Real-time microseismic overburden surveillance at the Grane PRM field offshore Norway

Abstract

Real-time microseismic detection at offshore hydrocarbon fields is on its way to becoming a standard monitoring tool. Recently, increased focus on injection and overburden surveillance for an improved health, safety and environment (HSE) and cost saving has led to this development. Several hydrocarbon fields are already equipped with permanent reservoir monitoring (PRM) systems with seismic sensors permanently installed at the seafloor (Caldwell et al., 2015), and similar installations are planned or under consideration for some other offshore fields. PRM systems are in principle designed for acquiring active time-lapse seismic data 1-2 times per years, and as such, they are not used during most of their lifetime. But apart from active seismic, PRM systems can also be used for recording passive seismic data. With appropriate processing and analysis methods, such as microseismic event detection, the continuous stream of passive data can be converted into useful real-time subsurface information. This results in improved HSE, and therefore a more valuable PRM system. In 2014, a mini-PRM system with 172 multi-component sensors was installed at the Oseberg field, offshore Norway. The mini-PRM system indeed demonstrated the feasibility of injection and overburden surveillance using real-time passive seismic (Bussat et al., 2016). Despite high installation costs, a cost/benefit evaluation shows net benefits for the Oseberg system owing to better control on waste injection rates. The next step, and topic of the current paper, is to scale up and transfer the learnings from the small Oseberg system to the large Grane PRM system, so as to enable processing and analysis of large amounts of passive data and allow for real-time injection monitoring at Grane. Compared to the Oseberg case, the microseismic data processing is distributed and optimized to be able to use many more sensors and monitor larger subsurface volumes with increased resolution. Moreover, the detection method is improved from an originally strict semblance-based method to also include signal-to-noise (S/N) analysis of the semblance-weighted stack. Crucial noise filtering is integrated into the real-time processing flow, enhancing the sensitivity of the system and ensuring the best possible detection/localization at any time. Key personnel receive an alert immediately after an event detection. This makes the monitoring fully integrated into the follow-up of the daily operation.