Minimum Miscibility Pressure of Gas Injection in Unconventional Reservoirs

2021 ◽  
Author(s):  
Gang Yang
Keyword(s):  
Phase Behavior ◽  
Gas Injection ◽  
Fluid Phase ◽  
Unconventional Reservoirs ◽  
Unconventional Reservoir ◽  
Minimum Miscibility Pressure ◽  
Confined Fluid ◽  
Miscible Gas Injection ◽  
Fluid Characterization ◽  
The Impact

Abstract Unconvnetional reservoirs are predominantly consisted of nanoscale pores. The strong confinement effect within nanopores imposes significant deviations to the confined fluid phase behavior. Minimum miscibility pressure (MMP) in unconventional reservoirs, as a parameter highly related to the phase behavior of confined fluids, is inevitably affected by the nanoscale confinement. The objective of this work is to investigate the impact of nanoscale confinement on MMP of unconventional reservoir fluids and to recognize a reliable theoretical approach to determine the MMP values in unconventional reservoirs. A modified Peng-Robinson equation of state (PR EOS) applicable for confined fluid characterization is applied to perform the EOS simulation of the vanishing interfacial tension (VIT) experiments. The MMP of a binary mixture at bulk and 50 nm are obtained via the VIT simulation. Meanwhile, the multiple mixing cell (MMC) algorithm coupled with the modified PR EOS is applied to compute the MMP for the same binary system. Comparison of the calculated results to the experimental values recognize that the MMC approach has higher accuracy in determining the MMP of confined fluid systems. Moreover, this approach is then applied to predict the MMP values of both Bakken and Eagle Ford oil at different pore sizes with various injected gases. Results demonstrate that the nanoscale confinement causes drastic suppression to the MMP of unconventional reservoir fluids and the suppression rate increases with decreasing pore size. The drastic suppression of MMP is highly favorable for the miscible gas injection EOR in unconventional reservoirs.

Minimum Miscibility Pressure of CO2/Hydrocarbon Systems Within Nanopores

2021 ◽  
Author(s):  
Gang Yang ◽  
Xiaoli Li
Keyword(s):  
Oil Recovery ◽  
Gas Injection ◽  
Unconventional Reservoirs ◽  
Minimum Miscibility Pressure ◽  
Theoretical Approaches ◽  
Fluid Systems ◽  
Confined Fluid ◽  
Miscible Gas Injection ◽  
Fluid Characterization ◽  
The Impact

Abstract Minimum miscibility pressure (MMP), as a key parameter for the miscible gas injection enhanced oil recovery (EOR) in unconventional reservoirs, is affected by the dominance of nanoscale pores. The objective of this work is to investigate the impact of nanoscale confinement on MMP of CO2/hydrocarbon systems and to compare the accuracy of different theoretical approaches in calculating MMP of confined fluid systems. A modified PR EOS applicable for confined fluid characterization is applied to perform the EOS simulation of the vanishing interfacial tension (VIT) experiments. The MMP of multiple CO2/hydrocarbon systems at different pore sizes are obtained via the VIT simulations. Meanwhile, the multiple mixing cell (MMC) algorithm coupled with the same modified PR EOS is applied to compute the MMP for the same fluid systems. Comparison of these results to the experimental values recognize that the MMC approach has higher accuracy in determining the MMP of confined fluid systems. Moreover, nanoscale confinement results in the drastic suppression of MMP and the suppression rate increases with decreasing pore size. The drastic suppression of MMP is highly favorable for the miscible gas injection EOR in unconventional reservoirs.

