_gfo_after_le_annual_df.shape[0]
forecast_NF_df = pd.DataFrame(forecast_NF_dict)
well_gfo_after_le_month_sum = SumDailyValues(
well_gfo_after_le_month_df, forecast_NF_df,
annual_default_multipliers_df,
'Gas_Production', 'Oil_Production')
well_gfo_after_le_quarter_sum = SumDailyValues(
well_gfo_after_le_quarter_df, forecast_NF_df,
annual_default_multipliers_df,
'Gas_Production', 'Oil_Production')
well_gfo_after_le_annual_sum = SumDailyValues(
well_gfo_after_le_annual_df, forecast_NF_df,
annual_default_multipliers_df,
'Gas_Production', 'Oil_Production')
well_le_df_all = DataRows.query(
'CorpID == @corpID')
well_le_df_month = well_le_df_all.query(
'Date_Key >= @today_date_only and Date_Key <= @month_end'
)
well_le_df_total = well_le_df_all.query(
'Date_Key > @month_end')
well_le_sum_total = SumDailyValues(
well_le_df_total, daily_Nf,
well_multipliers_df, 'Gas_Production',
'Oil_Production')
well_le_sum_month = SumDailyValues(
well_le_df_month, daily_Nf,
well_multipliers_df, 'Gas_Production',
'Oil_Production')
row['LE_Monthly'] = (well_mtd_actual_sum +
well_le_sum_month +
well_gfo_after_le_month_sum)
row['GFOzMonthly'] = well_gfo_monthly_sum_midmonth
row['LE_Quarterly'] = (
well_qtd_actual_sum + well_le_sum_month +
well_le_sum_total +
well_gfo_after_le_quarter_sum)
row['GFOzQuarterly'] = (
well_gfo_quarterly_sum_midmonth)
row['LE_Annually'] = (well_ytd_actual_sum +
well_le_sum_month +
well_le_sum_total +
well_gfo_after_le_annual_sum)
row['GFOzAnnually'] = (
well_gfo_annually_sum_midmonth)
summary.append(row)
di.callprogressbar(count, len(corpIDs))
count = count + 1
print('Summarizing wedge data...')
if summary:
summary_df = pd.DataFrame(summary)
length = summary_df.shape[0]
wedge_total['wedge_name'] = wedge
wedge_total['LE_Monthly'] = summary_df[
'LE_Monthly'].sum() / days_in_month / 1000
wedge_total['GFOzMonthly'] = summary_df[
'GFOzMonthly'].sum() / days_in_month / 1000
wedge_total['MonthlyVariance'] = wedge_total[
'LE_Monthly'] - wedge_total['GFOzMonthly']
wedge_total['LE_Quarterly'] = summary_df[
'LE_Quarterly'].sum() / days_in_quarter / 1000
wedge_total['GFOzQuarterly'] = summary_df[
'GFOzQuarterly'].sum() / days_in_quarter / 1000
wedge_total['QuarterlyVariance'] = wedge_total[
'LE_Quarterly'] - wedge_total['GFOzQuarterly']
wedge_total['LE_Annually'] = summary_df[
'LE_Annually'].sum() / days_in_year / 1000
wedge_total['GFOzAnnually'] = summary_df[
'GFOzAnnually'].sum() / days_in_year / 1000
wedge_total['AnnualVariance'] = wedge_total[
'LE_Annually'] - wedge_total['GFOzAnnually']
wedge_totals.append(wedge_total)
print('Writing to database...')
for row in wedge_totals:
#Create a Summary entry
WedgeName = row['wedge_name']
Midstream = ''
Reason = ''
Comments = ''
LEName = LEName
GFOForecastName = ForecastName
MonthlyAvgMBOED = row['LE_Monthly']
QuarterlyAvgMBOED = row['LE_Quarterly']
AnnualAvgMBOED = row['LE_Annually']
MonthlyGFOMBOED = row['GFOzMonthly']
QuarterlyGFOMBOED = row['GFOzQuarterly']
AnnualGFOMBOED = row['GFOzAnnually']
MonthlyVariance = row['MonthlyVariance']
QuarterlyVariance = row['QuarterlyVariance']
AnnualVariance = row['AnnualVariance']
SummaryObj = m.LESummaryRow(
SummaryName, WedgeName, Midstream, Reason, Comments,
SummaryDate, LEName, GFOForecastName, MonthlyAvgMBOED,
QuarterlyAvgMBOED, AnnualAvgMBOED, MonthlyGFOMBOED,
QuarterlyGFOMBOED, AnnualGFOMBOED, MonthlyVariance,
QuarterlyVariance, AnnualVariance, '')
Write_Success, Message = SummaryObj.Write(
Update_User, datetime.now())
if not Write_Success:
Messages.append(Message)
except Exception as ex:
Sucess = False
Messages.append('Error during calculation of summary information. ' +
str(ex))
return Success, Messages