Oil Model¶

You can create Oil, Gas and Water Python Objects that contain the main information that allow to characterize the fluid. They can contain, PVT, Chromatography, gravity among other values specific to the fluid.

By creating an Oil object you can obtain a PVT table throughout common correlations described by Ahmed (Reservoir Engineering Handbook & Equation of State and PVT Analysis ) and by Banzer (Correlaciones Numéricas PVT)

In [7]:

from pvtpy.pvt import PVT
import pvtpy.black_oil as bk
from pvtpy.fluids import Oil, InitialConditions
from pvtpy.units import Pressure, Temperature
import numpy as np
import pandas as pd
import seaborn as sns

from pvtpy.pvt import PVT import pvtpy.black_oil as bk from pvtpy.fluids import Oil, InitialConditions from pvtpy.units import Pressure, Temperature import numpy as np import pandas as pd import seaborn as sns

Initial Conditions.¶

It is required to provide initial conditions

In [8]:

init = InitialConditions(pressure=Pressure(value=3600), temperature=Temperature(value=160))
init

init = InitialConditions(pressure=Pressure(value=3600), temperature=Temperature(value=160)) init

Out[8]:

InitialConditions(pressure=Pressure(value=3600.0, unit=<PressureUnits.psi: 'psi'>), temperature=Temperature(value=160.0, unit=<TemperatureUnits.farenheit: 'farenheit'>))

In [9]:

cor = bk.SetOilCorrelations()
cor

cor = bk.SetOilCorrelations() cor

Out[9]:

SetOilCorrelations(pb=<pb_correlations.standing: 'standing'>, rs=<rs_correlations.standing: 'standing'>, bo=<bo_correlations.standing: 'standing'>, co_above=<co_above_correlations.vazquez_beggs: 'vazquez_beggs'>, co_below=<co_below_correlations.mccain: 'mccain'>, muod=<muod_correlations.beal: 'beal'>, muo_above=<muo_above_correlations.beal: 'beal'>, muo_below=<muo_below_correlations.beggs: 'beggs'>, rho=<rho_correlations.banzer: 'banzer'>)

In [10]:

oil1 = Oil.pvt_from_correlation(
    initial_conditions=init,
    api = 31,
    rsb = 675,
    sg_gas = 0.95,
    correlations=cor
)
print(oil1.pvt.df())

oil1 = Oil.pvt_from_correlation( initial_conditions=init, api = 31, rsb = 675, sg_gas = 0.95, correlations=cor ) print(oil1.pvt.df())

                     rs        bo        co       muo        rho
pressure                                                        
20.000000     70.222511  1.076649  0.018716  2.272673  51.178738
282.105263   210.895351  1.144722  0.000403  1.408308  49.537946
544.210526   285.093631  1.182737  0.000156  1.191746  48.641478
806.315789   346.908386  1.215335  0.000088  1.062432  47.890476
1068.421053  403.667546  1.245936  0.000058  0.969604  47.201993
1330.526316  458.048011  1.275809  0.000043  0.897034  46.545992
1592.631579  511.335510  1.305572  0.000033  0.837464  45.908448
1854.736842  564.242666  1.335573  0.000026  0.787013  45.281961
2116.842105  617.201069  1.366026  0.000022  0.743351  44.662384
2378.947368  670.488149  1.397074  0.000018  0.704963  44.047339
2641.052632  675.000000  1.394688  0.000015  0.712684  43.926841
2903.157895  675.000000  1.390158  0.000014  0.724424  43.784158
3165.263158  675.000000  1.386389  0.000013  0.736164  43.665459
3427.368421  675.000000  1.383205  0.000012  0.747904  43.565167
3689.473684  675.000000  1.380479  0.000011  0.759644  43.479309
3951.578947  675.000000  1.378119  0.000010  0.771384  43.404977
4213.684211  675.000000  1.376056  0.000009  0.783124  43.339997
4475.789474  675.000000  1.374237  0.000009  0.794864  43.282708
4737.894737  675.000000  1.372621  0.000008  0.806604  43.231822
5000.000000  675.000000  1.371176  0.000008  0.818344  43.186321

Once created an Oil object without a PVT, you can generate a tabulated table with the main properties using PVT correlations.

To set the the correlations to be used initialize an instance of `bk.SetOilCorrelations'. By default it has certain correlations

In [11]:

oil1.pvt.interpolate([500,1500,2500,3500])

oil1.pvt.interpolate([500,1500,2500,3500])

Out[11]:

	rs	bo	co	muo	rho
pressure
500	272.578258	1.176325	0.000197	1.228274	48.792690
1500	492.502980	1.295053	0.000036	0.858517	46.133765
2500	672.571936	1.395972	0.000017	0.708529	43.991687
3500	675.000000	1.382449	0.000011	0.751157	43.541375

In [12]:

oil1.pvt.df().plot(subplots=True, figsize=(10,7))

oil1.pvt.df().plot(subplots=True, figsize=(10,7))

Out[12]:

array([<AxesSubplot:xlabel='pressure'>, <AxesSubplot:xlabel='pressure'>,
       <AxesSubplot:xlabel='pressure'>, <AxesSubplot:xlabel='pressure'>,
       <AxesSubplot:xlabel='pressure'>], dtype=object)

In [13]:

print(oil1.to_ecl())

print(oil1.to_ecl())

-- OIL PVT TABLE FOR LIVE OIL
PVTO
-- rs      pres  bo      visc
-- Mscf/rb psi   RB/STB  cP  
-- ------- ----  ----    ---- 
0.070 20.000 1.077 2.273/
0.212 284.559 1.145 1.406/
0.286 549.119 1.183 1.189/
0.349 813.678 1.216 1.060/
0.406 1078.238 1.247 0.967/
0.461 1342.797 1.277 0.894/
0.514 1607.356 1.307 0.835/
0.568 1871.916 1.338 0.784/
0.621 2136.475 1.368 0.740/
0.671 2401.035 1.397 0.706
-- Unsaturated Data
2920.828 1.390 0.725
3440.621 1.383 0.748
3960.414 1.378 0.772
4480.207 1.374 0.795
5000.000 1.371 0.818/
/

In [ ]: