Open access peer-reviewed chapter

Carbon Dioxide-Oil Minimum Miscibility Pressure Methods Overview

Written By

Eman Mohamed Ibrahim Mansour

Submitted: 22 March 2022 Reviewed: 19 July 2022 Published: 02 November 2022

DOI: 10.5772/intechopen.106637

From the Edited Volume

Enhanced Oil Recovery - Selected Topics

Edited by Badie I. Morsi and Hseen O. Baled

Chapter metrics overview

246 Chapter Downloads

View Full Metrics

Abstract

One of the essential parameters in carbon dioxide (CO2) miscible flooding is the minimum miscibility pressure (MMP). Minimum miscibility pressure (MMP) is defined as the lowest pressure at which recovery of oil is (90–92%) at injection (1.2 PV) of carbon dioxide (CO2). The injected gas and oil become a multi-contact miscible at a fixed temperature. Before any field trial, minimum miscibility pressure (MMP) must be determined. This parameter must be determined before any field trial because any engineer needs a suitable plan to develop an injection and surface facilities environment. Estimation of reliable (MMP) maybe by traditional laboratory techniques, but it is very costly and time-consuming. Also, it can rely on various literature (MMP) empirical correlations, but this is not a good strategy because each minimum miscibility pressure (MMP) correlation relates to a unique formation condition.

Keywords

  • enhanced oil recovery
  • CO2 injection
  • minimum miscibility pressure (MMP)
  • slim-tube test
  • computational method

1. Introduction

Miscible gas injection could enhance light oil reservoirs’ enhanced recovery (EOR) process. Recycling CO2 into oil formation reservoirs allows good gas storage in subsurface formations; consequently, the oil recovery will improve [1]. Miscible flooding project design mostly depends on his success in the minimum miscibility pressure correct determination [2]. The minimum flooding pressure reached the miscibility point, which is the maximum oil recovery achieved (90–92%) at the lowest pressure during injection (1.2 PV) of carbon dioxide (CO2) [3]. Incremental oil recovery is negligible at a higher (CO2) flooding project pressure than the MMP. In addition, recovery will sharply decrease at a pressure lower than MMP [4].

1.1 Importance of the minimum miscibility pressure (MMP)

  • Minimum miscibility pressure (MMP) is an essential parameter in any design project of gas injection. At the minimum miscibility pressure (MMP), all oil was recovered within a porous medium of (CO2).

  • When oil and gas are miscible, displacement efficiency will be 100% [5].

1.2 Factors affecting minimum miscibility pressure (MMP)

oil composition, reservoir temperature, and (CO2) purity effect on minimum miscibility pressure (MMP), where [6]:

  • The minimum miscibility pressure (MMP) does not change as methane is present in the reservoir.

  • As the oil gravity of oil becomes heavier as minimum miscibility pressure (MMP) increases. So, fields with heavy API are not suitable for carbon dioxide (CO2)-EOR injection.

  • As reservoir temperature is high, minimum miscibility pressure (MMP) is higher.

  • Minimum miscibility pressure (MMP) is inversely related to the reservoir oil’s C5 to C30 summation.

  • Minimum miscibility pressure (MMP) does not require methane to propane presence.

Advertisement

2. Methods of estimating (MMP)

There are several experimental, equation of state, and empirical equations for estimating (MMP).

2.1 Experimental methods for estimating minimum miscibility pressure (MMP)

Minimum miscibility pressure (MMP) estimates through several testing methods: slim-tube experiments, rising bubble experiments, multiple-contact experiments, and vanishing interfacial tension experiments [7].

2.1.1 Slim-tube experiment

These experiments are widely accepted experimental methods for estimating minimum miscibility pressure (MMP) because they can repeat the interaction of gas and oil in a one-dimensional porous medium [8]. As a result, that slim-tube experiment can replicate oil and gas interaction in a one-dimensional porous medium. It remains the most reliable method of estimating minimum miscibility pressure (MMP)[9]. The slim-tube basic test is the small-diameter tube packed with an unconsolidated porous medium [3]. It is an idealized medium for carbon dioxide (CO2) and oil to develop dynamic miscibility [10]. The slim-tube experiment comes close to a one-dimensional displacement due to this large length-to-diameter ratio, thus the isolating of phase behavior affects the efficiency of removal [11]. A slim-tube is a long thin stainless steel-tube that is fifteen-meter, packed with sand or glass beads (commonly, sand packing is 160 to 200 mesh) [12]. The slim schematic-tube appears in Figures 1 and 2.

