Forecast Scheduling - Wells and WellsGroup Classes¶
To take advantage of the scheduling capabilities of covering Wells, Scenarios and peroduction periods, two additional classes must be introduced. Like Scenario class they are simple wrappers that allows to group multiple scenarios instances on a Well instances and multiple Wells into a WellsGroup Instance.
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import os
from dcapy import dca
from dcapy.schedule import Scenario, Period, Well, WellsGroup
from dcapy.cashflow import CashFlowParams
import numpy as np
import pandas as pd
from datetime import date, timedelta
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import stats
import seaborn as sns
import os
from dcapy import dca
from dcapy.schedule import Scenario, Period, Well, WellsGroup
from dcapy.cashflow import CashFlowParams
import numpy as np
import pandas as pd
from datetime import date, timedelta
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import stats
import seaborn as sns
Create two scenarios by changing simple parameters that can be denominated as decision variables
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#First Period First Scenario
p1a_dict = {
'name':'pdp',
'dca': {
'ti':'2021-01-01',
'di':0.025,
'freq_di':'M',
'qi':1500,
'b':0,
},
'start':'2021-01-01',
'end':'2040-01-01',
'freq_output':'A',
'rate_limit': 300,
'cashflow_params':[
{
'name':'capex',
'value':{
'date':['2021-01-01'],
'value':[-5000000]
},
'target':'capex'
}
]
}
#Second Period First Scenario
p2a_dict = {
'name':'pud',
'dca': {
'ti':'2022-01-01',
'di':0.3,
'freq_di':'A',
'qi':3000,
'b':0,
},
'start':'2022-01-01',
'end':'2040-01-01',
'freq_output':'A',
'rate_limit': 100,
'depends':{'period':'pdp'},
'cashflow_params':[
{
'name':'wo',
'value':-500000,
'periods':1,
'target':'capex'
},
{
'name':'abandon',
'value':-300000,
'periods':-1,
'target':'capex'
},
]
}
s1_dict = {
'name':'first',
'periods':[
p1a_dict,
p2a_dict
],
}
s1 = Scenario(**s1_dict)
#First Period First Scenario
p1a_dict = {
'name':'pdp',
'dca': {
'ti':'2021-01-01',
'di':0.025,
'freq_di':'M',
'qi':1500,
'b':0,
},
'start':'2021-01-01',
'end':'2040-01-01',
'freq_output':'A',
'rate_limit': 300,
'cashflow_params':[
{
'name':'capex',
'value':{
'date':['2021-01-01'],
'value':[-5000000]
},
'target':'capex'
}
]
}
#Second Period First Scenario
p2a_dict = {
'name':'pud',
'dca': {
'ti':'2022-01-01',
'di':0.3,
'freq_di':'A',
'qi':3000,
'b':0,
},
'start':'2022-01-01',
'end':'2040-01-01',
'freq_output':'A',
'rate_limit': 100,
'depends':{'period':'pdp'},
'cashflow_params':[
{
'name':'wo',
'value':-500000,
'periods':1,
'target':'capex'
},
{
'name':'abandon',
'value':-300000,
'periods':-1,
'target':'capex'
},
]
}
s1_dict = {
'name':'first',
'periods':[
p1a_dict,
p2a_dict
],
}
s1 = Scenario(**s1_dict)
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#First Period Second Scenario
p1b_dict = {
'name':'pdp',
'dca': {
'ti':'2021-01-01',
'di':0.025,
'freq_di':'M',
'qi':1500,
'b':0,
},
'start':'2021-01-01',
