Dcapy - Arps Class¶
This section introduces the Arps class which is a 'wrapper' for the Arps Function seen in the previous section. It add certain functionalities to the forecast estimation, like dates, plots, cumulatives, water calculation.
By taking advantage of python Object-Oriented functionalities it is very convinient to define a class with the required properties to make an Arps declination analysis. With the class are defined methods that help to make the forecast in a very flexible way. That means you can make different kind of forecast from the same Arps declination parameters.
In [13]:
Copied!
import os
from dcapy import dca
import numpy as np
import pandas as pd
from datetime import date
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import stats
np.seterr(divide='ignore')
import os
from dcapy import dca
import numpy as np
import pandas as pd
from datetime import date
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import stats
np.seterr(divide='ignore')
Out[13]:
{'divide': 'ignore', 'over': 'warn', 'under': 'ignore', 'invalid': 'warn'}
Arps Class¶
As seen in the previous section to define an Arps declination object you must have a Decline rate di, b coefficient b, Initial Time Ti, Initial rate qi. With these properties you can create a simple Arps Class. The time array to make a forecast can vary depending on the horizon time, frequency or rates limits. In that way you can estimate multiple forecast from the same class depending on the needs.
Let's define a simple Aprs class by providing the same properties we have been seen.
We can add a property we had not seen so far which is useful when we incorporates different time units. The units of the declination rate di. So far we can handle three periods of time. Days, Months and years.
In [14]:
Copied!
# Define a Simple Arps Class
a1 = dca.Arps(
ti = 0,
di = 0.03,
qi = 1500,
b = 0,
freq_di='M'
)
print(a1)
# Define a Simple Arps Class
a1 = dca.Arps(
ti = 0,
di = 0.03,
qi = 1500,
b = 0,
freq_di='M'
)
print(a1)
Declination Ti: 0 Qi: 1500.0 bbl/d Di: 0.03 M b: 0.0
We have defined a Arps class with a nominal declination rate of 0.03 monthly. This is usefull if you want to make a forecast on differnt time basis. You can get forecast on daily, monthly or annual basis from the same Arps Class
Time basis¶
When the time is defined with integers, they can represent any of the periods available (days, months or years). For example you can define forecast on daily basis each day or on daily basis each month. Next are the different ways you can create forecast
By calling the method forecast and providing either a time array or the start and end, and the frequencies of the output it returns a pandas DataFrame with the forecast with some useful metadata
In [15]:
Copied!
print('Calculate Daily Basis each day')
fr = a1.forecast(start=0,end=1095,freq_input='D',freq_output='D')
print(fr)
print('Calculate Daily Basis each day')
fr = a1.forecast(start=0,end=1095,freq_input='D',freq_output='D')
print(fr)
Calculate Daily Basis each day
oil_rate oil_cum iteration oil_volume
date
0 1500.000000 0.000000 0 1499.250250
1 1498.500750 1499.250250 0 1498.501000
2 1497.002998 2997.001999 0 1497.003248
3 1495.506743 4493.256745 0 1495.506993
4 1494.011984 5988.015984 0 1494.012233
... ... ... ... ...
1090 504.324741 995675.259440 0 504.324825
1091 503.820668 996179.332102 0 503.820752
1092 503.317099 996682.900944 0 503.317183
1093 502.814034 997185.966468 0 502.814117
1094 502.311471 997688.529178 0 502.562710
[1095 rows x 4 columns]
Let's Plot it instead
In [16]:
Copied!
print('Calculate Daily Basis each day - Plot')
fr = a1.plot(start=0,end=1095,freq_input='D',freq_output='D')
print('Calculate Daily Basis each day - Plot')
fr = a1.plot(start=0,end=1095,freq_input='D',freq_output='D')
Calculate Daily Basis each day - Plot
Generate forecast with more periods alternatives
In [17]:
Copied!
print('Calculate Daily Basis each Month')
fr = a1.forecast(start=0,end=1096,freq_input='D',freq_output='M')
print(fr)
a1.plot(start=0,end=1096,freq_input='D',freq_output='M',rate_kw=dict(palette=['darkgreen'],linestyle='-',linewidth=5))
print('Calculate Daily Basis each Month')
fr = a1.forecast(start=0,end=1096,freq_input='D',freq_output='M')
print(fr)
a1.plot(start=0,end=1096,freq_input='D',freq_output='M',rate_kw=dict(palette=['darkgreen'],linestyle='-',linewidth=5))
Calculate Daily Basis each Month
oil_rate oil_cum iteration oil_volume
date
0 1500.000000 0.000000 0 44331.699677
30 1455.668300 44331.699677 0 43676.599812
60 1412.646800 87353.199624 0 42385.761208
90 1370.896778 129103.222093 0 41133.072650
120 1330.380655 169619.344924 0 39917.406635
150 1291.061965 208938.035362 0 38737.668979
180 1252.905317 247094.682883 0 37592.797841
210 1215.876369 284123.631045 0 36481.762759
240 1179.941792 320058.208400 0 35403.563725
270 1145.069242 354930.758495 0 34357.230289
300 1111.227331 388772.668977 0 33341.820679
330 1078.385600 421614.399852 0 32356.420958
360 1046.514489 453485.510893 0 31400.144200
390 1015.585312 484414.688253 0 30472.129692
420 985.570230 514429.770277 0 29571.542157
450 956.442227 543557.772567 0 28697.571007
480 928.175088 571824.912291 0 27849.429607
510 900.743368 599256.631782 0 27026.354574
540 874.122379 625877.621439 0 26227.605085
570 848.288158 651711.841951 0 25452.462210
600 823.217454 676782.545859 0 24700.228269
630 798.887702 701112.298490 0 23970.226202
660 775.277002 724722.998262 0 23261.798956
690 752.364104 747635.896401 0 22574.308899
720 730.128384 769871.616060 0 21907.137244
750 708.549829 791450.170888 0 21259.683491
780 687.609017 812390.983042 0 20631.364889
810 667.287099 832712.900666 0 20021.615907
840 647.565785 852434.214856 0 19429.887731
870 628.427324 871572.676129 0 18855.647766
900 609.854490 890145.510389 0 18298.379157
930 591.830566 908169.434443 0 17757.580324
960 574.339329 925660.671037 0 17232.764512
990 557.365037 942634.963467 0 16723.459352
1020 540.892410 959107.589740 0 16229.206433
1050 524.906624 975093.376333 0 15749.560896
1080 509.393288 990606.711533 0 15513.335199
In [18]:
Copied!
