The use of Distributed Acoustic Sensing for Strain Fronts (DAS-SF) is gaining popularity as one of the tools to help characterize the geometries of hydraulic fracs and to assess the far-field efficiencies of stimulation operations in Unconventional Reservoirs. These strain fronts are caused by deformation of the rock during hydraulic fracture stimulation (HFS) which produces a characteristic strain signature measurable by interrogating a glass fiber in wells instrumented with a fiber optic (FO) cable cemented behind casing. This DAS application was first developed by Shell and OptaSense from datasets acquired in the Groundbirch Montney in Canada. In this paper we show examples of DAS-SF in wells stimulated for a variety of completion systems: plug-and-perforating (PnP), open hole packer sleeves (OHPS), as well as, data from a well completed via both ball-activated cemented single point entry sleeves (Ba-cSPES) and coil-tubing activated cemented single point entry sleeves (CTa-cSPES). By measuring the strain fronts during stimulation from nearby offset wells, it was observed that most stimulated stages produced far-field strain gradient responses in the monitor well. When mapped in space, the strain responses were found to agree with and confirm the dominant planar fracture geometry proposed for the Montney, with hydraulic fractures propagating in a direction perpendicular to the minimum stress. However; several unexpected and inconsistent off-azimuth events were also observed during the offset well stimulations in which the strain fronts were detected at locations already stimulated by previous stages. Through further integration and the analysis of multiple data sources, it was discovered that these strain events corresponded with stage isolation defects in the stimulated well, leading to "re-stimulation" of prior fracs and inefficient resource development. The strain front monitoring in the Montney has provided greater confidence in the planar fracture geometry hypothesis for this formation. The high resolution frac geometry information provided by DAS-SF away from the wellbore in the far-field has also enabled us to improve stage offsetting and well azimuth strategies. In addition, identifying the re-stimulation and loss of resource access that occurs with poor stage isolation also shows opportunities for improvement in future completion programs. This in turn, should allow us to optimize operational decisions to more effectively access the intended resource volumes. These datasets show how monitoring high-resolution deformation via FO combined with the integration of other data can provide high confidence insights about stimulation efficiency, frac geometry and well construction defects not available via other means.
Fiber-optic distributed acoustic sensing (DAS) can listen to a wide range of signals. This listening takes place at high sampling rates with fine spatial resolution, resulting in large data volumes. Data streaming solutions are available but result in large transmission and storage costs. In this paper, we describe strategies to convert large data streams from DAS interrogator units to diagnostics or processed products. Optimizing DAS systems results in higher signal-to-noise ratio for signals while extracting diagnostic features out of the noise that could be related to production or well engineering. DAS has sensitivity to diverse signals, and the first goal of edge processing is to separate them for consumption by various disciplines. Focusing the processing on specific aspects in the DAS recordings provides data products that are streamed in efficient ways. We show how DAS processing can deploy fast algorithms so that data diagnostics are sent to remote locations. This enables real-time-diagnostics and event-detection tools. By providing the bulk of computing in the field, data upload to remote servers is efficient and targeted. We show how this managed data stream enables digitalization of engineering and geoscience assets.
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