Prediction of CO2 Minimum Miscibility Pressure Using an Augmented Machine-Learning-Based Model

SPE Journal ◽  
2021 ◽  
pp. 1-13
Author(s):  
Utkarsh Sinha ◽  
Birol Dindoruk ◽  
Mohamed Soliman
Keyword(s):  
Machine Learning ◽  
Phase Behavior ◽  
Hybrid Method ◽  
Gas Injection ◽  
Supervised Machine Learning ◽  
Design Parameters ◽  
Support Vector ◽  
Hydrocarbon Gases ◽  
Minimum Miscibility Pressure ◽  
Analytical Correlation

Summary Minimum miscibility pressure (MMP) is one of the key design parameters for gas injection projects. It is a physical parameter that is a measure of local displacement efficiency while subject to some constraints due to its definition. Also, the MMP value is used to tune compositional models along with proper fluid description constrained with other available basic phase behavior data, such as bubble point pressure and volumetric properties. In general, carbon dioxide (CO2) and hydrocarbon gases are the most common gases used for (or screened for) gas injection processes, and because of recent focus, they are used to screen for the coupling of CO2-sequestration and CO2-enhanced oil recovery (EOR) projects. Because the CO2/oil phase behavior is quite different than the hydrocarbon gas/oil phase behavior, researchers developed specialized correlations for CO2 or CO2-rich streams. Therefore, there is a need for a tool with expanded range capabilities for the estimation of MMP for CO2 gas streams. The only known and widely accepted measurement technique for MMP that is coherent with its formal definition is the use of a slimtube apparatus. However, the use of slimtube restricts the amount of data available, even though there are other alternative techniques presented over the last three decades, which all have various limitations (Dindoruk et al. 2021). Due to some of the complexities highlighted in Dindoruk et al. (2021) and time and resource requirements, there have been a number of correlations developed in the literature using mostly classical regression techniques with relatively sparse data using various combinations of limited input data (Cronquist 1978; Lee 1979; Yellig and Metcalfe 1980; Alston et al. 1985; Glaso 1985; Jaubert et al. 1998; Emera and Sarma 2005; Yuan et al. 2005; Ahmadi et al. 2010; Ahmadi and Johns 2011). In this paper, we present two separate approaches for the calculation of the MMP of an oil for CO2 injection: analytical correlation in which the correlation coefficients were tuned using linear support vector machines (SVMs) (Press et al. 2007; MathWorks 2020; RDocumentation 2020b; Cortes and Vapnik 1995) and using a hybrid method (i.e., superlearner model), which consists of the combination of random forest (RF) regression (Breiman 2001) and the proposed analytical correlation. Both models take the compositional analysis of oils up to heptane plus fraction, molecular weight of oil, and the reservoir temperature as input parameters. Based on statistical and data analysis techniques in combination with the help of corresponding crossplots, we showed that the performance of the final proposed method (hybrid method) is superior to all the leading correlations (Cronquist 1978; Lee 1979; Yellig and Metcalfe 1980; Alston et al. 1985; Glaso 1985; Emera and Sarma 2005; Yuan et al. 2005) and supervised machine-learning (Metcalfe 1982) methods considered in the literature (Altman 1992; Chambers and Hastie 1992; Chapelle and Vapnik 2000; Breiman 2001; Press et al. 2007; MathWorks 2020). The proposed model works for the widest spectrum of MMPs from 1,000 to 4,900 psia, which covers the entire range of oils within the scope of CO2 EOR based on the widely used screening criteria (Taber et al. 1997a, 1997b).

Fluid-Phase Behaviour for a Miscible-Gas-Injection EOR Project in a Giant Offshore Oil Field With Large Compositional Variations

2008 ◽  
Author(s):  
Niels Lindeloff ◽  
Kristian Mogensen ◽  
Paul Peter van Lingen ◽  
Son Huu Do ◽  
Soren Frank ◽  
...  
Keyword(s):  
Gas Injection ◽  
Fluid Phase ◽  
Oil Field ◽  
Phase Behaviour ◽  
Miscible Gas Injection ◽  
Compositional Variations ◽  
Offshore Oil

Rapid Evaluation of the Impact of Heterogeneity on Miscible Gas Injection

1993 ◽  
Author(s):  
M.J. King ◽  
M.J. Blunt ◽  
M.M. Mansfield ◽  
M.A. Christie
Keyword(s):  
Gas Injection ◽  
Rapid Evaluation ◽  
Miscible Gas Injection ◽  
The Impact