print('Calculate Daily Basis each Year')
fr = a1.forecast(start=0,end=1096,freq_input='D',freq_output='A')
print(fr)
#Assign to a matplotlib axes
fig, ax = plt.subplots(figsize=(10,7))
a1.plot(start=0,end=1096,freq_input='D',freq_output='A',cum=True,rate_kw = {'palette':['green']}, ax=ax)
ax.set_title('Arps Forecast on Daily Basis each year', fontsize=14)
ax.set_xlabel('Time [days]', fontsize=10)
ax.set_ylabel('Oil Rate [bbl/d]', fontsize=10)
print('Calculate Daily Basis each Year')
fr = a1.forecast(start=0,end=1096,freq_input='D',freq_output='A')
print(fr)
#Assign to a matplotlib axes
fig, ax = plt.subplots(figsize=(10,7))
a1.plot(start=0,end=1096,freq_input='D',freq_output='A',cum=True,rate_kw = {'palette':['green']}, ax=ax)
ax.set_title('Arps Forecast on Daily Basis each year', fontsize=14)
ax.set_xlabel('Time [days]', fontsize=10)
ax.set_ylabel('Oil Rate [bbl/d]', fontsize=10)
Calculate Daily Basis each Year
oil_rate oil_cum iteration oil_volume
date
0 1500.000000 0.000000 0 458705.023683
365 1041.294976 458705.023683 0 388568.257432
730 722.863485 777136.514865 0 269742.782947
1095 501.809410 998190.589577 0 221054.074712
Out[18]:
Text(0, 0.5, 'Oil Rate [bbl/d]')
In [19]:
Copied!
print('Calculate Monthly Basis each Month')
fr = a1.forecast(start=0,end=37,freq_input='M',freq_output='M')
print(fr)
fig, ax = plt.subplots()
a1.plot(start=0,end=37,freq_input='M',freq_output='M',rate_kw=dict(palette=['darkgreen'],linestyle='-.',linewidth=2))
ax.set_title('Arps Forecast on Month Basis each month', fontsize=14)
ax.set_xlabel('Time [months]', fontsize=10)
ax.set_ylabel('Oil Rate [bbl/d]', fontsize=10)
print('Calculate Monthly Basis each Month')
fr = a1.forecast(start=0,end=37,freq_input='M',freq_output='M')
print(fr)
fig, ax = plt.subplots()
a1.plot(start=0,end=37,freq_input='M',freq_output='M',rate_kw=dict(palette=['darkgreen'],linestyle='-.',linewidth=2))
ax.set_title('Arps Forecast on Month Basis each month', fontsize=14)
ax.set_xlabel('Time [months]', fontsize=10)
ax.set_ylabel('Oil Rate [bbl/d]', fontsize=10)
Calculate Monthly Basis each Month
oil_rate oil_cum iteration oil_volume
date
0 1500.000000 0.000000 0 44331.699677
1 1455.668300 44331.699677 0 43676.599812
2 1412.646800 87353.199624 0 42385.761208
3 1370.896778 129103.222093 0 41133.072650
4 1330.380655 169619.344924 0 39917.406635
5 1291.061965 208938.035362 0 38737.668979
6 1252.905317 247094.682883 0 37592.797841
7 1215.876369 284123.631045 0 36481.762759
8 1179.941792 320058.208400 0 35403.563725
9 1145.069242 354930.758495 0 34357.230289
10 1111.227331 388772.668977 0 33341.820679
11 1078.385600 421614.399852 0 32356.420958
12 1046.514489 453485.510893 0 31400.144200
13 1015.585312 484414.688253 0 30472.129692
14 985.570230 514429.770277 0 29571.542157
15 956.442227 543557.772567 0 28697.571007
16 928.175088 571824.912291 0 27849.429607
17 900.743368 599256.631782 0 27026.354574
18 874.122379 625877.621439 0 26227.605085
19 848.288158 651711.841951 0 25452.462210
20 823.217454 676782.545859 0 24700.228269
21 798.887702 701112.298490 0 23970.226202
22 775.277002 724722.998262 0 23261.798956
23 752.364104 747635.896401 0 22574.308899
24 730.128384 769871.616060 0 21907.137244
25 708.549829 791450.170888 0 21259.683491
26 687.609017 812390.983042 0 20631.364889
27 667.287099 832712.900666 0 20021.615907
28 647.565785 852434.214856 0 19429.887731
29 628.427324 871572.676129 0 18855.647766
30 609.854490 890145.510389 0 18298.379157
31 591.830566 908169.434443 0 17757.580324
32 574.339329 925660.671037 0 17232.764512
33 557.365037 942634.963467 0 16723.459352
34 540.892410 959107.589740 0 16229.206433
35 524.906624 975093.376333 0 15749.560896
36 509.393288 990606.711533 0 15513.335199
Out[19]:
Text(0, 0.5, 'Oil Rate [bbl/d]')
In [20]:
Copied!