'end':'2040-01-01',
'freq_output':'A',
'rate_limit': 700,
'cashflow_params':[
{
'name':'capex',
'value':{
'date':['2021-01-01'],
'value':[-6500000]
},
'target':'capex'
}
]
}
#Second Period Second Escenario
p2b_dict = {
'name':'pud',
'dca': {
'ti':'2022-01-01',
'di':0.3,
'freq_di':'A',
'qi':3000,
'b':0,
},
'start':'2022-01-01',
'end':'2040-01-01',
'freq_output':'A',
'rate_limit': 100,
'depends':{'period':'pdp'},
'cashflow_params':[
{
'name':'wo',
'value':-50000,
'periods':1,
'target':'capex'
},
{
'name':'abandon',
'value':-300000,
'periods':-1,
'target':'capex'
},
]
}
s2_dict = {
'name':'second',
'periods':[
p1b_dict,
p2b_dict
],
}
s2 = Scenario(**s2_dict)
#First Period Second Scenario
p1b_dict = {
'name':'pdp',
'dca': {
'ti':'2021-01-01',
'di':0.025,
'freq_di':'M',
'qi':1500,
'b':0,
},
'start':'2021-01-01',
'end':'2040-01-01',
'freq_output':'A',
'rate_limit': 700,
'cashflow_params':[
{
'name':'capex',
'value':{
'date':['2021-01-01'],
'value':[-6500000]
},
'target':'capex'
}
]
}
#Second Period Second Escenario
p2b_dict = {
'name':'pud',
'dca': {
'ti':'2022-01-01',
'di':0.3,
'freq_di':'A',
'qi':3000,
'b':0,
},
'start':'2022-01-01',
'end':'2040-01-01',
'freq_output':'A',
'rate_limit': 100,
'depends':{'period':'pdp'},
'cashflow_params':[
{
'name':'wo',
'value':-50000,
'periods':1,
'target':'capex'
},
{
'name':'abandon',
'value':-300000,
'periods':-1,
'target':'capex'
},
]
}
s2_dict = {
'name':'second',
'periods':[
p1b_dict,
p2b_dict
],
}
s2 = Scenario(**s2_dict)
There have been created two scenarios with more than one period where the dependency option have been set.
There are capex and rate limit differences between the scenarios.
As seen, there are different ways of creating the instances due to the versatility that Pydantic gives to not only to create but validate them. In this case, the cashflow parameters that applies for all the scenarios can be set once when creating the Well instance and passing a list CashflowParams directly.
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well_1 = Well(
name = 'well_1',
scenarios = [s1,s2],
cashflow_params = [
CashFlowParams(
name = 'fix_opex',
value = -5000, # 5 KUSD per well per month
freq_value = 'M',
target = 'opex',
),
CashFlowParams(
name = 'var_opex',
value = -10, # 10 USD per barrel of oil
multiply = 'oil_volume',
target = 'opex',
),
CashFlowParams(
name = 'Sells',
value = 50, # 50 USD per barrel of oil
multiply = 'oil_volume',
target = 'income',
wi = 0.94,
)
]
)
print(type(well_1))
well_1 = Well(
name = 'well_1',
scenarios = [s1,s2],
cashflow_params = [
CashFlowParams(
name = 'fix_opex',
value = -5000, # 5 KUSD per well per month
freq_value = 'M',
target = 'opex',
),
CashFlowParams(
name = 'var_opex',
value = -10, # 10 USD per barrel of oil
multiply = 'oil_volume',
target = 'opex',
),
CashFlowParams(
name = 'Sells',
value = 50, # 50 USD per barrel of oil
multiply = 'oil_volume',
target = 'income',
wi = 0.94,
)
]
)
print(type(well_1))
<class 'dcapy.schedule.schedule.Well'>
Generate Forecast and plot it!
