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Full composition numerical simulation of CO2 utilization process in shale reservoir using projection-based embedded discrete fracture model (pEDFM) considering nano-confinement effect by Zhihao Jia; Linsong Cheng; Haoran Feng; Renyi Cao; Pin Jia; Baobiao Pu; Quanyu Pan; Junjie Shi is a Engineering article available to read on EtoBox.
What is Full composition numerical simulation of CO2 utilization process in shale reservoir using projection-based embedded discrete fracture model (pEDFM) considering nano-confinement effect about?
Carbon dioxide (CO2) injection for shale oil production is a promising CCUS (Carbon dioxide Capture, Utilization and Storage) technique, which achieves the synergistic effect of oil displacement and CO2 storage . The conventional black oil model is difficult to accurately characterize the complex fractures, the phase state considering nanoconfinement effect (NC) and the distribution of CO2 in the oil phase and gas phase. In this work, a full-composition (FC), projection-based three-dimensional embedded discrete fracture numerical model (3D-pEDFM) considering nanoconfinement effects is proposed to simulate the complex mass transfer process of CO2 injected into shale oil reservoirs. The discrete equations of the model are obtained by finite volume method (FVM), and the full implicit equations is solved by Newton-Raphson iteration method. The proposed new model is effectively adapted to the actual situation of CO2 injection development of fractured shale oil reservoir. These results obtained by comparing this model with the commercial software Eclipse300 in dealing with simple cases show the reliability of the model. Moreover, the processes of depletion development and CO2 injection d
Who reads Full composition numerical simulation of CO2 utilization process in shale reservoir using projection-based embedded discrete fracture model (pEDFM) considering nano-confinement effect?
It is typically read by researchers, students, and practitioners in Engineering.
- Author
- Zhihao Jia; Linsong Cheng; Haoran Feng; Renyi Cao; Pin Jia; Baobiao Pu; Quanyu Pan; Junjie Shi
- Publisher
- Elsevier BV
- Published
- 2023
- Language
- EN
- Field
- Engineering (Physical Sciences)