print('Calculate Monthly Basis each Year')
fr = a1.forecast(start=0,end=37,freq_input='M',freq_output='A')
print(fr)
fig, ax = plt.subplots()
a1.plot(start=0,end=37,freq_input='M',freq_output='A',rate_kw=dict(palette=['darkgreen'],linestyle='-.',linewidth=2))
ax.set_title('Arps Forecast on Month Basis each year', fontsize=14)
ax.set_xlabel('Time [months]', fontsize=10)
ax.set_ylabel('Oil Rate [bbl/d]', fontsize=10)
print('Calculate Monthly Basis each Year')
fr = a1.forecast(start=0,end=37,freq_input='M',freq_output='A')
print(fr)
fig, ax = plt.subplots()
a1.plot(start=0,end=37,freq_input='M',freq_output='A',rate_kw=dict(palette=['darkgreen'],linestyle='-.',linewidth=2))
ax.set_title('Arps Forecast on Month Basis each year', fontsize=14)
ax.set_xlabel('Time [months]', fontsize=10)
ax.set_ylabel('Oil Rate [bbl/d]', fontsize=10)
Calculate Monthly Basis each Year
oil_rate oil_cum iteration oil_volume
date
0 1500.000000 0.000000 0 453485.510893
12 1046.514489 453485.510893 0 384935.808030
24 730.128384 769871.616060 0 268560.600320
36 509.393288 990606.711533 0 220735.095473
Out[20]:
Text(0, 0.5, 'Oil Rate [bbl/d]')
In [21]:
Copied!
print('Calculate Annual Basis each Year')
fr = a1.forecast(start=0,end=4,freq_input='A',freq_output='A')
print(fr)
fig, ax = plt.subplots()
a1.plot(start=0,end=4,freq_input='A',freq_output='A',rate_kw=dict(palette=['darkgreen'],linestyle='-.',linewidth=2))
ax.set_title('Arps Forecast on Annual Basis each year', fontsize=14)
ax.set_xlabel('Time [Years]', fontsize=10)
ax.set_ylabel('Oil Rate [bbl/d]', fontsize=10)
print('Calculate Annual Basis each Year')
fr = a1.forecast(start=0,end=4,freq_input='A',freq_output='A')
print(fr)
fig, ax = plt.subplots()
a1.plot(start=0,end=4,freq_input='A',freq_output='A',rate_kw=dict(palette=['darkgreen'],linestyle='-.',linewidth=2))
ax.set_title('Arps Forecast on Annual Basis each year', fontsize=14)
ax.set_xlabel('Time [Years]', fontsize=10)
ax.set_ylabel('Oil Rate [bbl/d]', fontsize=10)
Calculate Annual Basis each Year
oil_rate oil_cum iteration oil_volume
date
0 1500.000000 0.000000e+00 0 459783.920767
1 1046.514489 4.597839e+05 0 390282.138697
2 730.128384 7.805643e+05 0 272290.608657
3 509.393288 1.004365e+06 0 223800.860687
Out[21]:
Text(0, 0.5, 'Oil Rate [bbl/d]')
Multiple Values¶
You may have noticed that the pandas dataframe returned with the forecast has a column name iteration. As we have defined so far a singles parameters for the Arps class it is created only one iteration.
You can declare Multiple values for any of the Arps parameters and they will result on Multiple iteration on the pandas dataframe.
In [22]:
Copied!
# Define an Arps Class with multiple values
a2 = dca.Arps(
ti = 0,
di = 0.03,
qi = [1500,1000,500],
b = 0,
freq_di='M'
)
print(a2)
# Define an Arps Class with multiple values
a2 = dca.Arps(
ti = 0,
di = 0.03,
qi = [1500,1000,500],
b = 0,
freq_di='M'
)
print(a2)
Declination Ti: 0 Qi: [1500.0, 1000.0, 500.0] bbl/d Di: 0.03 M b: 0.0
In [23]:
Copied!
print('Calculate Monthly Basis each month - Multiple parameters')
fr = a2.forecast(start=0,end=12,freq_input='M',freq_output='M')
#print(fr)
fig, ax = plt.subplots()
a2.plot(start=0,end=12,freq_input='M',freq_output='M')
print('Calculate Monthly Basis each month - Multiple parameters')
fr = a2.forecast(start=0,end=12,freq_input='M',freq_output='M')
#print(fr)
fig, ax = plt.subplots()
a2.plot(start=0,end=12,freq_input='M',freq_output='M')
Calculate Monthly Basis each month - Multiple parameters
Estimate Water Rate.¶
You can add water columns for the returning forecast by providing either a fluid rate or water cut. When any of them is provided the function assumes they are constant and the water estimation are simple substraction.
