Determination of minimum miscibility pressure for CO 2 and oil system using acoustically monitored separator

Czarnota, Robert; Janiga, Damian; Stopa, Jerzy; Wojnarowski, Paweł

doi:10.1016/j.jcou.2016.11.004

Cited by 58 publications

(33 citation statements)

References 15 publications

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“…Mole fraction (%) 0 [37,38], the IFT method [39,40], the slim tube method [41], the magnetic resonance imaging method [32], the acoustically monitored separator method [42], and so on. Among these, the slim tube method is the most reliable and classical experimental method, but it is time-consuming, has a higher cost, and lacks uniform criteria [43].…”

Section: C1-c4 C5-c8 C9-c12 C13-c18 C19-c22 C23-c27 C28+mentioning

confidence: 99%

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Experimental Study on Reducing CO2–Oil Minimum Miscibility Pressure with Hydrocarbon Agents

Liu

Sun

et al. 2019

Energies

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CO2 flooding is an important method for improving oil recovery for reservoirs with low permeability. Even though CO2 could be miscible with oil in regions nearby injection wells, the miscibility could be lost in deep reservoirs because of low pressure and the dispersion effect. Reducing the CO2–oil miscibility pressure can enlarge the miscible zone, particularly when the reservoir pressure is less than the needed minimum miscible pressure (MMP). Furthermore, adding intermediate hydrocarbons in the CO2–oil system can also lower the interfacial tension (IFT). In this study, we used dead crude oil from the H Block in the X oilfield to study the IFT and the MMP changes with different hydrocarbon agents. The hydrocarbon agents, including alkanes, alcohols, oil-soluble surfactants, and petroleum ethers, were mixed with the crude oil samples from the H Block, and their performances on reducing CO2–oil IFT and CO2–oil MMP were determined. Experimental results show that the CO2–oil MMP could be reduced by 6.19 MPa or 12.17% with petroleum ether in the boiling range of 30–60 °C. The effects of mass concentration of hydrocarbon agents on CO2–oil IFT and crude oil viscosity indicate that the petroleum ether in the boiling range of 30–60 °C with a mass concentration of 0.5% would be the best hydrocarbon agent for implementing CO2 miscible flooding in the H Block.

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Section: C1-c4 C5-c8 C9-c12 C13-c18 C19-c22 C23-c27 C28+mentioning

confidence: 99%

“…It also requires a relatively low capital and avoids radiation exposure. However, its measurement accuracy is adversely affected by the phenomena of emulsion during mixing two fluids [42]. The IFT method is based on the concept that IFT between a crude oil and CO 2 becomes zero when they are miscible.…”

Section: C1-c4 C5-c8 C9-c12 C13-c18 C19-c22 C23-c27 C28+mentioning

confidence: 99%

Experimental Study on Reducing CO2–Oil Minimum Miscibility Pressure with Hydrocarbon Agents

Liu

Sun

et al. 2019

Energies

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“…Results of PVT characteristic are presented in Table 1 and Figure 2. A complete crude oil properties summary was reported in the previous author's investigation [5]. …”

Section: Pvt Characteristic Of Oilmentioning

confidence: 99%

Microscale modeling of CO₂-EOR process in coupling with laboratory measurements

Janiga

Czarnota

Stopa

et al. 2017

drill

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“…The minimum miscibility pressure can be measured using slim tube tests [4]. However, the laboratory measurements are costly and time-consuming [5]. MMP can also be determined using empirical correlations, but these correlations can lead to significant deviations, where the absolute error can reach up to 25% [6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Prediction of Minimum Miscibility Pressure (MMP) During CO2 Flooding Using Artificial Intelligence Techniques

2019

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Carbon dioxide (CO2) injection is one of the most effective methods for improving hydrocarbon recovery. The minimum miscibility pressure (MMP) has a great effect on the performance of CO2 flooding. Several methods are used to determine the MMP, including slim tube tests, analytical models and empirical correlations. However, the experimental measurements are costly and time-consuming, and the mathematical models might lead to significant estimation errors. This paper presents a new approach for determining the MMP during CO2 flooding using artificial intelligent (AI) methods. In this work, reliable models are developed for calculating the minimum miscibility pressure of carbon dioxide (CO2-MMP). Actual field data were collected; 105 case studies of CO2 flooding in anisotropic and heterogeneous reservoirs were used to build and evaluate the developed models. The CO2-MMP is determined based on the hydrocarbon compositions, reservoir conditions and the volume of injected CO2. An artificial neural network, radial basis function, generalized neural network and fuzzy logic system were used to predict the CO2-MMP. The models’ reliability was compared with common determination methods; the developed models outperform the current CO2-MMP methods. The presented models showed a very acceptable performance: the absolute error was 6.6% and the correlation coefficient was 0.98. The developed models can minimize the time and cost of determining the CO2-MMP. Ultimately, this work will improve the design of CO2 flooding operations by providing a reliable value for the CO2-MMP.

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Determination of minimum miscibility pressure for CO 2 and oil system using acoustically monitored separator

Cited by 58 publications

References 15 publications

Experimental Study on Reducing CO2–Oil Minimum Miscibility Pressure with Hydrocarbon Agents

Experimental Study on Reducing CO2–Oil Minimum Miscibility Pressure with Hydrocarbon Agents

Microscale modeling of CO₂-EOR process in coupling with laboratory measurements

Intelligent Prediction of Minimum Miscibility Pressure (MMP) During CO2 Flooding Using Artificial Intelligence Techniques

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