Figure 1.

Schematic diagram of the slim-tube test setup.

Figure 2.

Actual slim-tube test system.

The slim-tube is saturated with at least two PV light oils at the reservoir temperature. Then the system pressurized gradually to the operating pressure in the backpressure regulator’s presence [3]. It is pressure generally kept constant by a backpressure regulator. Upstream pressure changed with the backpressure regulator as it set [13]. To avoid pressure from one side of the diaphragm in the backpressure regulator from becoming significantly higher than the pressure on the other side and damaging the diaphragm [14]. It was required to pressurize the tube system gradually. Just the required pressure is reached, and the system will be equilibrated under this pressure. The carbon dioxide (CO2) pressure pump was adjusted a little above the pressure of the backpressure regulator. The carbon dioxide (CO2) flow rate is 4 to 8cm3/ hr. at a constant rate [15]. The experiment terminated when at least 1.2 PV of carbon dioxide (CO2) was injected. Effluent flashed to atmospheric conditions, where the flow meter collects separator gas and the separated oil collects in a graduate cylinder. The pump’s initial and final volumes of carbon dioxide (CO2) were recorded to ensure the pump was not empty. The system was depressurized by venting loading gas gradually. After that, use two PV of methylene chloride in the slim-tube to remove residual oil and be ready for the next experiment [16]. The bubble point pressure of the formation oil must measure before the slim-tube experiment. On these results, the slim-tube test can repeat numerous times at different pressures greater than bubble-point pressure. The injected gas pour volume and oil recovery are recorded in each experiment. After indirect 1.2 gas pore volume, the minimum miscibility pressure (MMP) was observed from recovery data.

As shown in Figure 3 [17].

Figure 3.

Minimum miscibility pressure for (CO2).

2.1.2 Rising bubble experiment

Christiansen and Haines [18] are the first to introduce the rising bubble experiment as a fast option to the slim-tube test, where it can be measured within one hour. This method consists of eight inches high-pressure crystal clear-tube long packed with oil and set at a definite pressure and temperature. Gas introduces through the way of a needle at the tube bottom, forming a bubble and rising through the column [19]. This method can visually observe the miscibility between a gas bubble and an oil. The disadvantage of this method is significant limitations as not expensive and fast compared to the slim-tube method. This method is unreliable in predicting minimum miscibility pressure (MMP) in condensing drive and condensing/vaporizing gas drive (Mansour and Ragab, 2021) (Figure 4) [18].

Figure 4.

Rising bubble experiment.

2.1.3 Multiple-contact experiment

Multiple-contact experiments can detect minimum miscibility pressure (MMP) definite conditions. A multiple-contact test aims to study the gas and oil injection phase behavior [20]. The multiple-contact test is always on contacts between oil and gas. Oil and gas are mixed in a pressure-volume-temperature (PVT) cell [21]. A single PVT cell uses to make repeated contacts between oil and gas forward or backward. In a forward contract, the equilibrium gas retains after each contact.

In contrast, the equilibrium oil replaces with fresh oil—consequently, the equilibrium gas from the previous stage contacts fresh oil at each stage [22]. Equilibrium oil is retained in a backward connection, and the gas is replaced with new injection gas. The contacts are repeated till no change in the phase composition. These tests are repeated at different pressures until the repeated contacts result in a single phase (as shown visually from the cell window) [23]. The disadvantages of this method are that it can provide useful minimum miscibility pressure (MMP) and phase behavior data for gas floods that are purely condensing or vaporizing. Still, most gas flooding is condensing/evaporating drives, meaning that they have both condensing and drive features, but this makes the results of such experiments less accurate [24].