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well1_forecast = well_1.generate_forecast(freq_output='A')
#Plot results by scenario
sns.lineplot(data=well1_forecast, x=well1_forecast.index.to_timestamp(), y='oil_rate', hue='scenario')
well1_forecast = well_1.generate_forecast(freq_output='A')
#Plot results by scenario
sns.lineplot(data=well1_forecast, x=well1_forecast.index.to_timestamp(), y='oil_rate', hue='scenario')
/home/scuervo/Documents/dev/apps/dcapy/dcapy/dca/arps.py:245: RuntimeWarning: invalid value encountered in true_divide (np.power(qi / rate, b) - 1)/(b * di) /home/scuervo/Documents/dev/apps/dcapy/dcapy/dca/arps.py:68: RuntimeWarning: divide by zero encountered in true_divide return qi/np.power(1+b*di*time_array,1/b) /home/scuervo/Documents/dev/apps/dcapy/dcapy/dca/arps.py:85: RuntimeWarning: divide by zero encountered in true_divide g = np.power(b*di*time_array+1,(b-1)/b) /home/scuervo/Documents/dev/apps/dcapy/dcapy/dca/arps.py:86: RuntimeWarning: divide by zero encountered in true_divide h = np.power(b*di*ti+1,(b-1)/b) /home/scuervo/Documents/dev/apps/dcapy/dcapy/dca/arps.py:245: RuntimeWarning: invalid value encountered in true_divide (np.power(qi / rate, b) - 1)/(b * di) /home/scuervo/Documents/dev/apps/dcapy/dcapy/dca/arps.py:68: RuntimeWarning: divide by zero encountered in true_divide return qi/np.power(1+b*di*time_array,1/b) /home/scuervo/Documents/dev/apps/dcapy/dcapy/dca/arps.py:85: RuntimeWarning: divide by zero encountered in true_divide g = np.power(b*di*time_array+1,(b-1)/b) /home/scuervo/Documents/dev/apps/dcapy/dcapy/dca/arps.py:86: RuntimeWarning: divide by zero encountered in true_divide h = np.power(b*di*ti+1,(b-1)/b) /home/scuervo/Documents/dev/apps/dcapy/dcapy/dca/arps.py:245: RuntimeWarning: invalid value encountered in true_divide (np.power(qi / rate, b) - 1)/(b * di) /home/scuervo/Documents/dev/apps/dcapy/dcapy/dca/arps.py:68: RuntimeWarning: divide by zero encountered in true_divide return qi/np.power(1+b*di*time_array,1/b) /home/scuervo/Documents/dev/apps/dcapy/dcapy/dca/arps.py:85: RuntimeWarning: divide by zero encountered in true_divide g = np.power(b*di*time_array+1,(b-1)/b) /home/scuervo/Documents/dev/apps/dcapy/dcapy/dca/arps.py:86: RuntimeWarning: divide by zero encountered in true_divide h = np.power(b*di*ti+1,(b-1)/b) /home/scuervo/Documents/dev/apps/dcapy/dcapy/dca/arps.py:245: RuntimeWarning: invalid value encountered in true_divide (np.power(qi / rate, b) - 1)/(b * di) /home/scuervo/Documents/dev/apps/dcapy/dcapy/dca/arps.py:68: RuntimeWarning: divide by zero encountered in true_divide return qi/np.power(1+b*di*time_array,1/b) /home/scuervo/Documents/dev/apps/dcapy/dcapy/dca/arps.py:85: RuntimeWarning: divide by zero encountered in true_divide g = np.power(b*di*time_array+1,(b-1)/b) /home/scuervo/Documents/dev/apps/dcapy/dcapy/dca/arps.py:86: RuntimeWarning: divide by zero encountered in true_divide h = np.power(b*di*ti+1,(b-1)/b)
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<AxesSubplot:xlabel='date', ylabel='oil_rate'>
Generate Cashflows
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well1_cashflow = well_1.generate_cashflow(freq_output='A')
print(len(well1_cashflow))
well1_cashflow = well_1.generate_cashflow(freq_output='A')
