Investigation of gas solubility and its effects on natural gas reserve and production in tight formations

Liu, Guangfeng; He, Leiyu; Fan, Zhixing; He, Yilin; Wu, Zheng; Wang, Zhenjia

doi:10.1016/j.fuel.2021.120507

Cited by 7 publications

(4 citation statements)

References 32 publications

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“…8 The exploitation and application of hydrates are based on accurate information about the phase change kinetics (i.e., formation and dissociation) and thermodynamic properties of NGHs that have been extensively explored. Two physical parameters that are very important for controlling the hydrate phase change are the solubilities and self-diffusion coefficients of light alkanes in water, 9 and particularly in NaCl solutions, 10 as hydrates often occur in seabed sediments. 1 To date, many experimentalists have measured the solubility of methane, 11−17 ethane, 11,17 propane, 12,18−21 and butane 16 in pure water and in NaCl solutions 22−28 at different temperatures, pressures, and molalities.…”

Section: Introductionmentioning

confidence: 99%

“…The exploitation and application of hydrates are based on accurate information about the phase change kinetics (i.e., formation and dissociation) and thermodynamic properties of NGHs that have been extensively explored. Two physical parameters that are very important for controlling the hydrate phase change are the solubilities and self-diffusion coefficients of light alkanes in water, and particularly in NaCl solutions, as hydrates often occur in seabed sediments…”

Section: Introductionmentioning

confidence: 99%

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Solubilities and Self-Diffusion Coefficients of Light n-Alkanes in NaCl Solutions at the Temperature Range (278.15–308.15) K and Pressure Range (1–300) bar and Thermodynamics Properties of Their Corresponding Hydrates at (150–290) K and (1–7000) bar

et al. 2023

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Continuous Fractional Component Monte Carlo (CFCMC) and molecular dynamics (MD) simulations are performed to calculate the solubilities and self-diffusion coefficients of four light n-alkanes (methane, ethane, propane, and n-butane) in aqueous NaCl solutions as well as the thermodynamic properties of their corresponding hydrate crystals. Correction factors k ij to the Lorentz–Berthelot combining rules for alkane groups (CH3) and water are optimized (k ij = 1.04) by fitting excess chemical potentials to experimental data at 1 bar and 298.15 K. Using these values of k ij , we calculate the solubilities of the four alkanes in aqueous NaCl solutions with different molalities (0–6) mol/kg at different temperatures (278.15–308.15) K and pressures (1, 100, 200, 300) bar. The diffusion coefficients of the four alkanes in NaCl solutions (0–6) mol/kg are calculated at different temperatures (278.15–308.15) K and 1 bar and corrected for the finite-size effects. The lattice parameters of the corresponding hydrates with different guest molecules are computed using MD simulations at different temperatures (150–290) K and pressures (5–700) MPa. Isothermal compressibilities at 287.15 K and thermal expansion coefficients at 14.5 MPa for the corresponding hydrates are calculated. We present an extensive collection of thermodynamic data related to gas hydrates that contribute to a fundamental understanding of natural gas hydrate science.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Solubilities and Self-Diffusion Coefficients of Light n-Alkanes in NaCl Solutions at the Temperature Range (278.15–308.15) K and Pressure Range (1–300) bar and Thermodynamics Properties of Their Corresponding Hydrates at (150–290) K and (1–7000) bar

et al. 2023

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“…Currently, energy is the driving force to promote the development of all countries in the world. Natural gas has become a global energy hotspot with its huge reserves . More than 90% of the world’s total natural gas storage exists in the form of natural gas hydrate in deep-sea clay silt or muddy sediments, which is difficult to exploit. , In 2017, China carried out the world’s first natural gas hydrate (NGH) production test in the clay silt of the northern South China Sea and achieved great success .…”

Section: Introductionmentioning

confidence: 99%

Analysis and Prediction of Methane Invasion Distance Considering Real Ground Boundary