2.1.4 Vanishing interfacial tension experiment

Rao (1997) planned the vanishing interfacial tension (VIT) test as a technique for defining minimum miscibility pressure (MMP). This method involves a high temperature and pressure cell occupied with the CO2 injection. A drop of crude oil (about 10% of the cell volume) is introduced through a capillary-tube [25]. It measures the interfacial tension (IFT) between oil and CO2 injected gas at numerous pressures and a specific temperature. The analyzing data were determined by the shape of the hanging oil drop and the oil and gas densities. The pressure increases by pumping more gas into the cell, and the interfacial tension (IFT) measurement is repeated several times [26]. The minimum miscibility pressure (MMP) is approached by extrapolating the interfacial tension (IFT) plot versus pressures. The disadvantage of this test is that after extending their analysis to a multi-component mixture, and concluding that vanishing interfacial tension (VIT) experiments may not be a dependable method of determining minimum miscibility pressure (MMP) [27]. Among these, all experimental methods, the only known experiment of minimum miscibility pressure (MMP) between oil and injection gas is the slim-tube testing.

The experimental methods for estimating minimum miscibility pressure (MMP) have much money and time-overwhelming disadvantages. But these tests can provide us with useful phase behavior data that can be used to estimate and confirm the reliability of a computational minimum miscibility pressure (MMP) [26].

2.2 Computational method of estimating minimum miscibility pressure (MMP)

Empirical correlations for approximating minimum miscibility pressure (MMP) provide fast and cheap alternatives to experimental methods. It is beneficial for quick screening reservoirs for potential carbon dioxide (CO2) flooding. Various empirical correlations for estimating minimum miscibility pressure (MMP) have been calculated from regression data analysis of slimtube data [28]. Generally, empirical correlations for the predicting of minimum miscibility pressure (MMP) reservoir temperature, the (C2-C6) content of reservoir fluid, and API (oil gravity) as input parameters [29]. This study includes popular minimum miscibility pressure (MMP) empirical correlations reported in the petroleum literature are included in this study. It can be used as a practical guide for the application of different reservoir oils, such as Cronquist [30], Lee [31], Yelling and Metcalfe [32], Alston et al. [33], Emera and Sarma [34], Liao et al. [35], and Mansour et al. [36].

2.2.1 Cronquist empirical correlation

Cronquist [30] empirical correlation is based on the reservoir temperature, pentane plus (C5+) molecular weight, and volatile oil fraction as (CH4 and N2) for minimum miscibility pressure (MMP) estimation as shown in Eq. (1) [30].

MMP=0.11027+1.8TR+32yE1

where Y = 0.744206 + 0.0011038 × MWTC5++0.0015279 × Vol.

The experimental data range tested used in this study is as the following:

  • The oil gravity (API) ranged from 23.7 to 44 8.

  • The temperature ranged from 21.67 to 120.8°C.

  • The experimental (MMP) ranged from 7.4 to34.5 MPa.

2.2.2 Lee empirical correlation

Lee [31] predicted a model to estimate minimum miscibility pressure (MMP) using reservoir temperature as input data only by considering carbon dioxide (CO2) vapor pressure, as shown in Eq.(2). If any reservoir oil’s bubble point pressure (BP) is more than minimum miscibility pressure (MMP), the bubble point pressure (BP) takes as minimum miscibility pressure [31]. The bubble point can be detected from the constant mass study test [37].

MMP=7.3924×10bE2

where b = 2.772−(1519/(492 + 1.8TR)).

2.2.3 Yelling and metcalfe empirical correlation

Yelling and Metcafe (1980) proposed an empirical correlation for estimating minimum miscibility pressure (MMP) at different reservoir temperatures by using the equation Eq. (3). This correlation is not dependent on oil composition and is based only on reservoir conditions. The empirical correlation of minimum miscibility pressure (MMP) is varied from 15 to 19 Mpa approximately [32].

MMP=12.6472+0.015531×1.8TR+32+1.24192×104×1.8TR+322716.9427/1.8TR+32E3

The limitation of reservoir temperature data used 35.8 °C < TR < 88.9°C. Suppose the minimum miscibility pressure (MMP) is less than the bubble point pressure (BP) for any sample. The bubble point pressure is taken as the minimum miscibility pressure (MMP) determined by the constant mass study test [38].

