Presentation Description
In the CO2 release near a shallow fault zone, significant subsurface changes occur within several weeks, and thus require frequent monitor surveys. Thus, this experiment will be monitored with so-called reverse 4D vertical seismic profiling (VSP) with a sparker source in the well and over 1000 receivers on the surface. This method avoids the need for shooting many shot points in each survey as required in conventional 4D VSP, which would make each monitor survey duration too long. The sparker source also provides ultra-high spatial resolution required for this small injection.
The reverse VSP will be complemented by a trial of time-lapse surface seismic monitoring using refracted waves, with a mobile source and the same array of surface receivers. The monitoring program for the deep injection includes continuous offset-VSP acquisition with permanently mounted surface orbital vibrators as seismic sources and downhole distributed acoustic sensors (DAS) complemented by vibroseis 4D VSP, a combination that proved its efficacy in the Otway Stage 3 Project. It would be useful to complement this proven technology with trials of new approaches, including real-time and in-depth analysis of induced seismicity, real-time continuous reflection data interpretation using machine learning, estimation of CO2 saturation from direct
The reverse VSP will be complemented by a trial of time-lapse surface seismic monitoring using refracted waves, with a mobile source and the same array of surface receivers. The monitoring program for the deep injection includes continuous offset-VSP acquisition with permanently mounted surface orbital vibrators as seismic sources and downhole distributed acoustic sensors (DAS) complemented by vibroseis 4D VSP, a combination that proved its efficacy in the Otway Stage 3 Project. It would be useful to complement this proven technology with trials of new approaches, including real-time and in-depth analysis of induced seismicity, real-time continuous reflection data interpretation using machine learning, estimation of CO2 saturation from direct