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Downhole permanent DAS monitoring of CO2 geosequestration
Time-lapse (TL) seismic is widely used in CO2 geosequestration projects for both monitoring the behavior of the injected fluid in the target reservoir and demonstrating absence of unwanted migration out of primary containment. However, issues with land access and relatively high cost are only few of the downsides of the conventional 4D seismic for CO2 sequestration.
To overcome some of these issues, Stage 3 of the CO2CRC Otway Project aims to develop and test remotely-operated subsurface monitoring techniques capable of providing continuous/on-demand surveillance for CO2 storage, minimizing environmental and societal impact while increasing cost-effectiveness of the monitoring. In order to achieve this, we design a monitoring strategy combining multi-well 4D Vertical Seismic Profiling (VSP) acquired using permanently deployed Distributed Acoustic Sensors (DAS) and geophone arrays with continuous surveys through a network of remotely operated permanent seismic sources (Seismic Orbital Vibrators or SOV). Continuous monitoring requires a semi-automated data assimilation system to facilitate timely decision-making by site operators. Given the time-constraints and abundance of the data, we propose a data-driven approach that implements some relatively recent computer vision algorithms. Feasibility of the monitoring concept and its optimal design is validated by extensive numerical simulations and field tests.
Senior Research Fellow
WA School of Mines: Minerals, Energy and Chemical Engineering
Faculty of Science & Engineering
Curtin University (Perth, Australia)
Stanislav received PhD in mathematical geophysics from Lomonosov Moscow State University in 2012. Since his PhD years, Stanislav’s research has involved theoretical and practical aspects of several diverse themes: all aspects of rock physics, reservoir modelling and seismic monitoring. After Stanislav joined Curtin geophysics team, he has been working on (1) modelling-driven optimization of data analysis at both stages, seismic reservoir characterization and reservoir surveillance, and (2) application of statistical learning algorithms to the data assimilation.