Time-of-flight (TOF)-based two-phase upscaling for subsurface flow and transport |
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| Institution: | 1. Chevron Energy Technology Company, San Ramon, CA 94583, USA;2. Department of Mathematics, Texas A&M University, College Station, TX 77843, USA;1. Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China;2. School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China;3. School of Information Science and Engineering, Shandong Normal University, Ji’nan 250358, China;4. Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing 100053, China;5. Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA;6. Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06511, USA;7. Department of Radiology, Tianjin Huanhu Hospital, Tianjin 300350, China;8. School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China;9. Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China;10. Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing 100053, China;11. Department of Neurology, Qilu Hospital of Shandong University, Ji’nan 250012, China;12. Department of Neurology, Tianjin Huanhu Hospital, Tianjin University, Tianjin 300350, China;13. Department of Neurology, The Secondary Medical Center, National Clinical Research Center for Geriatric Disease, Chinese PLA General Hospital, Beijing 100853, China;14. Department of Radiology, Qilu Hospital of Shandong University, Ji’nan 250012, China;15. Department of Radiology, The Secondary Medical Center, National Clinical Research Center for Geriatric Disease, Chinese PLA General Hospital, Beijing 100853, China;p. Department of Radiology, Tianjin Medical University General Hospital, Tianjin 300052, China;q. Center of Alzheimer’s Disease, Beijing Institute for Brain Disorders, Beijing 100069, China;r. Beijing Institute of Geriatrics, Beijing 100053, China;s. National Clinical Research Center for Geriatric Disorders, Beijing 100053, China;1. School of Energy Resources, China University of Geosciences, Beijing 100083, PR China;2. School of Chemistry and Chemical Engineering, University of Jinan, Jinan 250024, PR China;3. Xianhe Oil Production Plant, Shengli Oilfield Branch, SINOPEC, Dongying 257068, PR China;4. Gudao Oil Production Plant, Shengli Oilfield Branch, SINOPEC, Dongying 256231, PR China;5. School of Petroleum Engineering, China University of Petroleum, East China, Qingdao, PR China;1. Shandong Key Laboratory of Oilfield Chemistry (China University of Petroleum (East China)), Qingdao 266580, PR China;2. Key Laboratory of Unconventional Oil & Gas Development (China University of Petroleum (East China)), Ministry of Education, Qingdao 266580, PR China;3. School of Energy Resources (China University of Geosciences, Beijing), Beijing 100083, PR China;4. School of Chemistry and Chemical Engineering (University of Jinan), Jinan 250022, PR China;5. Petroleum Engineering Technology Research Institute, Sinopec Shengli Oilfield Company, Dongying 257000, PR China;1. Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing 100053, China;2. Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China;3. School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China;4. Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China;5. Department of Neurology, Qilu Hospital of Shandong University, Ji’nan 250012, China;6. Department of Neurology, Tianjin Huanhu Hospital, Tianjin 300350, China;7. Institute of Geriatrics and Gerontology, Chinese PLA General Hospital, Beijing 100853, China;8. Department of Radiology, Qilu Hospital, Ji’nan 250012, China;9. Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing 100053, China;10. Department of Radiology, Chinese PLA General Hospital, Beijing 100853, China;11. Université Normandie, Inserm, Université de Caen-Normandie, Inserm UMR-S U1237, GIP Cyceron, Caen 14000, France;12. Department of Radiology, Tianjin Huanhu Hospital, Tianjin 300350, China;13. Department of Radiology, Tianjin Medical University General Hospital, Tianjin 300052, China;14. Center of Alzheimer’s Disease, Beijing Institute for Brain Disorders, Beijing 100053, China;15. Beijing Institute of Geriatrics, Beijing 100053, China;p. National Clinical Research Center for Geriatric Disorders, Beijing 100053, China;1. Department of Mechanical Engineering, Babol University of Technology, Babol, Iran;2. Department of Mathematics, COMSATS Institute of Information Technology, Sahiwal 57000, Pakistan;1. Department of Mechanical Engineering, National University of Singapore, 10 Kent Ridge Crescent, 119260, Singapore;2. Department of Aerodynamics, College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Yudao Street, Nanjing 210016, Jiangsu, China |
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| Abstract: | Subsurface formations are characterized by heterogeneity over multiple length scales, which can have a strong impact on flow and transport. In this paper, we present a new upscaling approach, based on time-of-flight (TOF), to generate upscaled two-phase flow functions. The method focuses on more accurate representations of local saturation boundary conditions, which are found to have a dominant impact (in comparison to the pressure boundary conditions) on the upscaled two-phase flow models. The TOF-based upscaling approach effectively incorporates single-phase flow and transport information into local upscaling calculations, accounting for the global flow effects on saturation, as well as the local variations due to subgrid heterogeneity. The method can be categorized into quasi-global upscaling techniques, as the global single-phase flow and transport information is incorporated in the local boundary conditions. The TOF-based two-phase upscaling can be readily integrated into any existing local two-phase upscaling framework, thus more flexible than local–global two-phase upscaling approaches developed recently. The method was applied to permeability fields with different correlation lengths and various fluid-mobility ratios. It was shown that the new method consistently outperforms existing local two-phase upscaling techniques, including recently developed methods with improved local boundary conditions (such as effective flux boundary conditions), and provides accurate coarse-scale models for both flow and transport. |
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