Liu

Chen

et al. 2021

ACS Omega

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Natural gas has become a global energy consumption hotspot because of its large reserves and clean combustion. Due to soil corrosion, construction damage, and natural disasters, leakage accidents of buried natural gas pipelines often occur. In this paper, the steady simulation method was used to study the methane invasion limit state (MILS) and the methane invasion limit distance (MILD) under the conditions of hardened surface ground (HSG), unhardened surface ground (UHSG), and semihardened surface ground (SHSG), and the transient simulation of methane invasion distance (MID) under the condition of HSG with the largest MILD was carried out. The results showed that regardless of ground conditions, with the increase of leakage time, the diffusion range of methane in soil will not increase all the time, and there was a limit state (MILS). The distribution range and concentration of methane in the soil under HSG condition were the largest, followed by the SHSG condition, and the UHSG condition was the smallest. When the ground condition changed from UHSG to HSG, the MILD increased from 3.41 to 9.32 m. The HSG condition will increase the MILD and the range of dangerous areas. The buried depth of the pipeline had a serious impact on the MILD. When the buried depth of the pipeline increased from 0.3 to 1.5 m, the MILD increased from 1.75 to 3.49 m under the condition of UHSG and exceeded 10 m under the condition of HSG. The average error of the MID prediction model was 2.37% under the condition of HSG, which can accurately predict the leakage of buried pipeline. The MID provides a reference for the layout of urban underground gas leakage monitoring points. The MILD can provide guidance for the safe distance between natural gas pipeline and structures in the design code of natural gas pipeline.

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“…Attempts to model the vapor-liquid phase equilibria of non-hydrocarbon and hydrocarbon gases and brine solutions have always been considered by researchers due to the limited number of measurements. The activity coefficient, Henry's constant approach, and EOSs were widely used in thermodynamic models in order to gain information about the equilibrium conditions of non-hydrocarbon and hydrocarbon gases and pure water or aqueous electrolytes solutions 5,9,[31][32][33][34][35][36][37][38][39][40][41] . Although Henry's law can appropriately be utilized to estimate the solubilities, this approach has several drawbacks.…”

mentioning

confidence: 99%

Modeling the solubility of light hydrocarbon gases and their mixture in brine with machine learning and equations of state

Mohammadi

Hadavimoghaddam

Atashrouz

et al. 2022

Sci Rep

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Knowledge of the solubilities of hydrocarbon components of natural gas in pure water and aqueous electrolyte solutions is important in terms of engineering designs and environmental aspects. In the current work, six machine-learning algorithms, namely Random Forest, Extra Tree, adaptive boosting support vector regression (AdaBoost-SVR), Decision Tree, group method of data handling (GMDH), and genetic programming (GP) were proposed for estimating the solubility of pure and mixture of methane, ethane, propane, and n-butane gases in pure water and aqueous electrolyte systems. To this end, a huge database of hydrocarbon gases solubility (1836 experimental data points) was prepared over extensive ranges of operating temperature (273–637 K) and pressure (0.051–113.27 MPa). Two different approaches including eight and five inputs were adopted for modeling. Moreover, three famous equations of state (EOSs), namely Peng-Robinson (PR), Valderrama modification of the Patel–Teja (VPT), and Soave–Redlich–Kwong (SRK) were used in comparison with machine-learning models. The AdaBoost-SVR models developed with eight and five inputs outperform the other models proposed in this study, EOSs, and available intelligence models in predicting the solubility of mixtures or/and pure hydrocarbon gases in pure water and aqueous electrolyte systems up to high-pressure and high-temperature conditions having average absolute relative error values of 10.65% and 12.02%, respectively, along with determination coefficient of 0.9999. Among the EOSs, VPT, SRK, and PR were ranked in terms of good predictions, respectively. Also, the two mathematical correlations developed with GP and GMDH had satisfactory results and can provide accurate and quick estimates. According to sensitivity analysis, the temperature and pressure had the greatest effect on hydrocarbon gases’ solubility. Additionally, increasing the ionic strength of the solution and the pseudo-critical temperature of the gas mixture decreases the solubilities of hydrocarbon gases in aqueous electrolyte systems. Eventually, the Leverage approach has revealed the validity of the hydrocarbon solubility databank and the high credit of the AdaBoost-SVR models in estimating the solubilities of hydrocarbon gases in aqueous solutions.

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Investigation of gas solubility and its effects on natural gas reserve and production in tight formations

Cited by 7 publications

References 32 publications

Solubilities and Self-Diffusion Coefficients of Light n-Alkanes in NaCl Solutions at the Temperature Range (278.15–308.15) K and Pressure Range (1–300) bar and Thermodynamics Properties of Their Corresponding Hydrates at (150–290) K and (1–7000) bar

Solubilities and Self-Diffusion Coefficients of Light n-Alkanes in NaCl Solutions at the Temperature Range (278.15–308.15) K and Pressure Range (1–300) bar and Thermodynamics Properties of Their Corresponding Hydrates at (150–290) K and (1–7000) bar

Analysis and Prediction of Methane Invasion Distance Considering Real Ground Boundary

Modeling the solubility of light hydrocarbon gases and their mixture in brine with machine learning and equations of state

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