In [24]:
Copied!
# Define an Arps Class with multiple values - Fluid rate
a3 = dca.Arps(
ti = 0,
di = 0.03,
qi = [1500,1450],
b = [0,1],
freq_di='M',
fluid_rate = 2000
)
fr = a3.forecast(start=0,end=12,freq_input='M',freq_output='M')
print(fr)
# Define an Arps Class with multiple values - Fluid rate
a3 = dca.Arps(
ti = 0,
di = 0.03,
qi = [1500,1450],
b = [0,1],
freq_di='M',
fluid_rate = 2000
)
fr = a3.forecast(start=0,end=12,freq_input='M',freq_output='M')
print(fr)
oil_rate oil_cum iteration oil_volume fluid_rate \
date
0 1500.000000 0.000000 0 44331.699677 2000.0
1 1455.668300 44331.699677 0 43676.599812 2000.0
2 1412.646800 87353.199624 0 42385.761208 2000.0
3 1370.896778 129103.222093 0 41133.072650 2000.0
4 1330.380655 169619.344924 0 39917.406635 2000.0
5 1291.061965 208938.035362 0 38737.668979 2000.0
6 1252.905317 247094.682883 0 37592.797841 2000.0
7 1215.876369 284123.631045 0 36481.762759 2000.0
8 1179.941792 320058.208400 0 35403.563725 2000.0
9 1145.069242 354930.758495 0 34357.230289 2000.0
10 1111.227331 388772.668977 0 33341.820679 2000.0
11 1078.385600 421614.399852 0 32841.730875 2000.0
0 1450.000000 0.000000 1 42860.263250 2000.0
1 1407.766990 42860.263250 1 42244.958390 2000.0
2 1367.924528 84489.916780 1 41048.698150 2000.0
3 1330.275229 124957.659550 1 39918.338458 2000.0
4 1294.642857 164326.593695 1 38848.578447 2000.0
5 1260.869565 202654.816444 1 37834.671049 2000.0
6 1228.813559 239995.935792 1 36872.352494 2000.0
7 1198.347107 276399.521433 1 35957.782326 2000.0
8 1169.354839 311911.500445 1 35087.492125 2000.0
9 1141.732283 346574.505682 1 34258.341517 2000.0
10 1115.384615 380428.183478 1 33467.480278 2000.0
11 1090.225564 413509.466239 1 33081.282761 2000.0
water_rate bsw wor water_cum fluid_cum water_volume \
date
0 500.000000 0.250000 0.333333 0.000000 0.0 16329.950990
1 544.331700 0.272166 0.373939 16329.950990 60000.0 16975.273490
2 587.353200 0.293677 0.415782 33950.546979 120000.0 18246.846326
3 629.103222 0.314552 0.458899 52823.643642 180000.0 19480.838505
4 669.619345 0.334810 0.503329 72912.223990 240000.0 20678.360704
5 708.938035 0.354469 0.549112 94180.365050 300000.0 21840.490774
6 747.094683 0.373547 0.596290 116593.205537 360000.0 22968.274709
7 784.123631 0.392062 0.644904 140116.914468 420000.0 24062.727592
8 820.058208 0.410029 0.694999 164718.660720 480000.0 25124.834503
9 854.930758 0.427465 0.746619 190366.583475 540000.0 26155.551412
10 888.772669 0.444386 0.799812 217029.763544 600000.0 27155.806032
11 921.614400 0.460807 0.854624 244678.195540 660000.0 27648.431996
0 550.000000 0.275000 0.379310 0.000000 0.0 17766.990291
1 592.233010 0.296117 0.420690 17766.990291 60000.0 18364.627221
2 632.075472 0.316038 0.462069 36729.254442 120000.0 19527.003635
3 669.724771 0.334862 0.503448 56820.997561 180000.0 20626.228702
4 705.357143 0.352679 0.544828 77981.711847 240000.0 21667.313665
5 739.130435 0.369565 0.586207 100155.624891 300000.0 22654.753132
6 771.186441 0.385593 0.627586 123291.218111 360000.0 23592.589999
7 801.652893 0.400826 0.668966 147340.804888 420000.0 24484.470808
8 830.645161 0.415323 0.710345 172260.159727 480000.0 25333.693167
9 858.267717 0.429134 0.751724 198008.191223 540000.0 26143.246517
10 884.615385 0.442308 0.793103 224546.652761 600000.0 26915.847311
11 909.774436 0.454887 0.834483 251839.885844 660000.0 27293.233083
fluid_volume
date
0 60000.0
1 60000.0
2 60000.0
3 60000.0
4 60000.0
5 60000.0
6 60000.0
7 60000.0
8 60000.0
9 60000.0
10 60000.0
11 60000.0
0 60000.0
1 60000.0
2 60000.0
3 60000.0
4 60000.0
5 60000.0
6 60000.0
7 60000.0
8 60000.0
9 60000.0
10 60000.0
11 60000.0
/Users/scuervo91/Documents/dev/dcapy/dcapy/dca/arps.py:87: RuntimeWarning: invalid value encountered in multiply return f*(g-h)
In [25]:
Copied!