Phase Behavior and Minimum Miscibility Pressure in Nanopores

2014 ◽  
Vol 17 (03) ◽  
pp. 396-403 ◽  
Author(s):  
Tadesse Weldu Teklu ◽  
Najeeb Alharthy ◽  
Hossein Kazemi ◽  
Xiaolong Yin ◽  
Ramona M. Graves ◽  
...  
Keyword(s):  
Phase Behavior ◽  
Electrostatic Interactions ◽  
Structural Changes ◽  
Unconventional Reservoirs ◽  
Gas Oil ◽  
Confined Space ◽  
Liquid Equilibrium ◽  
Minimum Miscibility Pressure ◽  
Multiple Mixing ◽  
Carbon Dioxide Co2

Summary Numerous studies indicate that the pressure/volume/temperature (PVT) phase behavior of fluids in large pores (designated “unconfined” space) deviates from phase behavior in nanopores (designated “confined” space). The deviation in confined space has been attributed to the increase in capillary force, electrostatic interactions, van der Waals forces, and fluid structural changes. In this paper, conventional vapor/liquid equilibrium (VLE) calculations are modified to account for the capillary pressure and the critical-pressure and -temperature shifts in nanopores. The modified VLE is used to study the phase behavior of reservoir fluids in unconventional reservoirs. The multiple-mixing-cell (MMC) algorithm and the modified VLE procedure were used to determine the minimal miscibility pressure (MMP) of a synthetic oil and Bakken oil with carbon dioxide (CO2) and mixtures of CO2 and methane gas. We show that the bubblepoint pressure, gas/oil interfacial tension (IFT), and MMP are decreased with confinement (nanopores), whereas the upper dewpoint pressure increases and the lower dewpoint pressure decreases.

scholarly journals Prediction of asphaltene precipitation upon injection of various gases at near-wellbore conditions: A simulation study using PC-SAFT EoS

2019 ◽  
Vol 74 ◽  
pp. 63 ◽  
Author(s):  
Saba Mahmoudvand ◽  
Behnam Shahsavani ◽  
Rafat Parsaei ◽  
Mohammad Reza Malayeri
Keyword(s):  
Phase Behavior ◽  
Oil Recovery ◽  
Gas Injection ◽  
Drop Out ◽  
Maximum Point ◽  
Asphaltene Precipitation ◽  
Wide Range ◽  
Tertiary Oil Recovery ◽  
The Impact ◽  
Oil Type

The depletion of oil reservoirs and increased global oil demand have given impetus to employ various secondary and tertiary oil recovery methods. Gas injection is widely used in both secondary and tertiary modes, though the major problem associated with this process is the precipitation and deposition of asphaltene, particularly at near-wellbore conditions. In-depth knowledge of asphaltene phase behavior is therefore essential for the prediction of asphaltene precipitation. Previous studies reported the impact of gas injection on asphaltene phase behavior, but the knowledge of precipitation of asphaltene as a function of different mole fractions of injected gas is also imperative. In this study, the thermodynamic model of PC-SAFT EoS is used to discern the phase equilibrium of asphaltene by analyzing the asphaltene drop-out curve during gas injection. Asphaltene drop-out curves of two different live oil samples are analyzed by injecting CO2, CH4, and N2 gases at different mole percentages and temperatures. The results revealed that PC-SAFT EoS can serve as a reliable tool for estimating bubble pressure and asphaltene onset pressure for a wide range of temperatures, pressures, and compositions. The simulation results for the injection of CO2, CH4, and N2 also showed that CO2 gas gives minimum asphaltene precipitation. It reduces the size of the drop-out curve or moves it toward higher pressures. CH4 and N2 expand the drop-out curve by raising the upper onset point. CH4 increases the maximum point of the drop-out curve for two types of oil studied (A and B) at two different temperatures. N2 raises the maximum point of oil type “A” by approximately 57% at 395 K, while it has no effect on the maximum point of oil type “B”. In addition, reducing the temperature resulted in either decrease or increase of asphaltene solubility, demonstrating that the impact of temperature on asphaltene precipitation is closely related to the composition of the crude.

Sign in / Sign up

Export Citation Format

Share Document