-----------------------------A collection of Reservoir Engineering Utilities -----------------------------
This set of functions focuses on those that the author uses often while crafting programming solutions. These are the scripts that are often copy/pasted from previous work - sometimes slightly modified - resulting in a trail of slightly different versions over the years. Some attempt has been made here to make this implementation flexible enough such that it can be relied on as-is going forward.
Includes functions to perform simple calculations including;
- Inflow for oil and gas
- PVT Calculations for oil
- PVT calculation for gas
- Return critical parameters for typical components
- Creation of Black Oil Table information
- Creation of layered permeability distribution consistent with a Lorenze heterogeneity factor
- Extract problem cells information from Intesect (IX) print files
- Generation of AQUTAB include file influence functions for use in ECLIPSE
- Creation of Corey and LET relative permeability tables in Eclipse format
- Calculation of Methane and CO2 saturated brine properties
Upgrade previous installations with
pip install pyrestoolbox --upgrade| Inflow |
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| Gas PVT |
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| Component Library |
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| Oil PVT |
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| CH4 Saturated Brine PVT |
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| CO2 Saturated Brine PVT |
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| Permeability Layering |
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| Simulation Helpers |
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Install the library with pip:
pip install pyrestoolboxImport library into your project and start using.
A simple example below of estimating oil bubble point pressure.
>>> from pyrestoolbox import pyrestoolbox as rtb
>>> rtb.oil_pbub(api=43, degf=185, rsb=2350, sg_g =0.72, pbmethod ='VALMC')
5179.51086900132A set of Gas-Oil relative permeability curves with the LET method
>>> import matplotlib.pyplot as plt
>>> df = rtb.simtools.rel_perm(rows=25, krtable='SGOF', krfamily='LET', kromax =1, krgmax =1, swc =0.2, sorg =0.15, Lo=2.5, Eo = 1.25, To = 1.75, Lg = 1.2, Eg = 1.5, Tg = 2.0)
>>> plt.plot(df['Sg'], df['Krgo'], c = 'r', label='Gas')
>>> plt.plot(df['Sg'], df['Krog'], c = 'g', label='Oil')
>>> plt.title('SGOF Gas Oil LET Relative Permeability Curves')
>>> plt.xlabel('Sg')
>>> plt.ylabel('Kr')
>>> plt.legend()
>>> plt.grid('both')
>>> plt.plot()
Or a set of Water-Oil relative permeability curves with the Corey method
>>> df = rtb.simtools.rel_perm(rows=25, krtable='SWOF', kromax =1, krwmax =0.25, swc =0.15, swcr = 0.2, sorw =0.15, no=2.5, nw=1.5)
>>> plt.plot(df['Sw'], df['Krow'], c = 'g', label='Oil')
>>> plt.plot(df['Sw'], df['Krwo'], c = 'b', label='Water')
>>> plt.title('SWOF Water Oil Corey Relative Permeability Curves')
>>> plt.xlabel('Sw')
>>> plt.ylabel('Kr')
>>> plt.legend()
>>> plt.grid('both')
>>> plt.plot()
A set of dimensionless pressures for the constant terminal rate Van Everdingin & Hurst aquifer, along with an AQUTAB.INC export for use in ECLIPSE.
>>> ReDs = [1.5, 2, 3, 5, 10, 25, 1000]
>>> tds, pds = rtb.influence_tables(ReDs=ReDs, export=True)
>>>
>>> for p, pd in enumerate(pds):
>>> plt.plot(tds, pd, label = str(ReDs[p]))
>>>
>>> plt.xscale('log')
>>> plt.yscale('log')
>>> plt.legend(loc='upper left')
>>> plt.grid(which='both')
>>> plt.xlabel('Dimensionless Time (tD)')
>>> plt.ylabel('Dimensionless Pressure Drop (PD)')
>>> plt.title('Constant Terminal Rate Solution')
>>> plt.show()
Or creating black oil table information for oil
>>> results = rtb.make_bot_og(pi=4000, api=38, degf=175, sg_g=0.68, pmax=5000, pb=3900, rsb=2300, nrows=50)
>>> df, st_deno, st_deng, res_denw, res_cw, visw, pb, rsb, rsb_frac, usat = results['bot'], results['deno'], results['deng'], results['denw'], results['cw'], results['uw'], results['pb'], results['rsb'], results['rsb_scale'], results['usat']
>>>
>>> print('Stock Tank Oil Density:', st_deno, 'lb/cuft')
>>> print('Stock Tank Gas Density:', st_deng, 'lb/cuft')
>>> print('Reservoir Water Density:', res_denw, 'lb/cuft')
