A Digital Twin for Unconventional Reservoirs: A Multiscale Modeling and Algorithm to Investigate Complex Mechanisms
KAUST DepartmentComputational Transport Phenomena Lab
Computational Transport Phenomena Laboratory, Division of Physical Science and Engineering, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia
Earth Science and Engineering Program
Physical Science and Engineering (PSE) Division
KAUST Grant NumberBAS/1/135101-01
Online Publication Date2020-11-02
Print Publication Date2020-10-31
Permanent link to this recordhttp://hdl.handle.net/10754/665790
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AbstractThe special mechanisms underneath the flow and transport behaviors in unconventional reservoirs are still challenging an accurate and reliable production estimation. As an emerging approach in intelligent manufacturing, the concept of digital twin has attracted increasing attentions due to its capability of monitoring engineering processes based on modeling and simulation in digital space. The application potential is highly expected especially for problems with complex mechanisms and high data dimensions, because the utilized platform in the digital twin can be easily extended to cover more mechanisms and solve highly complicated problems with strong nonlinearity compared with experimental studies in physical space. In this paper, a digital twin is designed to numerically model the representative mechanisms that affect the production unconventional reservoirs, such as capillarity, dynamic sorption, and injection salinity, and it incorporates multiscale algorithms to simulate and illustrate the effect of these mechanisms on flow and transport phenomena. The preservation of physical laws among different scales is always the first priority, and simulation results are analyzed to verify the robustness of proposed multiscale algorithms.
CitationZhang, T., Li, Y., Cai, J., Meng, Q., Sun, S., & Li, C. (2020). A Digital Twin for Unconventional Reservoirs: A Multiscale Modeling and Algorithm to Investigate Complex Mechanisms. Geofluids, 2020, 1–12. doi:10.1155/2020/8876153
SponsorsThe work of Tao Zhang, Yiteng Li, and Shuyu Sun was supported by funding from the National Natural Scientific Foundation of China (Grants Nos. 51874262 and 51936001) and King Abdullah University of Science and Technology (KAUST) through the Grant no. BAS/1/135101-01.
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