Determination of Individual Diffusion Coefficients of Solvent/CO2 Mixture in Heavy Oil With Pressure-Decay Method

Li, Huazhou; Yang, Daoyong

doi:10.2118/176032-pa

Cited by 63 publications

(64 citation statements)

References 17 publications

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“…(4) The extraction effect of CO2 on oil is negligible. (The literature has reported that the extraction effect of CO2 on hydrocarbons that are heavier than C9 is tiny [56], and Figure 1 shows that it is reasonable to ignore extraction in this study.) (5) The convection flow caused by the density difference is ignored.…”

Section: Diffusion Model In Porous Mediamentioning

confidence: 88%

“…Thus, it is important to simplify the calculation process with the prerequisite of ensuring a high accuracy of calculation. In this study, several pseudo-components are introduced as substitutes for all hydrocarbon components in the oil sample, which can efficiently decrease the calculation load of PR EOS without influencing the computing accuracy [56][57][58]. Specifically, in this work, hydrocarbons with a single carbon number (SCN) are lumped into three pseudo-components, which have been tested as having a good result on the oil/CO 2 system [59,60].…”

Section: Peng-robinson Equation Of State (Pr Eos)mentioning

confidence: 99%

“…where zi, and are the mole friction of component i in the mixed oil, crude oil and kerosene samples, respectively, and n c and n k are the mole numbers of crude oil and kerosene, respectively. Figure 5 shows that the components lighter than C 9 account for a tiny part of those oil samples (<1%), which means that there is no obvious extraction effect during the mass transfer process [56,59,60]. The components of the CO 2 phase were analyzed by a GC method after the diffusion experiments, and no hydrocarbons were found in the CO 2 phase.…”

Section: Characterization Of the Oil Samplesmentioning

confidence: 99%

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The Effect of Oil Properties on the Supercritical CO2 Diffusion Coefficient under Tight Reservoir Conditions

Zhang

Qiao

et al. 2018

Energies

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In this paper, a generalized methodology has been developed to determine the diffusion coefficient of supercritical CO 2 in cores that are saturated with different oil samples, under reservoir conditions. In theory, a mathematical model that combines Fick's diffusion equation and the Peng-Robinson equation of state has been established to describe the mass transfer process. In experiments, the pressure decay method has been employed, and the CO 2 diffusion coefficient can be determined once the experimental data match the computational result of the theoretical model. Six oil samples with different compositions (oil samples A to F) are introduced in this study, and the results show that the supercritical CO 2 diffusion coefficient decreases gradually from oil samples A to F. The changing properties of oil can account for the decrease in the CO 2 diffusion coefficient in two aspects. First, the increasing viscosity of oil slows down the speed of the mass transfer process. Second, the increase in the proportion of heavy components in oil enlarges the mass transfer resistance. According to the results of this work, a lower viscosity and lighter components of oil can facilitate the mass transfer process.Energies 2018, 11, 1495 2 of 20 methods for the quantitative determination of the gas (N 2 , CO 2 , CH 4 [28], C 2 H 6 , C 3 H 8 , or a mixture gas) diffusion coefficient in crude oil. These methods include the pressure decay method, X-ray computer-assisted tomography (CAT) method, magnetic resonance imaging (MRI) method [29,30], dynamic drop volume analysis (DPDVA), and pore-scale network modeling method. All of these methods have advantages and flaws:Direct testing method. Hill and Lacey [31] tested the diffusion coefficient of CH 4 in isopentane with a constant pressure method in a Pressure-Volume-Temperature (PVT) system. Sigmund [32] also used this method to study the binary dense gas diffusion coefficients under reservoir conditions. Islas-Juarez et al. [33] set up an experimental device to determine the effective diffusion coefficient of N 2 in a sandpack model. They devised a special setting to sample crude oil that was dissolved with N 2 in their porous model. The samples were then analyzed by gas chromatograph, and the gas concentration in the oil phase was determined. The corresponding diffusion coefficient was determined by matching the mathematical diffusion model with the experimentally measured concentration curve. The main shortcomings of the direct testing method for the solvent diffusion coefficient are that it is both time and labor consuming, which results in an expensive process.Pressure decay method. Riazi [34] proposed a technique known as the pressure decay method based on the fact that the pressure of the gas phase in a closed diffusion cell decreases with the migration of the gas phase into the liquid phase. One of the remarkable features of this method is that it does not require costly component measurements. In the following two decades, many scholars adopted this method to test the diffus...