a4 = dca.Arps(
ti = 0,
di = 0.03,
qi = [1500,1450],
b = [0,1],
freq_di='M',
bsw = 0.6
)
fr = a4.forecast(start=0,end=12,freq_input='M',freq_output='M')
print(fr)
a4 = dca.Arps(
ti = 0,
di = 0.03,
qi = [1500,1450],
b = [0,1],
freq_di='M',
bsw = 0.6
)
fr = a4.forecast(start=0,end=12,freq_input='M',freq_output='M')
print(fr)
oil_rate oil_cum iteration oil_volume bsw water_rate \
date
0 1500.000000 0.000000 0 44331.699677 0.6 2250.000000
1 1455.668300 44331.699677 0 43676.599812 0.6 2183.502450
2 1412.646800 87353.199624 0 42385.761208 0.6 2118.970201
3 1370.896778 129103.222093 0 41133.072650 0.6 2056.345167
4 1330.380655 169619.344924 0 39917.406635 0.6 1995.570983
5 1291.061965 208938.035362 0 38737.668979 0.6 1936.592947
6 1252.905317 247094.682883 0 37592.797841 0.6 1879.357976
7 1215.876369 284123.631045 0 36481.762759 0.6 1823.814553
8 1179.941792 320058.208400 0 35403.563725 0.6 1769.912687
9 1145.069242 354930.758495 0 34357.230289 0.6 1717.603862
10 1111.227331 388772.668977 0 33341.820679 0.6 1666.840997
11 1078.385600 421614.399852 0 32841.730875 0.6 1617.578400
0 1450.000000 0.000000 1 42860.263250 0.6 2175.000000
1 1407.766990 42860.263250 1 42244.958390 0.6 2111.650485
2 1367.924528 84489.916780 1 41048.698150 0.6 2051.886792
3 1330.275229 124957.659550 1 39918.338458 0.6 1995.412844
4 1294.642857 164326.593695 1 38848.578447 0.6 1941.964286
5 1260.869565 202654.816444 1 37834.671049 0.6 1891.304348
6 1228.813559 239995.935792 1 36872.352494 0.6 1843.220339
7 1198.347107 276399.521433 1 35957.782326 0.6 1797.520661
8 1169.354839 311911.500445 1 35087.492125 0.6 1754.032258
9 1141.732283 346574.505682 1 34258.341517 0.6 1712.598425
10 1115.384615 380428.183478 1 33467.480278 0.6 1673.076923
11 1090.225564 413509.466239 1 33081.282761 0.6 1635.338346
fluid_rate wor water_cum fluid_cum water_volume \
date
0 3750.000000 1.5 0.000000 0.000000e+00 65505.073515
1 3639.170751 1.5 65505.073515 1.091751e+05 64537.089766
2 3531.617001 1.5 129074.179531 2.151236e+05 62629.730511
3 3427.241945 1.5 190764.534537 3.179409e+05 60778.742242
4 3325.951638 1.5 250631.664016 4.177194e+05 58982.458944
5 3227.654912 1.5 308729.452424 5.145491e+05 57239.263839
6 3132.263293 1.5 365110.191695 6.085170e+05 55547.587937
7 3039.690922 1.5 419824.628298 6.997077e+05 53905.908612
8 2949.854479 1.5 472922.008920 7.882033e+05 52312.748245
9 2862.673104 1.5 524450.124787 8.740835e+05 50766.672882
10 2778.068328 1.5 574455.354683 9.574256e+05 49266.290951
11 2695.964000 1.5 622982.706690 1.038305e+06 48527.352007
0 3625.000000 1.5 0.000000 0.000000e+00 63349.514563
1 3519.417476 1.5 63349.514563 1.055825e+05 62453.059168
2 3419.811321 1.5 124906.118337 2.081769e+05 60709.494547
3 3325.688073 1.5 184768.503658 3.079475e+05 59060.656946
4 3236.607143 1.5 243027.432229 4.050457e+05 57499.029503
5 3152.173913 1.5 299766.562664 4.996109e+05 56017.870302
6 3072.033898 1.5 355063.172833 5.917720e+05 54611.115002
7 2995.867769 1.5 408988.792668 6.816480e+05 53273.293788
8 2923.387097 1.5 461609.760410 7.693496e+05 51999.460249
9 2854.330709 1.5 512987.713166 8.549795e+05 50785.130224
10 2788.461538 1.5 563180.020858 9.386334e+05 49626.229034
11 2725.563910 1.5 612240.171234 1.020400e+06 49060.150376
fluid_volume
date
0 109175.122524
1 107561.816276
2 104382.884186
3 101297.903737
4 98304.098239
5 95398.773066
6 92579.313228
7 89843.181021
8 87187.913741
9 84611.121470
10 82110.484919
11 80878.920011
0 105582.524272
1 104088.431947
2 101182.490912
3 98434.428244
4 95831.715839
5 93363.117170
6 91018.525004
7 88788.822981
8 86665.767082
9 84641.883707
10 82710.381724
11 81766.917293
/Users/scuervo91/Documents/dev/dcapy/dcapy/dca/arps.py:87: RuntimeWarning: invalid value encountered in multiply return f*(g-h)
Remember you can pass a time list with a custom time distribution
In [26]:
Copied!