print(len(well1_cashflow))
2
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print(well1_cashflow[0].fcf())
print(well1_cashflow[0].fcf())
Sells_well_1-first-pdp Sells_well_1-first-pud total_income \
2021 2.218737e+07 0.000000e+00 2.218737e+07
2022 1.927792e+07 0.000000e+00 1.927792e+07
2023 1.422205e+07 0.000000e+00 1.422205e+07
2024 1.050258e+07 0.000000e+00 1.050258e+07
2025 7.748154e+06 0.000000e+00 7.748154e+06
2026 6.566778e+06 2.223132e+07 2.879810e+07
2027 0.000000e+00 3.870068e+07 3.870068e+07
2028 0.000000e+00 2.869882e+07 2.869882e+07
2029 0.000000e+00 2.126061e+07 2.126061e+07
2030 0.000000e+00 1.572160e+07 1.572160e+07
2031 0.000000e+00 1.164684e+07 1.164684e+07
2032 0.000000e+00 8.636817e+06 8.636817e+06
2033 0.000000e+00 6.398312e+06 6.398312e+06
2034 0.000000e+00 4.731363e+06 4.731363e+06
2035 0.000000e+00 3.505080e+06 3.505080e+06
2036 0.000000e+00 2.599222e+06 2.599222e+06
2037 0.000000e+00 2.215218e+06 2.215218e+06
fix_opex_well_1-first-pdp var_opex_well_1-first-pdp \
2021 -60000.0 -4.720718e+06
2022 -60000.0 -4.101685e+06
2023 -60000.0 -3.025969e+06
2024 -60000.0 -2.234592e+06
2025 -60000.0 -1.648543e+06
2026 -60000.0 -1.397187e+06
2027 0.0 0.000000e+00
2028 0.0 0.000000e+00
2029 0.0 0.000000e+00
2030 0.0 0.000000e+00
2031 0.0 0.000000e+00
2032 0.0 0.000000e+00
2033 0.0 0.000000e+00
2034 0.0 0.000000e+00
2035 0.0 0.000000e+00
2036 0.0 0.000000e+00
2037 0.0 0.000000e+00
fix_opex_well_1-first-pud var_opex_well_1-first-pud total_opex \
2021 0.0 0.000000e+00 -4.780718e+06
2022 0.0 0.000000e+00 -4.161685e+06
2023 0.0 0.000000e+00 -3.085969e+06
2024 0.0 0.000000e+00 -2.294592e+06
2025 0.0 0.000000e+00 -1.708543e+06
2026 -60000.0 -4.730067e+06 -6.247254e+06
2027 -60000.0 -8.234188e+06 -8.294188e+06
2028 -60000.0 -6.106132e+06 -6.166132e+06
2029 -60000.0 -4.523534e+06 -4.583534e+06
2030 -60000.0 -3.345020e+06 -3.405020e+06
2031 -60000.0 -2.478052e+06 -2.538052e+06
2032 -60000.0 -1.837621e+06 -1.897621e+06
2033 -60000.0 -1.361343e+06 -1.421343e+06
2034 -60000.0 -1.006673e+06 -1.066673e+06
2035 -60000.0 -7.457617e+05 -8.057617e+05
2036 -60000.0 -5.530260e+05 -6.130260e+05
2037 -60000.0 -4.713230e+05 -5.313230e+05
capex_well_1-first-pdp wo_well_1-first-pud abandon_well_1-first-pud \
2021 -5000000.0 0.0 0.0
2022 0.0 0.0 0.0
2023 0.0 0.0 0.0
2024 0.0 0.0 0.0
2025 0.0 0.0 0.0
2026 0.0 -500000.0 0.0
2027 0.0 0.0 0.0
2028 0.0 0.0 0.0
2029 0.0 0.0 0.0
2030 0.0 0.0 0.0
2031 0.0 0.0 0.0
2032 0.0 0.0 0.0
2033 0.0 0.0 0.0
2034 0.0 0.0 0.0
2035 0.0 0.0 0.0
2036 0.0 0.0 0.0
2037 0.0 0.0 -300000.0
total_capex fcf cum_fcf
2021 -5000000.0 1.240666e+07 1.240666e+07
2022 0.0 1.511623e+07 2.752289e+07
2023 0.0 1.113608e+07 3.865897e+07
2024 0.0 8.207992e+06 4.686697e+07
2025 0.0 6.039611e+06 5.290658e+07
2026 -500000.0 2.205084e+07 7.495742e+07
2027 0.0 3.040649e+07 1.053639e+08
2028 0.0 2.253269e+07 1.278966e+08
2029 0.0 1.667708e+07 1.445737e+08
2030 0.0 1.231658e+07 1.568903e+08
2031 0.0 9.108793e+06 1.659990e+08
2032 0.0 6.739197e+06 1.727382e+08
2033 0.0 4.976969e+06 1.777152e+08
2034 0.0 3.664690e+06 1.813799e+08
2035 0.0 2.699318e+06 1.840792e+08
2036 0.0 1.986196e+06 1.860654e+08
2037 -300000.0 1.383895e+06 1.874493e+08
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fig, ax = plt.subplots(2,1, figsize=(10,15))