2.2.4 Alstonetal et al. empirical correlation

Alston et al. [33] presented an empirical correlation for minimum miscibility pressure (MMP) caused by gas solution in reservoir fluids. The minimum miscibility pressure empirical correlation that is in Eq.(4) was predicted based on carbon dioxide (CO2) composition stream, light oil fraction (CH4 + N2), reservoir temperature, pentane plus (C5+) molecular weight, and Intermediate oil fraction (C2 to C4, H2S, and CO2). So, they proposed an impurity factor for predicting minimum miscibility pressure empirical correlation (MMP) by contaminated or en-riched carbon dioxide (CO2) stream (Alston, Kokolis, and James, 1985).

pCO2=1.251071.8t4601.06MWC5+1.78xvolxint0.136E4

Also, suppose the minimum miscibility pressure (MMP) of volatile reservoir oil is fewer than the saturation pressure (BP). In that case, the saturation pressure act as the minimum miscibility pressure (MMP).

2.2.5 Emera and Sarma empirical correlation

Emera and Sarma (2005) presented genetic logarithm (GA)-depending on correlation to predicate minimum miscibility pressure (MMP) as shown in Eq.(5). The input data parameters that are based on this correlation are (C1 and N2) volatiles ratio, reservoir temperature, intermediates components (C2–C4, H2S, and CO2), pentane plus (C5+) molecular weight, and (C2–C4, H2S, and CO2) [34].

MMPpure=0.003T0.544MWC5+1.006YVOLYINT0.143E5

2.2.6 Liao et al. empirical correlation

Liao et al. [35] offered minimum miscibility pressure (MMP) empirical correlation depending on (CH4 + N2) oil fraction, (C2 to C4, H2S, and CO2) intermediate oil fraction, pentane plus (C5+) molecular weight, and reservoir temperature, as shown in Eq.(6).

MMPpure=0.003T0.544MWC5+1.006YVOLYINT0.143E6

This minimum miscibility pressure (MMP) empirical correlation was appropriate for low permeability reservoirs. The characteristics of oil reservoir low permeability must be (vol/yint>1), where the experimental and published data were used in this correlation [35].

2.2.7 Mansour et al. empirical correlation

Mansour et al. [36] proposed a new method for predicting (MMP) of a multi-component volatile oil reservoir. This model used twenty-live crude oil samples to correlate this unique formula with new constants. The data range for using this equation API from 40.5 to 26 and in range temperature from 160 to 246°F. The developed (MMP) equation gives good results to reduce the previous correlations errors, where (Er) was found to be 0.627%, (Ea) 2.4%, (Er) 0.627%, (R2) 94.82% (S), and 2.7%. Consequently, this new model has better accuracy than previous literature correlations, as shown in Eq. (7) [36].

LnMMP=β0+β1LnT+β2LnMWTC5++β3yVol/yintE7

where β0, β1, β2, β3 are the coefficient values and have the following values 11.222, −0.355, −0.2069, and 0.039, respectively.