fr = a4.forecast(time_list=[0,2,3,4,6,8,12],freq_input='M',freq_output='M')
print(fr)
fr = a4.forecast(time_list=[0,2,3,4,6,8,12],freq_input='M',freq_output='M')
print(fr)
oil_rate oil_cum iteration oil_volume bsw water_rate \
date
0 1500.000000 0.000000 0 87353.199624 0.6 2250.000000
2 1412.646800 87353.199624 0 64551.611047 0.6 2118.970201
3 1370.896778 129103.222093 0 41133.072650 0.6 2056.345167
4 1330.380655 169619.344924 0 58995.730395 0.6 1995.570983
6 1252.905317 247094.682883 0 75219.431738 0.6 1879.357976
8 1179.941792 320058.208400 0 103195.414005 0.6 1769.912687
12 1046.514489 453485.510893 0 133427.302493 0.6 1569.771734
0 1450.000000 0.000000 1 84489.916780 0.6 2175.000000
2 1367.924528 84489.916780 1 62478.829775 0.6 2051.886792
3 1330.275229 124957.659550 1 39918.338458 0.6 1995.412844
4 1294.642857 164326.593695 1 57519.138121 0.6 1941.964286
6 1228.813559 239995.935792 1 73792.453375 0.6 1843.220339
8 1169.354839 311911.500445 1 102928.439421 0.6 1754.032258
12 1066.176471 445852.814635 1 133941.314190 0.6 1599.264706
fluid_rate wor water_cum fluid_cum water_volume \
date
0 3750.000000 1.5 0.000000 0.000000e+00 127138.212034
2 3531.617001 1.5 127138.212034 2.118970e+05 94414.283520
3 3427.241945 1.5 188828.567040 3.147143e+05 60778.742242
4 3325.951638 1.5 248695.696518 4.144928e+05 86314.304009
6 3132.263293 1.5 361457.175059 6.024286e+05 109478.119892
8 2949.854479 1.5 467651.936303 7.794199e+05 147283.684642
12 2616.286223 1.5 656024.544342 1.093374e+06 188372.608039
0 3625.000000 1.5 0.000000 0.000000e+00 123113.207547
2 3419.811321 1.5 123113.207547 2.051887e+05 91487.796434
3 3325.688073 1.5 182975.592868 3.049593e+05 59060.656946
4 3236.607143 1.5 241234.521440 4.020575e+05 84426.074455
6 3072.033898 1.5 351827.741779 5.863796e+05 107917.577911
8 2923.387097 1.5 457069.677263 7.617828e+05 148576.850095
12 2665.441176 1.5 648981.441968 1.081636e+06 191911.764706
fluid_volume
date
0 211897.020056
2 157357.139200
3 101297.903737
4 143857.173349
6 182463.533154
8 245472.807736
12 313954.346732
0 205188.679245
2 152479.660724
3 98434.428244
4 140710.124092
6 179862.629852
8 247628.083491
12 319852.941176
/Users/scuervo91/Documents/dev/dcapy/dcapy/dca/arps.py:87: RuntimeWarning: invalid value encountered in multiply return f*(g-h)
Using Arps class with dates¶
You can also define the Arps class with dates. Like before, the output frequency approach also works
In [27]:
Copied!
a5 = dca.Arps(
ti = date(2021,1,1),
di = [0.03,0.05],
qi = 1500,
b = 0,
freq_di='M',
fluid_rate = 2000
)
print(a5)
a5 = dca.Arps(
ti = date(2021,1,1),
di = [0.03,0.05],
qi = 1500,
b = 0,
freq_di='M',
fluid_rate = 2000
)
print(a5)
Declination Ti: 2021-01-01 Qi: 1500.0 bbl/d Di: [0.03, 0.05] M b: 0.0
In [28]:
Copied!