well1_cashflow[0].plot(ax=ax[0])
well1_cashflow[1].plot(ax=ax[1])
ax[0].set_title('First Scenario')
ax[0].set_title('Second Scenario')
fig, ax = plt.subplots(2,1, figsize=(10,15))
well1_cashflow[0].plot(ax=ax[0])
well1_cashflow[1].plot(ax=ax[1])
ax[0].set_title('First Scenario')
ax[0].set_title('Second Scenario')
/home/scuervo/Documents/dev/apps/dcapy/dcapy/cashflow/cashflow.py:351: UserWarning: FixedFormatter should only be used together with FixedLocator grax.set_yticklabels([fmt.format(i/format_dict[format]['factor']) for i in ticks]) /home/scuervo/Documents/dev/apps/dcapy/dcapy/cashflow/cashflow.py:351: UserWarning: FixedFormatter should only be used together with FixedLocator grax.set_yticklabels([fmt.format(i/format_dict[format]['factor']) for i in ticks])
Out[8]:
Text(0.5, 1.0, 'Second Scenario')
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well_1.npv([0.1], freq_rate='A', freq_cashflow='A')
well_1.npv([0.1], freq_rate='A', freq_cashflow='A')
Out[9]:
| npv | iteration | |
|---|---|---|
| 0.1 | 1.110744e+08 | 0 |
| 0.1 | 1.184897e+08 | 1 |
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well_1.npv([0.1], freq_rate='A', freq_cashflow='A').reset_index().to_dict()
well_1.npv([0.1], freq_rate='A', freq_cashflow='A').reset_index().to_dict()
Out[10]:
{'index': {0: 0.10000000000000009, 1: 0.10000000000000009},
'npv': {0: 111074433.96223314, 1: 118489715.93003416},
'iteration': {0: 0, 1: 1}}
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from dcapy.auth import Credential
from dcapy.auth import Credential
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cred = Credential(token='eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpZCI6ImIyZDQ5NjMyLWM0MzEtNDAzYi04OTEyLTJiZGIyOTA3NTMxNCIsIm5hbWUiOiJTYW50aWFnbyIsImxhc3RfbmFtZSI6IkN1ZXJ2byIsInVzZXJuYW1lIjoic2N1ZXJ2bzkxIiwiZXhwIjoxNjI2OTI2NTk3fQ.n3HuheJvoQKF9RNKTC9gEstC449EWd2qsrWR7f30V2U')
cred = Credential(token='eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpZCI6ImIyZDQ5NjMyLWM0MzEtNDAzYi04OTEyLTJiZGIyOTA3NTMxNCIsIm5hbWUiOiJTYW50aWFnbyIsImxhc3RfbmFtZSI6IkN1ZXJ2byIsInVzZXJuYW1lIjoic2N1ZXJ2bzkxIiwiZXhwIjoxNjI2OTI2NTk3fQ.n3HuheJvoQKF9RNKTC9gEstC449EWd2qsrWR7f30V2U')
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well_2 = Well(
name = 'well_1',
scenarios = [s1,s2],
cashflow_params = [
CashFlowParams(
name = 'fix_opex',
value = -5000, # 5 KUSD per well per month
freq_value = 'M',
target = 'opex',
),
CashFlowParams(
name = 'var_opex',
value = -10, # 10 USD per barrel of oil
multiply = 'oil_volume',
target = 'opex',
),
CashFlowParams(
name = 'Sells',
value = 50, # 50 USD per barrel of oil
multiply = 'oil_volume',
target = 'income',
wi = 0.94,
)
]
)
well_2.insert_db(cred, 'well-Cash tutorial1')
well_2 = Well(
name = 'well_1',
scenarios = [s1,s2],
cashflow_params = [
CashFlowParams(
name = 'fix_opex',
value = -5000, # 5 KUSD per well per month
freq_value = 'M',
target = 'opex',
),
CashFlowParams(
name = 'var_opex',
value = -10, # 10 USD per barrel of oil
multiply = 'oil_volume',
target = 'opex',
),
CashFlowParams(
name = 'Sells',
value = 50, # 50 USD per barrel of oil
multiply = 'oil_volume',
target = 'income',
wi = 0.94,
)
]
)
well_2.insert_db(cred, 'well-Cash tutorial1')
Out[13]:
'1b1ecf58-e252-4ea0-9ac3-66bee10cc050'