Advertisement

Nomenclature

API

American Petroleum Institute, degree

BP

Bubble point(saturation pressure), psi

CO2

Carbon Dioxide

CMD

Constant Mass Depletion

Ea

Average Absolute percent relative error

Er

Average percent relative error %

PV

Pore Volume

MMP

Minimum Miscibility Pressure

MWC5+

Molecular weight of pentane plus

R2

Correlation Coefficient

S

Standard deviation

T

Reservoir temperature

y

Mole percentage

yi

Mole fraction of I component

Advertisement

Subscript

Int

Intermediate

Psi

Pound/square inch

Res

Reservoir

vol

Volatile

References

  1. 1. Rezaei M, Eftekhari M, Schaffie M, Ranjbar M. A CO2-oil minimum miscibility pressure model based on multi-gene genetic programming. Energy Exploration & Exploitation. 2013;31:607-622
  2. 2. Ragab A, Mansour EM. Investigating the impact of PVT analysis errors on Material balance calculations for oil reservoirs. Petroleum & Coal. 2020
  3. 3. Mansour EM, Al-Sabagh AM, Desouky SM, Zawawy FM, Ramzi MR. Experimental approach of minimum miscibility pressure for CO2 miscible flooding: Application to Egyptian oil fields. International Journal of New Technology and Research. 2016;2:263507
  4. 4. Li L, Zhang Y, Sheng JJ. Effect of the injection pressure on enhancing oil recovery in shale cores during the CO2 huff-n-puff process when it is above and below the minimum miscibility pressure. Energy & Fuels. 2017;31:3856-3867
  5. 5. Perera MS, Anne RP, Gamage TD, Rathnaweera AS, Ranathunga AK, Choi X. A review of CO2-enhanced oil recovery with a simulated sensitivity analysis. Energies. 2016;9:481
  6. 6. Abdullah N, Hasan N. Effects of miscible CO2 injection on production recovery. Journal of Petroleum Exploration and Production Technology. 2021;11:3543-3557
  7. 7. Golkari A, Riazi M. Experimental investigation of miscibility conditions of dead and live asphaltenic crude oil–CO2 systems. Journal of Petroleum Exploration and Production Technology. 2017;7:597-609
  8. 8. Ahmad W, Vakili-Nezhaad G, Al-Bemani AS, Al-Wahaibi Y. Experimental determination of minimum miscibility pressure. Procedia Engineering. 2016;148:1191-1198
  9. 9. Saini D, Rao DN. Experimental determination of minimum miscibility pressure (MMP) by gas/oil IFT measurements for a gas injection EOR project. In: SPE Western Regional Meeting. 2010
  10. 10. Mansour EM, Al-Sabagh AM, Desouky SM, Zawawy FM, Ramzi M. A laboratory investigation of carbon dioxide-enhanced oil recovery by focusing on CO2-oil physical properties. Egyptian Journal of Petroleum. 2019;28:21-26
  11. 11. Abdurrahman M, Bae W, Permadi AK. Determination and evaluation of minimum miscibility pressure using various methods: Experimental, visual observation, and simulation. Oil & Gas Science and Technology–Revue d’IFP Energies nouvelles. 2019;74:55
  12. 12. Bui LH. Near Miscible CO2 Application to Improve Oil Recovery. University of Kansas; 2010
  13. 13. Ragab A, Mansour EM. Enhanced Oil Recovery: Chemical Flooding. London, UK: IntechOpen; 2021
  14. 14. Cao M, Yongan G. Oil recovery mechanisms and asphaltene precipitation phenomenon in immiscible and miscible CO2 flooding processes. Fuel. 2013;109:157-166
  15. 15. Moradi S, Dabiri M, Dabir B, Rashtchian D, Emadi MA. Investigation of asphaltene precipitation in miscible gas injection processes: Experimental study and modeling. Brazilian Journal of Chemical Engineering. 2012;29:665-676
  16. 16. Bahadori A. Fundamentals of Enhanced Oil and Gas Recovery from Conventional and Unconventional Reservoirs. Gulf Professional Publishing; 2018
  17. 17. Oyinloye O, Al Darmaki N, Al Zarooni M, Boukadi F, Nantongo H. Estimation of minimum miscibility pressure for flue gas injection using soft experimentations. Natural Resources. 2021;12:363-380
  18. 18. Christiansen RL, Haines HK. Rapid measurement of minimum miscibility pressure with the rising-bubble apparatus. SPE Reservoir Engineering. 1987;2:523-527
  19. 19. Zhou D, Orr FM. An analysis of rising bubble experiments to determine minimum miscibility pressures. In: SPE Annual Technical Conference and Exhibition. 1995