fr = a5.forecast(start=date(2021,1,1),end=date(2021,1,10),freq_output='D')
print(fr.head())
print(fr.tail())
fr = a5.forecast(start=date(2021,1,1),end=date(2021,1,10),freq_output='D')
print(fr.head())
print(fr.tail())
oil_rate oil_cum iteration oil_volume fluid_rate \
date
2021-01-01 1500.000000 0.000000 0 1499.250250 2000.0
2021-01-02 1498.500750 1499.250250 0 1498.501000 2000.0
2021-01-03 1497.002998 2997.001999 0 1497.003248 2000.0
2021-01-04 1495.506743 4493.256745 0 1495.506993 2000.0
2021-01-05 1494.011984 5988.015984 0 1494.012233 2000.0
water_rate bsw wor water_cum fluid_cum \
date
2021-01-01 500.000000 0.250000 0.333333 0.000000 0.0
2021-01-02 501.499250 0.250750 0.334667 501.499250 2000.0
2021-01-03 502.997002 0.251499 0.336003 1004.496252 4000.0
2021-01-04 504.493257 0.252247 0.337339 1508.989509 6000.0
2021-01-05 505.988016 0.252994 0.338677 2014.977525 8000.0
water_volume fluid_volume
date
2021-01-01 501.499250 2000.0
2021-01-02 502.248126 2000.0
2021-01-03 503.745129 2000.0
2021-01-04 505.240636 2000.0
2021-01-05 506.734649 2000.0
oil_rate oil_cum iteration oil_volume fluid_rate \
date
2021-01-06 1487.551939 7468.836625 1 1487.552628 2000.0
2021-01-07 1485.074751 8955.149626 1 1485.075438 2000.0
2021-01-08 1482.601687 10438.987501 1 1482.602374 2000.0
2021-01-09 1480.132743 11920.354374 1 1480.133428 2000.0
2021-01-10 1477.667909 13399.254357 1 1478.899984 2000.0
water_rate bsw wor water_cum fluid_cum \
date
2021-01-06 512.448061 0.256224 0.344491 2537.385677 10000.0
2021-01-07 514.925249 0.257463 0.346734 3052.310926 12000.0
2021-01-08 517.398313 0.258699 0.348980 3569.709238 14000.0
2021-01-09 519.867257 0.259934 0.351230 4089.576496 16000.0
2021-01-10 522.332091 0.261166 0.353484 4611.908586 18000.0
water_volume fluid_volume
date
2021-01-06 513.686655 2000.0
2021-01-07 516.161781 2000.0
2021-01-08 518.632785 2000.0
2021-01-09 521.099674 2000.0
2021-01-10 522.332091 2000.0
In [29]:
Copied!
a5.plot(start=date(2021,1,1),end=date(2021,1,10),freq_output='D')
a5.plot(start=date(2021,1,1),end=date(2021,1,10),freq_output='D')
In [17]:
Copied!
fr = a5.forecast(start=date(2021,1,1),end=date(2022,1,1),freq_output='M')
print(fr)
fr = a5.forecast(start=date(2021,1,1),end=date(2022,1,1),freq_output='M')
print(fr)
oil_rate oil_cum iteration oil_volume fluid_rate \
date
2021-01 1500.000000 0.000000 0 45786.640386 2000.0
2021-02 1454.213360 45786.640386 0 42969.923132 2000.0
2021-03 1414.060154 85939.846264 0 41658.290854 2000.0
2021-04 1370.896778 129103.222093 0 41839.749330 2000.0
2021-05 1330.380655 169619.344924 0 40562.614959 2000.0
2021-06 1289.771548 210228.452011 0 39363.808516 2000.0
2021-07 1251.653038 248346.961956 0 38162.250820 2000.0
2021-08 1213.447046 286552.953650 0 37622.883693 2000.0
2021-09 1176.407271 323592.729343 0 35903.932453 2000.0
2021-10 1141.639181 358360.818557 0 34807.985491 2000.0
2021-11 1106.791300 393208.700324 0 33779.254051 2000.0
2021-12 1074.080673 425919.326659 0 32748.161679 2000.0
2022-01 1041.294976 458705.023683 0 32785.697024 2000.0
2021-01 1500.000000 0.000000 1 45319.173710 2000.0
2021-02 1424.468044 45319.173710 1 42143.967983 2000.0
2021-03 1359.520107 84287.935965 1 40021.823754 2000.0
2021-04 1291.061965 125362.821217 1 39427.193132 2000.0
2021-05 1228.096130 163142.322230 1 37441.851116 2000.0
2021-06 1166.255794 200246.523450 1 35615.790490 2000.0
2021-07 1109.376828 234373.903209 1 33822.370272 2000.0
2021-08 1053.514560 267891.263994 1 32673.483509 2000.0
2021-09 1000.465216 299720.870227 1 30552.782427 2000.0
2021-10 951.671952 328996.828847 1 29014.308144 2000.0
2021-11 903.750856 357749.486516 1 27599.263638 2000.0
2021-12 859.674406 384195.356123 1 26209.512724 2000.0
2022-01 816.385813 410168.511963 1 25973.155840 2000.0
water_rate bsw wor water_cum fluid_cum \
date
2021-01 500.000000 0.250000 0.333333 0.000000 0.0
2021-02 545.786640 0.272893 0.375314 16919.385852 62000.0
2021-03 585.939846 0.292970 0.414367 33325.701547 118000.0
2021-04 629.103222 0.314552 0.458899 52827.901432 180000.0
2021-05 669.619345 0.334810 0.503329 72916.481780 240000.0
2021-06 710.228452 0.355114 0.550662 94933.563792 302000.0