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_ = well_2.generate_forecast()
_ = well_2.generate_cashflow()
_ = well_2.generate_forecast()
_ = well_2.generate_cashflow()
/home/scuervo/Documents/dev/apps/dcapy/dcapy/dca/arps.py:245: RuntimeWarning: invalid value encountered in true_divide (np.power(qi / rate, b) - 1)/(b * di) /home/scuervo/Documents/dev/apps/dcapy/dcapy/dca/arps.py:68: RuntimeWarning: divide by zero encountered in true_divide return qi/np.power(1+b*di*time_array,1/b) /home/scuervo/Documents/dev/apps/dcapy/dcapy/dca/arps.py:85: RuntimeWarning: divide by zero encountered in true_divide g = np.power(b*di*time_array+1,(b-1)/b) /home/scuervo/Documents/dev/apps/dcapy/dcapy/dca/arps.py:86: RuntimeWarning: divide by zero encountered in true_divide h = np.power(b*di*ti+1,(b-1)/b) /home/scuervo/Documents/dev/apps/dcapy/dcapy/dca/arps.py:245: RuntimeWarning: invalid value encountered in true_divide (np.power(qi / rate, b) - 1)/(b * di) /home/scuervo/Documents/dev/apps/dcapy/dcapy/dca/arps.py:68: RuntimeWarning: divide by zero encountered in true_divide return qi/np.power(1+b*di*time_array,1/b) /home/scuervo/Documents/dev/apps/dcapy/dcapy/dca/arps.py:85: RuntimeWarning: divide by zero encountered in true_divide g = np.power(b*di*time_array+1,(b-1)/b) /home/scuervo/Documents/dev/apps/dcapy/dcapy/dca/arps.py:86: RuntimeWarning: divide by zero encountered in true_divide h = np.power(b*di*ti+1,(b-1)/b) /home/scuervo/Documents/dev/apps/dcapy/dcapy/dca/arps.py:245: RuntimeWarning: invalid value encountered in true_divide (np.power(qi / rate, b) - 1)/(b * di) /home/scuervo/Documents/dev/apps/dcapy/dcapy/dca/arps.py:68: RuntimeWarning: divide by zero encountered in true_divide return qi/np.power(1+b*di*time_array,1/b) /home/scuervo/Documents/dev/apps/dcapy/dcapy/dca/arps.py:85: RuntimeWarning: divide by zero encountered in true_divide g = np.power(b*di*time_array+1,(b-1)/b) /home/scuervo/Documents/dev/apps/dcapy/dcapy/dca/arps.py:86: RuntimeWarning: divide by zero encountered in true_divide h = np.power(b*di*ti+1,(b-1)/b) /home/scuervo/Documents/dev/apps/dcapy/dcapy/dca/arps.py:245: RuntimeWarning: invalid value encountered in true_divide (np.power(qi / rate, b) - 1)/(b * di) /home/scuervo/Documents/dev/apps/dcapy/dcapy/dca/arps.py:68: RuntimeWarning: divide by zero encountered in true_divide return qi/np.power(1+b*di*time_array,1/b) /home/scuervo/Documents/dev/apps/dcapy/dcapy/dca/arps.py:85: RuntimeWarning: divide by zero encountered in true_divide g = np.power(b*di*time_array+1,(b-1)/b) /home/scuervo/Documents/dev/apps/dcapy/dcapy/dca/arps.py:86: RuntimeWarning: divide by zero encountered in true_divide h = np.power(b*di*ti+1,(b-1)/b)
In [15]:
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well_2.update_db(cred, description='well-Cash tutorial_update1')
well_2.update_db(cred, description='well-Cash tutorial_update1')
Out[15]:
'1b1ecf58-e252-4ea0-9ac3-66bee10cc050'
In [16]:
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wd = Well()
wd.get_db('1b1ecf58-e252-4ea0-9ac3-66bee10cc050',cred)
wd = Well()
wd.get_db('1b1ecf58-e252-4ea0-9ac3-66bee10cc050',cred)
In [18]:
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type(wd)
type(wd)
Out[18]:
dcapy.schedule.schedule.Well
In [ ]:
Copied!