Talk · SPE Annual Caspian Technical Conference and Exhibition · November 25, 2025
InSAR-Based Monitoring of Surface Deformation Risks in Azerbaijan: Insights from Absheron Oilfields
Emil Bayramov, Manfred Buchroithner, M. Kada, Jessica Neafie, Аскар Шамшидинович Дуйсенбиев, S. Aliyeva
Abstract
Abstract Areas with intensive petroleum production are prone to significant ground deformation, which can lead to serious environmental and infrastructure risks. This study investigates vertical surface movements, including both subsidence and uplift, within a major oil and gas producing region of the Absheron Peninsula, located along the western coast of the Caspian Sea. Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) was applied to a time series of satellite radar images spanning 2015–2023 to map deformation patterns at a high spatial resolution. To better understand the driving forces behind these changes, the analysis was combined with Geographically Weighted Regression (GWR), examining correlations with factors such as well density and elevation. Results reveal notable vertical displacement, with maximum subsidence and uplift rates of −26 mm/year and +23 mm/year, respectively. Statistical modeling indicates that approximately 32% of the variability in subsidence can be attributed to the combined influence of well concentration and elevation. These findings highlight the importance of continuous satellite-based monitoring and geospatial analytics to assess and manage ground deformation risks in oil- and gas-producing regions.
More by Sonny Irawan
- An explainable physics-informed neural network prototype for synthetic proppant transport in a two-dimensional fracture domainPhysics of Fluids · 2026
- Physics‐Guided Neural Regression Modeling of Energy‐Balance Proxies for Hydraulic Fracture Propagation in 1D–3DInternational Journal for Numerical and Analytical Methods in Geomechanics · 2026
- Modeling and Prediction of Kappaphycus alvarezii Viscosity Using Artificial Neural Network and Adaptive Neuro-Fuzzy Inference SystemJournal of Earth Energy Science Engineering and Technology · 2025 · Citations: 1
- Physics-Informed Machine Learning for Predicting Drilling Fluid Losses in Naturally Fractured Caspian FormationsSPE Annual Caspian Technical Conference and Exhibition · 2025 · Citations: 4