  20. 20. Yang FL, Yu P, Xue Z. Multiple-mixing-cell model for calculation of minimum miscibility pressure controlled by tie-line length. In: Geofluids. 2020
  21. 21. Amao AM, Siddiqui S, Menouar H, Herd BL. A new look at the minimum miscibility pressure (MMP) determination from slimtube measurements. In: SPE Improved Oil Recovery Symposium. 2012
  22. 22. Sun H, Li H. A modified cell-to-cell simulation model to determine the minimum miscibility pressure in tight/shale formations. Oil & Gas Science and Technology–Revue d’IFP Energies Nouvelles. 2021;76:48
  23. 23. Zhao G-B, Adidharma H, Towler B, Radosz M. Using a multiple-mixing-cell model to study minimum miscibility pressure controlled by thermodynamic equilibrium tie lines. Industrial & Engineering Chemistry Research. 2006;45:7913-7923
  24. 24. Zhang K, Jia N, Zeng F, Luo P. A new diminishing interface method for determining the minimum miscibility pressures of light oil–CO2 systems in bulk phase and nanopores. Energy & Fuels. 2017;31:12021-12034
  25. 25. Orr J, Franklin M, Jessen K. An analysis of the vanishing interfacial tension technique for determination of minimum miscibility pressure. Fluid Phase Equilibria. 2007;255:99-109
  26. 26. Teklu TW, Alharthy N, Kazemi H, Yin X, Graves RM. Vanishing interfacial tension algorithm for MMP determination in unconventional reservoirs. In: SPE Western North American and Rocky Mountain Joint Meeting. 2014
  27. 27. Seyyedsar SM, Assareh M, Ghotbi C. Minimum miscibility pressure calculation: A comparison between multi-contact test and compositional simulation. In: CIVILICA, 14th Iranian National Chemical Engineering congress. Tehran; 2012
  28. 28. Delforouz FB, Movaghar MRK, Shariaty S. New empirical correlations for predicting minimum miscibility pressure (MMP) during CO2 injection; implementing the group method of data handling (GMDH) algorithm and Pitzer’s acentric factor. Oil & Gas Science and Technology–Revue d’IFP Energies Nouvelles. 2019;74:64
  29. 29. Alomair O, Malallah A, Elsharkawy A, Iqbal M. Predicting CO2 minimum miscibility pressure (MMP) using alternating conditional expectation (ACE) algorithm. Oil & Gas Science and Technology–Revue d’IFP Energies nouvelles. 2015;70:967-982
  30. 30. Cronquist C. Carbon dioxide dynamics displacement with light reservoir oil. In: DOE Annual Symposium. Tulsa; 1978
  31. 31. Lee IJ. Effectiveness of Carbon Dioxide Displacement under Miscible and Immiscible Conditions. 1979
  32. 32. Yellig WF, Metcalfe RS. Determination and prediction of CO2 minimum miscibility pressures (includes associated paper 8876). Journal of Petroleum Technology. 1980;32:160-168
  33. 33. Alston RB, Kokolis GP, James CF. CO2 minimum miscibility pressure: A correlation for impure CO2 streams and live oil systems. Society of Petroleum Engineers Journal. 1985;25:268-274
  34. 34. Emera MK, Sarma HK. Genetic Algorithm (GA)-based correlations offer more reliable prediction of CO2-oil physical properties. In: Canadian International Petroleum Conference. Calgary, Alberta, Canada; 2005
  35. 35. Liao C, Liao X, Chen J, Ye H, Chen X, Wang H. Correlations of minimum miscibility pressure for pure and impure CO2 in low permeability oil reservoir. Journal of the Energy Institute. 2014;87:208-214
  36. 36. Mansour EM, Al-Sabagh AM, Desouky SM, Zawawy FM, Ramzi M. A new estimating method of minimum miscibility pressure as a key parameter in designing CO2 gas injection process. Egyptian Journal of Petroleum. 2018;27:801-810
  37. 37. Mansour EM, Desouky SM, Batanoni MH, Mahmoud MR, Farag AB, El-Dars FS. Modification proposed for SRK equation of state. Oil & Gas Journal. 2012;110:78-78
  38. 38. Mansour EM, Farag AB, El-Dars FS, Desouky SM, Batanoni MH, Mahmoud MRM. Predicting PVT properties of Egyptian crude oils by a modified soave–Redlich–Kowng equation of state. Egyptian Journal of Petroleum. 2013;22:137-148

Written By

Eman Mohamed Ibrahim Mansour

Submitted: 22 March 2022 Reviewed: 19 July 2022 Published: 02 November 2022