>>> print('Reservoir Water Compressibility:', res_cw, '1/psi')
>>> print('Reservoir Water Viscosity:', visw,'cP')
>>>
>>> fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(10,10))
>>> ax1.plot(df['Pressure (psia)'], df['Rs (mscf/stb)'])
>>> ax2.plot(df['Pressure (psia)'], df['Bo (rb/stb)'])
>>> ax3.plot(df['Pressure (psia)'], df['uo (cP)'])
>>> ax4.semilogy(df['Pressure (psia)'], df['Co (1/psi)'])
>>>
>>> fig.suptitle('Black Oil Properties')
>>> ax1.set_title("Rs vs P")
>>> ax1.set_ylabel('Rs (mscf/stb)')
>>> ax1.set_xlabel('Pressure (psia)')
>>> ax1.grid('both')
>>>
>>> ax2.set_title("Bo vs P")
>>> ax2.set_ylabel('Bo (rb/stb)')
>>> ax2.set_xlabel('Pressure (psia)')
>>> ax2.grid('both')
>>>
>>> ax3.set_title("Viso vs P")
>>> ax3.set_xlabel('Pressure (psia)')
>>> ax3.set_ylabel('Viscosity (cP)')
>>> ax3.grid('both')
>>>
>>> ax4.set_title("Co vs P")
>>> ax4.set_ylabel('Co (1/psi)')
>>> ax4.set_xlabel('Pressure (psia)')
>>> ax4.grid('both')
>>>
>>> plt.tight_layout()
>>> plt.show()
Iteratively solving for Rsb fraction to use in order to harmonize user specified Pb and Rsb
Stock Tank Oil Density: 52.09203539823009 lb/cuft
Stock Tank Gas Density: 0.052046870460837856 lb/cuft
Reservoir Water Density: 61.40223160167964 lb/cuft
Reservoir Water Compressibility: 2.930237693350768e-06 1/psi
Reservoir Water Viscosity: 0.3640686136171888 cP
And gas
>>> fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(10,10))
>>> ax1.semilogy(df['Pressure (psia)'], df['Bg (rb/mscf'])
>>> ax2.plot(df['Pressure (psia)'], df['ug (cP)'])
>>> ax3.plot(df['Pressure (psia)'], df['Gas Z (v/v)'])
>>> ax4.semilogy(df['Pressure (psia)'], df['Cg (1/psi)'])
>>> ...
>>> plt.show()
With ability to generate Live Oil PVTO style table data as well
>>> pb = 4500
>>> results = rtb.make_bot_og(pvto=True, pi=4000, api=38, degf=175, sg_g=0.68, pmax=5500, pb=pb, nrows=25, export=True)
>>> df, st_deno, st_deng, res_denw, res_cw, visw, pb, rsb, rsb_frac, usat = results['bot'], results['deno'], results['deng'], results['denw'], results['cw'], results['uw'], results['pb'], results['rsb'], results['rsb_scale'], results['usat']
>>>
>>> if len(usat) == 0:
>>> usat_flag = False
>>> else:
>>> usat_flag=True
>>> usat_p, usat_bo, usat_uo = usat
>>>
>>> try:
>>> pb_idx = df['Pressure (psia)'].tolist().index(pb)
>>> bob = df['Bo (rb/stb)'].iloc[pb_idx]
>>> rsb = df['Rs (mscf/stb)'].iloc[pb_idx]
>>> uob = df['uo (cP)'].iloc[pb_idx]
>>> cob = df['Co (1/psi)'].iloc[pb_idx]
>>> no_pb = False
>>> except:
>>> print('Pb was > Pmax')
>>> no_pb = True
>>>
>>> print('Pb (psia):', pb)
>>> print('Bob (rb/stb):', bob)
>>> print('Rsb (mscf/stb):', rsb)
>>> print('Rsb Scaling Required:', rsb_frac)
>>> print('Visob (cP):', uob)
>>> print('Cob (1/psi):', cob,'\n')
>>> print('Stock Tank Oil Density:', st_deno, 'lb/cuft')
>>> print('Stock Tank Gas Density:', st_deng, 'lb/cuft')
>>> print('Reservoir Water Density:', res_denw, 'lb/cuft')
>>> print('Reservoir Water Compressibility:', res_cw, '1/psi')
>>> print('Reservoir Water Viscosity:', visw,'cP')
>>>
>>> fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(10,10))
>>> ax1.plot(df['Pressure (psia)'], df['Rs (mscf/stb)'])
>>> ax2.plot(df['Pressure (psia)'], df['Bo (rb/stb)'])
>>> ax3.plot(df['Pressure (psia)'], df['uo (cP)'])
>>> ax4.semilogy(df['Pressure (psia)'], df['Co (1/psi)'])
>>>
>>> ax1.plot([pb], [rsb], 'o', c='r')
>>> ax2.plot([pb], [bob], 'o', c='r')
>>> ax3.plot([pb], [uob], 'o', c='r')
>>> ax4.plot([pb], [cob], 'o', c='r')
>>>
>>> if usat_flag:
>>> if no_pb == False:
>>> for i in range(len(usat_bo)):
>>> ax2.plot(usat_p[i], usat_bo[i], c='k')
>>> ax3.plot(usat_p[i], usat_uo[i], c='k')
>>>
>>> fig.suptitle('Black Oil Properties')
>>> ..
>>> ..
>>> plt.show()
Pb (psia): 4500
Bob (rb/stb): 1.6072798403441817
Rsb (mscf/stb): 1.2863705330979234
Rsb Scaling Required: 0.9713981737449556
Visob (cP): 0.3422139569449832
Cob (1/psi): 5.711273668114706e-05
Stock Tank Oil Density: 52.05522123893805 lb/cuft
Stock Tank Gas Density: 0.052025361717109773 lb/cuft
Reservoir Water Density: 61.40223160167964 lb/cuft
Reservoir Water Compressibility: 2.930237693350768e-06 1/psi
Reservoir Water Viscosity: 0.3640686136171888 cP
pyrestoolbox is maintained by Mark W. Burgoyne (https://github.com/mwburgoyne).