10:40 〜 11:00
[BH-01] Fiber Optic: A New Paradigm in Borehole Seismic
★ Invited
Irrespective of the play, a constant to data harvesting is to minimize acquisition time without impacting data quality and if possible, do more with less. The versatile and highly sensitive distributed fiber-optic sensing technology maybe the answer to those acquisition challenges as it provides continuous data in both time and space and can be used for long-term monitoring in many places otherwise inaccessible due to size restrictions, high endurance to harsh environments, and long sensing range. These characteristics provide opportunities and challenges in data acquisition, processing, and interpretation.
Using a few case studies, we document how after thorough modeling of the cable-to-formation coupling and modeling of the anticipated recorded amplitude, distributed vibration sensing fiber could be deployed efficiently to provide high-resolution quality seismic data both onshore and offshore in relation to vertical seismic profile acquisitions.
Furthermore, to minimize processing time, we leverage full-waveform inversion in order to compute a highly detailed data-driven model of the subsurface velocity, absorption and reflectivity for use in seismic imaging and interpretation by minimizing the differences between observed and modeled seismic waveforms. In this work, we use a processing workflow that leverages the strain information directly rather than converting the strain data to velocity.
Using a few case studies, we document how after thorough modeling of the cable-to-formation coupling and modeling of the anticipated recorded amplitude, distributed vibration sensing fiber could be deployed efficiently to provide high-resolution quality seismic data both onshore and offshore in relation to vertical seismic profile acquisitions.
Furthermore, to minimize processing time, we leverage full-waveform inversion in order to compute a highly detailed data-driven model of the subsurface velocity, absorption and reflectivity for use in seismic imaging and interpretation by minimizing the differences between observed and modeled seismic waveforms. In this work, we use a processing workflow that leverages the strain information directly rather than converting the strain data to velocity.
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