Monitoring CO۲ injection in shale gas reservoir through cross well seismic and electromagnetic tomography
Publish Year: 1404
نوع سند: مقاله ژورنالی
زبان: English
View: 17
This Paper With 9 Page And PDF Format Ready To Download
- Certificate
- من نویسنده این مقاله هستم
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
JR_IJMGE-59-4_011
تاریخ نمایه سازی: 16 دی 1404
Abstract:
Carbon dioxide (CO₂) injection, also known as geological carbon sequestration or carbon capture and storage, is a critical strategy for mitigating anthropogenic CO₂ emissions and their subsequent impact on the physical and petrophysical properties of subsurface formations. Shale gas reservoirs are receiving increased attention, with CO₂ injection emerging as a leading technology for enhancing gas recovery and achieving carbon storage. This study employs cross-well tomography, incorporating both seismic and electromagnetic (EM) methods, to monitor the CO₂ injection process within a synthetic shale gas reservoir model. The methodology utilizes time-lapse cross-well tomography data acquired before and after CO₂ injection. Seismic P-wave velocity (Vp) and electrical resistivity (ρ) serve as key physical parameters in detecting CO₂ distribution. A mixed-norm inversion technique is applied to improve the resolution and accuracy of recovered models. The results demonstrate that seismic tomography is effective in delineating changes in P-wave velocity associated with CO₂ saturation, while electromagnetic tomography captures resistivity variations indicative of CO₂ migration. Integrated seismic-EM tomography provides a comprehensive framework for monitoring CO₂ injection, enhancing the reliability of long-term carbon storage assessments.
Keywords:
Authors
Seyed Masoud Ghiasi
Institute of Geophysics, University of Tehran, Tehran, Iran.
Maysam Abedi
School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran.
مراجع و منابع این Paper:
لیست زیر مراجع و منابع استفاده شده در این Paper را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود Paper لینک شده اند :