2021-07 748.346962 0.374173 0.597887 117383.972651 362000.0
2021-08 786.552954 0.393276 0.648197 141767.114214 424000.0
2021-09 823.592729 0.411796 0.700091 167298.488824 486000.0
2021-10 858.360819 0.429180 0.751867 193049.313380 546000.0
2021-11 893.208700 0.446604 0.807025 220738.783091 608000.0
2021-12 925.919327 0.462960 0.862058 248516.362890 668000.0
2022-01 958.705024 0.479353 0.920685 278236.218624 730000.0
2021-01 500.000000 0.250000 0.333333 0.000000 0.0
2021-02 575.531956 0.287766 0.404033 17841.490642 62000.0
2021-03 640.479893 0.320240 0.471107 35774.927653 118000.0
2021-04 708.938035 0.354469 0.549112 57752.006750 180000.0
2021-05 771.903870 0.385952 0.628537 80909.122861 240000.0
2021-06 833.744206 0.416872 0.714890 106755.193239 302000.0
2021-07 890.623172 0.445312 0.802814 133473.888400 362000.0
2021-08 946.485440 0.473243 0.898408 162814.937039 424000.0
2021-09 999.534784 0.499767 0.999070 193800.515335 486000.0
2021-10 1048.328048 0.524164 1.101565 225250.356777 546000.0
2021-11 1096.249144 0.548125 1.212999 259234.080247 608000.0
2021-12 1140.325594 0.570163 1.326462 293443.848053 668000.0
2022-01 1183.614187 0.591807 1.449822 330135.887838 730000.0
water_volume fluid_volume
date
2021-01 16919.385852 62000.0
2021-02 16662.850774 59000.0
2021-03 17954.257790 59000.0
2021-04 19795.390116 61000.0
2021-05 21052.831180 61000.0
2021-06 22233.745436 61000.0
2021-07 23416.775211 61000.0
2021-08 24957.258086 62000.0
2021-09 25641.099583 61000.0
2021-10 26720.147133 61000.0
2021-11 27733.524755 61000.0
2021-12 28748.717767 61000.0
2022-01 29719.855734 62000.0
2021-01 17841.490642 62000.0
2021-02 17887.463827 59000.0
2021-03 19955.258054 59000.0
2021-04 22567.097604 61000.0
2021-05 24501.593245 61000.0
2021-06 26282.382769 61000.0
2021-07 28029.871900 61000.0
2021-08 30163.313467 62000.0
2021-09 31217.709869 61000.0
2021-10 32716.782456 61000.0
2021-11 34096.745638 61000.0
2021-12 35450.903795 61000.0
2022-01 36692.039785 62000.0
In [18]:
Copied!
fr = a5.forecast(start=date(2021,1,1),end=date(2026,1,1),freq_output='A')
print(fr)
fr = a5.forecast(start=date(2021,1,1),end=date(2026,1,1),freq_output='A')
print(fr)
oil_rate oil_cum iteration oil_volume fluid_rate \
date
2021 1500.000000 0.000000e+00 0 458705.023683 2000.0
2022 1041.294976 4.587050e+05 0 388568.257432 2000.0
2023 722.863485 7.771365e+05 0 269742.782947 2000.0
2024 501.809410 9.981906e+05 0 187428.626667 2000.0
2025 348.006232 1.151994e+06 0 130112.324911 2000.0
2026 241.584761 1.258415e+06 0 106421.471200 2000.0
2021 1500.000000 0.000000e+00 1 410168.511963 2000.0
2022 816.385813 4.101685e+05 1 316702.840738 2000.0
2023 444.323864 6.334057e+05 1 172367.804160 2000.0
2024 241.826466 7.549041e+05 1 93878.173091 2000.0
2025 131.396621 8.211620e+05 1 51093.872466 2000.0
2026 71.513558 8.570919e+05 1 35929.837558 2000.0
water_rate bsw wor water_cum fluid_cum \
date
2021 500.000000 0.250000 0.333333 0.000000e+00 0.0
2022 958.705024 0.479353 0.920685 3.499273e+05 730000.0
2023 1277.136515 0.638568 1.766774 8.160822e+05 1460000.0
2024 1498.190590 0.749095 2.985577 1.362922e+06 2190000.0
2025 1651.993768 0.825997 4.747024 1.967551e+06 2922000.0
2026 1758.415239 0.879208 7.278668 2.609373e+06 3652000.0
2021 500.000000 0.250000 0.333333 0.000000e+00 0.0
2022 1183.614187 0.591807 1.449822 4.320192e+05 730000.0
2023 1555.676136 0.777838 3.501221 9.998410e+05 1460000.0
2024 1758.173534 0.879087 7.270393 1.641574e+06 2190000.0
2025 1868.603379 0.934302 14.221092 2.325483e+06 2922000.0
2026 1928.486442 0.964243 26.966725 3.029381e+06 3652000.0
water_volume fluid_volume
date
2021 349927.333644 730000.0
2022 408041.080785 730000.0
2023 506497.196561 730000.0
2024 575734.642178 731000.0
2025 623225.640771 731000.0
2026 641821.562380 730000.0
2021 432019.178111 730000.0
2022 499920.483838 730000.0
2023 604777.564702 730000.0
2024 662821.088355 731000.0
2025 693903.194105 731000.0
2026 703897.551339 730000.0
Plot them
In [19]:
Copied!
a5.plot(start=date(2021,1,1),end=date(2022,1,1),freq_output='M')
a5.plot(start=date(2021,1,1),end=date(2022,1,1),freq_output='M')
