Influence of pore water on the heat mining performance of supercritical CO2 injected for geothermal development

Zhang, Liang; Cui, Guodong; Yin, Zhang; Ren, Bo; Ren, Shaoran; Wang, Xiaohui

doi:10.1016/j.jcou.2016.08.008

Cited by 34 publications

(15 citation statements)

References 19 publications

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“…The sum of mole fractions of CO 2 and water in the CO 2 -rich phase is equal to one. Therefore, the following expression can be obtained combining equation (25) and equation (26): The flow chart for model simulation includes the following steps:…”

Section: Specific Volume and Relative Activity Coefficientmentioning

confidence: 99%

“…(3) According to the estimated composition of the aqueous phase, the mole fractions of CO 2 and water in the CO 2 -rich phase can be recalculated using equation (25) and equation (26).…”

Section: Specific Volume and Relative Activity Coefficientmentioning

confidence: 99%

“…This complicated geochemical process related to CO 2 dissolution has a long-term positive or adverse influence on the performance of subsurface systems. Firstly, the preliminary simulation revealed that the heat exploitation efficiency was decreased by 27% in a CO 2 geothermal system due to CO 2 dissolution and mineral precipitation [25]. Borgia et al [26] indicated that the geothermal reservoir could even be dead in 1 year.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Modelling of the Phase-Partitioning Behaviors for CO2-Brine System at Geological Conditions

Sun

Wang

et al. 2021

Lithosphere

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An improved phase-partitioning model is proposed for the prediction of the mutual solubility in the CO2-brine system containing Na+, K+, Ca2+, Mg2+, Cl-, and SO42-. The correlations are computationally efficient and reliable, and they are primarily designed for incorporation into a multiphase flow simulator for geology- and energy-related applications including CO2 sequestration, CO2-enhanced geothermal systems, and CO2-enhanced oil recovery. The model relies on the fugacity coefficient in the CO2-rich phase and the activity coefficient in the aqueous phase to estimate the phase-partitioning properties. In the model, (i) the fugacity coefficients are simulated by a modified Peng-Robinson equation of state which incorporates a new alpha function and binary interaction parameter (BIP) correlation; (ii) the activity coefficient is estimated by a unified equilibrium constant model and a modified Margules expression; and (iii) the simultaneous effects of salting-out on the compositions of the CO2-rich phase and the aqueous phase are corrected by a Pizter interaction model. Validation of the model calculations against literature experimental data and traditional models indicates that the proposed model is capable of predicting the phase-partitioning behaviors in the CO2-brine system with a higher accuracy at temperatures of up to 623.15 K and pressures of up to 350 MPa. Using the proposed model, the phase diagram of the CO2+H2O system is generated. An abrupt change in phase compositions is revealed during the transfer of the CO2-rich phase from vapor to liquid or supercritical. Furthermore, the preliminary simulation shows that the salting-out effect can considerably decrease the water content in the CO2-rich phase, which has not been well experimentally studied so far.

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Section: Specific Volume and Relative Activity Coefficientmentioning

confidence: 99%

“…(3) According to the estimated composition of the aqueous phase, the mole fractions of CO 2 and water in the CO 2 -rich phase can be recalculated using equation (25) and equation (26).…”

Section: Specific Volume and Relative Activity Coefficientmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Modelling of the Phase-Partitioning Behaviors for CO2-Brine System at Geological Conditions

Sun

Wang

et al. 2021

Lithosphere

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“…As CO 2 is injected into a geothermal reservoir, the brine initially occupying the reservoir is displaced by CO 2 . A CO 2 -plume then is formed, and hot sCO 2 is then produced from production wellbores with the extracted geothermal energy [4,[6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Thermo-economic analysis of a direct supercritical CO2 electric power generation system using geothermal heat

et al. 2021

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A comprehensive thermo-economic model combining a geothermal heat mining system and a direct supercritical CO 2 turbine expansion electric power generation system was proposed in this paper. Assisted by this integrated model, thermo-economic and optimization analyses for the key design parameters of the whole system including the geothermal well pattern and operational conditions were performed to obtain a minimal levelized cost of electricity (LCOE). Specifically, in geothermal heat extraction simulation, an integrated wellbore-reservoir system model (T2Well/ECO2N) was used to generate a database for creating a fast, predictive, and compatible geothermal heat mining model by employing a response surface methodology. A parametric study was conducted to demonstrate the impact of turbine discharge pressure, injection and production well distance, CO 2 injection flowrate, CO 2 injection temperature, and monitored production well bottom pressure on LCOE, system thermal efficiency, and capital cost. It was found that for a 100 MW e power plant, a minimal LCOE of $0.177/kWh was achieved for a 20-year steady operation without considering CO 2 sequestration credit. In addition, when CO 2 sequestration credit is $1.00/t, an LCOE breakeven point compared to a conventional geothermal power plant is achieved and a breakpoint for generating electric power generation at no cost was achieved for a sequestration credit of $2.05/t.

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“…One of the common methods for extracting methane from its hydrates without damaging to the marine is replacing carbon dioxide instead of methane hydrate (Bai et al, 2012;Ota et al, 2007). The utilization of carbon dioxide for heat transmission is more applicable for extraction heat from hot fractured rock respect to water (Cui et al, 2016;Pruess, 2006;Zhang et al, 2016). Also in the other viewpoint the carbon dioxide injection to the geothermal reservoirs and seabed environment has straight effect on reduction of carbon dioxide emission (Agartan et al, 2015;Eccles et al, 2009;Javadpour, 2009;Rau & Caldeira, 1999;Ren et al, 2015;Ren et al, 2014;Trevisan et al, 2014a).…”

Section: Introductionmentioning

confidence: 99%

Estimating CO2-Brine diffusivity using hybrid models of ANFIS and evolutionary algorithms

Bemani

Baghban

Mosavi

et al. 2020

Engineering Applications of Computational Fluid Mechanics

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One of the important parameters illustrating the mass transfer process is the diffusion coefficient of carbon dioxide which has a great impact on carbon dioxide storage in marine ecosystems, saline aquifers, and depleted reservoirs. Due to the complex interpretation approaches and special laboratory equipment for measurement of carbon dioxide-brine system diffusivity, the computational and mathematical methods are preferred. In this paper, the adaptive neuro-fuzzy inference system (ANFIS) is coupled with five different evolutionary algorithms for predicting the diffusivity coefficient of carbon dioxide. The R 2 values forthe testing phase are 0.9978, 0.9932, 0.9854, 0.9738 and 0.9514 for ANFIS optimized by particle swarm optimization (PSO), genetic algorithms (GA), ant colony optimization (ACO), backpropagation (BP), and differential evolution (DE), respectively. The hybrid machine learning model of ANFIS-PSO outperforms the other models.

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Influence of pore water on the heat mining performance of supercritical CO2 injected for geothermal development

Cited by 34 publications

References 19 publications

Modelling of the Phase-Partitioning Behaviors for CO2-Brine System at Geological Conditions

Modelling of the Phase-Partitioning Behaviors for CO2-Brine System at Geological Conditions

Thermo-economic analysis of a direct supercritical CO2 electric power generation system using geothermal heat

Estimating CO2-Brine diffusivity using hybrid models of ANFIS and evolutionary algorithms

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