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Section: Diffusion Model In Porous Mediamentioning

confidence: 88%

Section: Peng-robinson Equation Of State (Pr Eos)mentioning

confidence: 99%

Section: Characterization Of the Oil Samplesmentioning

confidence: 99%

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The Effect of Oil Properties on the Supercritical CO2 Diffusion Coefficient under Tight Reservoir Conditions

Zhang

Qiao

et al. 2018

Energies

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“…Flue gas combined with n-hexane injection recovered more oil than steam alone (Li et al, 2017;Dong et al, 2015;Li and Yang, 2016). The surface tension between flue gas and heavy oil increases with temperature.…”

Section: Introductionmentioning

confidence: 99%

Multi-Component Thermal Fluid Injection Performance in Recovery of Heavy Oil Reservoirs

et al. 2021

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Field observations discern that the oil production rate decreases substantially and water cut increases rapidly with the increase of steam injection cycles. Compared with steam drive, the advantage of flue gas (also called multi-component thermal gas) co-injection with steam is that flue gas can increase the reservoir pressure and expand the heating chamber. In this paper, the flue gas generated by fuel burning in the field was injected with steam to improve heavy oil recovery. This technique was investigated in the large laboratory 3D model and implemented in the field as well. The huff-n-puff process efficiency by flue gas, steam, and flue gas–steam co-injection was compared in the experiments. The field practice also demonstrated that the addition of non-condensable gas in the steam huff-n-puff process recovered more oil than steam alone. The temperature profile in the wellbore with flue gas injection is higher than that with steam injection since the low thermal conductivity of N2 reduces the heat loss. With the increase of stimulation cycles, the incremental oil recovery by flue gas injection declines significantly.

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“…Recently, Li and Yang have developed a modified alpha function which combines the advantages of both the Soave-type alpha function and the logarithm-type alpha function by using a comprehensive database comprising 59 hydrocarbon and nonhydrocarbon compounds, including heavy alkanes up to n -tritetracontane ( n -C 43 H 88 ). This modified alpha function has been successfully used to more accurately predict the phase behavior of both pure substances and alkane solvent(s)–CO 2 –heavy oil systems by treating heavy oil as one pseudocomponent − and multiple pseudocomponents. − …”

Section: Introductionmentioning

confidence: 99%

Optimization of the Reduced Temperature Associated with Peng–Robinson Equation of State and Soave–Redlich–Kwong Equation of State To Improve Vapor Pressure Prediction for Heavy Hydrocarbon Compounds

Chen

Yang

2017

J. Chem. Eng. Data

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A pragmatic technique has been developed to optimize the reduced temperature for the acentric factor associated with the Peng−Robinson equation of state (PR-EOS) and the Soave−Redlich−Kwong equation of state (SRK-EOS) by minimizing the deviation between the measured and calculated vapor pressures for nonhydrocarbon compounds and hydrocarbon compounds including heavy alkanes up to n-tritetracontane (n-C 43 H 88 ) under different conditions. All the compounds are divided into four categories, that is, lightsaturated hydrocarbons, heavy-saturated hydrocarbons, aromatic compounds, and other compounds, among which the first three categories are used to examine their effects on the optimum reduced temperature for the entire database. By redefining the reduced temperature, three existing alpha functions together with the newly developed alpha functions for the PR-EOS as well as one existing alpha function and the newly developed alpha functions for the SRK-EOS are then used to evaluate their respective accuracy of predicting vapor pressures for pure substances. As for the newly expanded database with 1880 data points, the reduced temperature has its optimum value of 0.59 for the acentric factor for both the PR-EOS and SRK-EOS corresponding to the minimum absolute average relative deviations (AARDs) of 4.04% and 4.08%, respectively. Therefore, it is recommended that a reduced temperature of 0.60 be used for predicting the vapor pressures of heavy hydrocarbon compounds and their mixtures, yielding AARDs of 4.08% and 4.12% and maximum absolute relative deviations (MARDs) of 77.20% and 79.74% for the PR-EOS and SRK-EOS, respectively. Among the three subdivided categories, the heavysaturated hydrocarbons impose the largest effect on the optimum reduced temperature for the entire database, while the aromatic compounds take the second place, and the light-saturated hydrocarbons have the smallest effect. The sensitivity of the calculated alpha functions reduces with an increase in the reduced temperature, while it remains no change as the acentric factor varies. Finally, the newly developed alpha functions all lead to the minimum AARDs for the corresponding compound categories or for the entire database compared with existing alpha functions except for the light-saturated hydrocarbons. Also, the newly developed alpha function leads to the most accurate predictions of vaporization enthalpy with an AARD of 1.93% and MARD of 8.03% for the PR-EOS as well as an AARD of 2.02% and MARD of 7.76% for the SRK-EOS compared with the existing alpha functions.

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Determination of Individual Diffusion Coefficients of Solvent/CO2 Mixture in Heavy Oil With Pressure-Decay Method

Cited by 63 publications

References 17 publications

The Effect of Oil Properties on the Supercritical CO2 Diffusion Coefficient under Tight Reservoir Conditions

The Effect of Oil Properties on the Supercritical CO2 Diffusion Coefficient under Tight Reservoir Conditions

Multi-Component Thermal Fluid Injection Performance in Recovery of Heavy Oil Reservoirs

Optimization of the Reduced Temperature Associated with Peng–Robinson Equation of State and Soave–Redlich–Kwong Equation of State To Improve Vapor Pressure Prediction for Heavy Hydrocarbon Compounds

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