Working fluid selection and parameter optimization of enhanced geothermal system: a case study in the Gonghe basin
ID:437
Submission ID:53 View Protection:ATTENDEE
Updated Time:2024-05-20 10:47:07
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Oral Presentation
Abstract
Both working fluid selection and operational parameter optimization are two crucial prerequisites to the success of Enhanced Geothermal Systems (EGS). However, the suitability and recovery efficiency by using CO2 or H2O as EGS working fluid have not been studied so far. Moreover, existing operational parameter optimization methods confront some critical challenges like high computation demands or limited number of optimization parameters. This study firstly conducted a comparative analysis for the heat extraction efficiency between CO2 and H2O to explore their reservoir suitability and extraction conditions through thermal-hydraulic-mechanical (THM) coupling simulations. Then, a novel progressive many-objective optimization approach was proposed and applied to the potential assessment and parameter design of the EGS in the Gonghe Basin, China. The simulation results indicate that CO2 is more efficient in reservoirs with smaller permeability (no more than 2.5 mD), lower injection temperature (no more than 35℃), and lower pressure difference (8 MPa). In contrast, H2O is more suitable for reservoirs with bigger permeability (no less than 5.0 mD), higher injection temperature (no less than 40℃) and higher pressure difference (no less than 12 MPa). The EGS in the Gonghe Basin should choose H2O as its working fluid and optimal parameter scheme has the optimal injection rate of 40.71 kg/s, injection temperature of 73.22℃, and well spacing of 425.92 m. This optimal scheme can have a mean electric power of over 4 MW in 50 years, a total investment cost about 65.39 M$, a LCOE of 0.037 $/kWh, and a potential reduction in greenhouse gas emissions by 0.60-2.06 Mt.
Keywords
Geothermal energy; supercritical carbon dioxide; comparative analysis; many-objective optimization; thermal-hydraulic-mechanical coupling; non-dominated sorting genetic algorithm III
Submission Author
嘉杰 羊
中国矿业大学
建国 王
中国矿业大学力学与土木工程学院
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