Monitoring surface deformation over oilfield using MT-InSAR and production well data
Surface displacements associated with the average subsidence due to hydrocarbon exploitation in southwest of Iran which has a long history in oil production, can lead to significant damages to surface and subsurface structures, and requires serious consideration. In this study, the Small BAseline Subset (SBAS) approach, which is a multi-temporal Interferometric Synthetic Aperture Radar (InSAR) algorithm was employed to resolve ground deformation in the Marun region, Iran. A total of 22 interferograms were generated using 10 Envisat ASAR images. The mean velocity map obtained in the Line-Of-Sight (LOS) direction of satellite to the ground reveals the maximum subsidence on order of 13.5 mm per year over the field due to both tectonic and non-tectonic features. In order to assess the effect of non-tectonic features such as petroleum extraction on ground surface displacement, the results of InSAR have been compared with the oil production rate, which have shown a good agreement.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
An InSAR Phase Unwrapping Framework for Large-scale and Complex Events
Phase unwrapping remains a critical and challenging problem in InSAR processing, particularly in scenarios involving complex deformation patterns. In earthquake-related deformation, shallow sources can generate surface-b…
Detecting Volcano Deformation in InSAR using Deep learning
Globally 800 million people live within 100 km of a volcano and currently 1500 volcanoes are considered active, but half of these have no ground-based monitoring. Alternatively, satellite radar (InSAR) can be employed to…
Deep LearningEvent DetectionSparse InSAR Data 3D Inpainting for Ground Deformation Detection Along the Rail Corridor
Monitoring of ground movement close to the rail corridor, such as that associated with landslips caused by ground subsidence and/or uplift, is of great interest for the detection and prevention of possible railway faults…
3D InpaintingWaveDINO: Learning-Based Atmospheric Correction of Unwrapped InSAR Interferograms Validated by GNSS: Results at Laguna del Maule and Campi Flegrei Volcanoes
Interferometric Synthetic Aperture Radar (InSAR) enables effective monitoring of volcanic deformation; however, the observed signals are often corrupted by atmospheric phase delays, seasonal surface changes, and decorrel…
UnwrapDiff: A Conditional Diffusion Model for InSAR Phase Unwrapping
Phase unwrapping is a fundamental problem in InSAR data processing, supporting geophysical applications such as deformation monitoring and hazard assessment. Its reliability is limited by noise and decorrelation in radar…