Determination of relative permeability under simulated reservoir conditions

AIChE Journal ◽  
1956 ◽  
Vol 2 (1) ◽  
pp. 94-100 ◽  
Author(s):  
J. W. Wilson
Keyword(s):  
Relative Permeability ◽  
Reservoir Conditions

Determination of Gas-Condensate Relative Permeability on Whole Cores Under Reservoir Conditions

1986 ◽  
Vol 1 (01) ◽  
pp. 9-15 ◽  
Author(s):  
J.F. Gravier ◽  
P. Lemouzy ◽  
C. Barroux ◽  
A.F. Abed
Keyword(s):  
Relative Permeability ◽  
Gas Condensate ◽  
Reservoir Conditions

Machine Learning Predictive Models to Estimate the Minimum Miscibility Pressure of CO2-Oil System

2021 ◽  
Author(s):  
Abderraouf Chemmakh ◽  
Ahmed Merzoug ◽  
Habib Ouadi ◽  
Abdelhak Ladmia ◽  
Vamegh Rasouli
Keyword(s):  
Machine Learning ◽  
Predictive Models ◽  
Critical Parameters ◽  
Co2 Injection ◽  
Learning Models ◽  
Minimum Miscibility Pressure ◽  
Reservoir Conditions ◽  
Important Amount ◽  
Machine Learning Models

Abstract One of the most critical parameters of the CO2 injection (for EOR purposes) is the Minimum Miscibility Pressure MMP. The determination of this parameter is crucial for the success of the operation. Different experimental, analytical, and statistical technics are used to predict the MMP. Nevertheless, experimental technics are costly and tedious, while correlations are used for specific reservoir conditions. Based on that, the purpose of this paper is to build machine learning models aiming to predict the MMP efficiently and in broad-based reservoir conditions. Two ML models are proposed for both pure CO2 and non-pure CO2 injection. An important amount of data collected from literature is used in this work. The ANN and SVR-GA models have shown enhanced performance comparing to existing correlations in literature for both the pure and non-pure models, with a coefficient of R2 0.98, 0.93 and 0.96, 0.93 respectively, which confirms that the proposed models are reliable and ready to use.

scholarly journals Determination of gas-saturated oil density at reservoir conditions and development of quality control index of PVT laboratory report

2020 ◽  
Author(s):  
Muhammad Ali Al-Marhoun
Keyword(s):  
Quality Control ◽  
Production Engineering ◽  
Accuracy Estimate ◽  
Bubble Point ◽  
Laboratory Report ◽  
Reservoir Conditions ◽  
Data Yield ◽  
Oil Density

AbstractThe oil density at the bubble point is an important thermodynamic property required in reservoir simulation and production engineering. A higher-accuracy estimate of this property would improve the accuracy of reservoir and production engineering calculations. The bubble point oil density is obtained either from separator tests of reservoir fluids or from differential gas liberation tests. A new procedure utilizing separator and differential tests is proposed whereby the experimental data yield a unique value with high accuracy for the bubble point oil density. A consistent correction of other PVT properties, which are influenced by the bubble point oil density, is required to reflect the unique density value. A quantitative quality control index is defined to measure the quality of PVT laboratory reports. This is achieved by utilizing the unique property of the bubble point oil density, which is usually ignored.

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