Jinjie Mao Ntnu Jinjie is sharing her phd project on "data driven modeling optimization for subsurface co2 storage." join her on a journey to mitigate carbon footprints by injecting co2 into. State of the art techniques with regard to numerical simulation, data driven modeling (ddm), and multi objective optimization are comprehensively reviewed.
Pdf Subsurface Topographic Modeling Using Geospatial And Data Driven Mao, jinjie; jahanbani ghahfarokhi, ashkan. (2023) impact of uncertainties and decision variables on co2 enhanced oil recovery and storage: a numerical investigation. After earning my master's degree in applied earth sciences (track: geo energy engineering) at the delft university of technology in 2021, i work as a junior reservoir engineer at china national. Jinjie mao norwegian university of science and technology 在 ntnu.no 的电子邮件经过验证 文章 1–5. To better understand and manage our environment for safety and economic reasons, much progress has been made in imaging the subsurface and estimating physical properties based on remote sensing data.
Lingjun Mao Homepage Jinjie mao norwegian university of science and technology 在 ntnu.no 的电子邮件经过验证 文章 1–5. To better understand and manage our environment for safety and economic reasons, much progress has been made in imaging the subsurface and estimating physical properties based on remote sensing data. This study explores the application of sparsity promoting dynamic mode decomposition (sp dmd) for developing reduced order models (roms) that effectively manage the computational complexity of subsurface co2 storage simulations. Applications of the intelligent modeling optimization paradigm is surveyed, showcasing the current deficiencies and future trends in gcs gcsu (geological co2 storage and utilization) practice. The research will contribute to the sustainable utilization of the subsurface by developing digital solutions for modeling and optimization of co2 injection for subsurface storage and. In this study, a kernel extreme learning machine (kelm) model based on variational mode decomposition (vmd) and dung beetle optimization optimizer (dbo) was proposed to perform the prediction task.
Pdf Project Deepgeo âž Data Driven 3d Subsurface Mapping This study explores the application of sparsity promoting dynamic mode decomposition (sp dmd) for developing reduced order models (roms) that effectively manage the computational complexity of subsurface co2 storage simulations. Applications of the intelligent modeling optimization paradigm is surveyed, showcasing the current deficiencies and future trends in gcs gcsu (geological co2 storage and utilization) practice. The research will contribute to the sustainable utilization of the subsurface by developing digital solutions for modeling and optimization of co2 injection for subsurface storage and. In this study, a kernel extreme learning machine (kelm) model based on variational mode decomposition (vmd) and dung beetle optimization optimizer (dbo) was proposed to perform the prediction task.
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