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    Optimization and uncertainty quantification model for time-continuous geothermal energy extraction undergoing re-injection

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    Name:
    2112.05544.pdf
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    4.099Mb
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    PDF
    Description:
    Preprint
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    Type
    Preprint
    Authors
    Hoteit, Hussein cc
    He, Xupeng
    Yan, Bicheng
    Vahrenkamp, Volker cc
    KAUST Department
    Physical Science and Engineering (PSE) Division
    Energy Resources and Petroleum Engineering Program
    Ali I. Al-Naimi Petroleum Engineering Research Center (ANPERC)
    Energy Resources & Petroleum Engineering
    Physical Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
    Date
    2021-12-10
    Permanent link to this record
    http://hdl.handle.net/10754/674025
    
    Metadata
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    Abstract
    Geothermal field modeling is often associated with uncertainties related to the subsurface static properties and the dynamics of fluid flow and heat transfer. Uncertainty quantification using simulations is a useful tool to design optimum field-development and to guide decision-making. The optimization process includes assessments of multiple time-dependent flow mechanisms, which are functions of operational parameters subject to subsurface uncertainties. This process requires careful determination of the parameter ranges, dependencies, and their probabilistic distribution functions. This study presents a new approach to assess time-dependent predictions of thermal recovery and produced-enthalpy rates, including uncertainty quantification and optimization. We use time-continuous and multi-objective uncertainty quantification for geothermal recovery, undergoing a water re-injection scheme. The ranges of operational and uncertainty parameters are determined from a collected database, including 135 geothermal fields worldwide. The uncertainty calculation is conducted non-intrusively, based on a workflow that couples low-fidelity models with Monte Carlo analysis. Full-physics reservoir simulations are used to construct and verify the low-fidelity models. The sampling process is performed with Design of Experiments, enhanced with space-filling, and combined with analysis of covariance to capture parameter dependencies. The predicted thermal recovery and produced-enthalpy rates are then evaluated as functions of the significant uncertainty parameters based on dimensionless groups. The workflow is applied for various geothermal fields to assess their optimum well-spacing in their well configuration. This approach offers an efficient and robust workflow for time-continuous uncertainty quantification and global sensitivity analysis applied for geothermal field modeling and optimization.
    Sponsors
    We would like to thank CMG Ltd. for providing the STARS academic license, KAUST for the support, and UQLab for the software’s license.
    Publisher
    arXiv
    arXiv
    2112.05544
    Additional Links
    https://arxiv.org/pdf/2112.05544.pdf
    Collections
    Energy Resources and Petroleum Engineering Program; Ali I. Al-Naimi Petroleum Engineering Research Center (ANPERC); Preprints; Physical Science and Engineering (PSE) Division

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