We present one of the first case studies demonstrating the use of distributed acoustic sensing deployed on regional unlit fiber-optic telecommunication infrastructure (dark fiber) for broadband seismic monitoring of both near-surface soil properties and earthquake seismology. We recorded 7 months of passive seismic data on a 27 km section of dark fiber stretching from West Sacramento, CA to Woodland, CA, densely sampled at 2 m spacing. This dataset was processed to extract surface wave velocity information using ambient noise interferometry techniques; the resulting V_S profiles were used to map both shallow structural profiles and groundwater depth, thus demonstrating that basin-scale variations in hydrological state could be resolved using this technique. The same array was utilized for detection of regional and teleseismic earthquakes and evaluated for long period response using records from the M8.1 Chiapas, Mexico 2017, Sep 8th event. The combination of these two sets of observations conclusively demonstrates that regionally extensive fiber-optic networks can effectively be utilized for a host of geoscience observation tasks at a combination of scale and resolution previously inaccessible.

Previous studies have shown that underground natural gas storage (UGS) in California has served a critical role in meeting energy demands in California, and there is no immediate alternative. Therefore, it is important to ensure the safety of UGS infrastructure, especially considering that many of the UGS sites are using a combination of new and old wells, some of which were installed decades ago and re-purposed for UGS. The purpose of this project is to develop an integrated risk management and decision support system (IRMDSS) to manage risks associated with this heterogeneous subsurface infrastructure. The approach of the IRMDSS is to take advantage of the predictive capability of mechanistic models, with support from data acquired from advanced monitoring technologies, for evaluation and analysis of various incident scenarios or potential threats. In this project, we have demonstrated data collection by four advanced monitoring technologies. These include two downhole monitoring technologies, distributed temperature sensing (DTS), distributed acoustic sensing (DAS), and two surface monitoring technologies, Interferometric Synthetic Aperture Radar (InSAR), and unmanned aerial vehicle (UAV). DTS and DAS data are collected continuously, providing information related to individual wells. InSAR data are collected frequently (~every 24 days), and UAV data can be collected as frequently as is practical depending on need. Together, these subsurface and surface monitoring technologies provide near real-time information useful for risk management of UGS facilities.

Enhanced geothermal systems (EGS) require cost-effective monitoring of fracture networks. We validate the capability of using borehole distributed acoustic sensing (DAS) ambient noise for fracture monitoring using core photos and core logs. The EGS Collab project has conducted 10 m scale field experiments of hydraulic fracture stimulation using 50-60 m deep experimental wells at the Sanford Underground Research Facility (SURF) in Lead, South Dakota. The first EGS Collab testbed is located at 1616.67 m (4850 ft) depth at SURF and consists of one injection well, one production well, and six monitoring wells. All wells are drilled subhorizontally from an access tunnel called a drift. The project uses a single continuous fiber-optic cable installed sequentially in the six monitoring wells to record DAS data for monitoring hydraulic fracturing during stimulation. We analyze 60 s time records of the borehole DAS ambient noise data and compute the noise root-mean-square (rms) amplitude on each channel (points along the fiber cable) to obtain DAS ambient noise rms amplitude depth profiles along the monitoring wellbore. Our noise rms amplitude profiles indicate amplitude peaks at distinct depths. We compare the DAS noise rms amplitude profiles with borehole core photos and core logs and find that the DAS noise rms amplitude peaks correspond to the locations of fractures or lithologic changes indicated in the core photos or core logs. We then compute the hourly DAS noise rms amplitude profiles in two monitoring wells during three stimulation cycles in 72 h and find that the DAS noise rms amplitude profiles vary with time, indicating the fracture opening/growth or closing during the hydraulic stimulation. Our results demonstrate that borehole DAS passive ambient noise can be used to detect fractures and monitor fracturing processes in EGS reservoirs.