Advanced Computation Methods towards High-Resolution Fiber Optic Distributed Acoustic Sensing

Commercial-scale geothermal reservoirs are thousands of feet below the Earth’s surface, so scientists rely on indirect measurements to reconstruct 3D subsurface reservoir maps from seismic vibrations, electromagnetic waves, or gravity measurements. Geothermal operators typically use electromagnetic or gravity surveys, but seismic surveys are preferable because they enable higher-resolution 3D subsurface map reconstruction. Seismic velocity models indicate subsurface materials’ speed of sound, thus reservoir parameters— rock type, fluid type, rock porosity, pressure, saturation, and temperature. The primary challenge in calculating deep seismic velocity models is first calculating high-resolution near-surface seismic velocity models. Improving near-surface seismic velocity models requires denser sensor spacing— cost prohibitive with traditional sensors. Distributed acoustic sensing is being rapidly adopted in oil and gas which repurposes fiber-optic cable as many densely-spaced seismic sensors. Distributed acoustic sensing shows promise for lowering the cost of repeatable, reliable seismic surveys as it requires a single power source and the sensor is flexible and low-cost. Current commercial distributed acoustic sensing systems in oil and gas applications are based on coherent optical time domain reflectometry technology which can measure vibrations averaged along 25-30 foot fiber segments. The proposed distributed acoustic sensing approach is based on optical frequency domain reflectometry and collects millimeter-scale data, which will enable high-resolution geotechnical surveys. However, seismic processing software available to geothermal operators is unable to efficiently process high-resolution seismic data sets. Thus we propose to develop advanced computational methods for high-resolution distributed acoustic sensing signals from commercially available optical frequency domain reflectometry equipment towards development of cost efficient data acquisition and analysis of near-surface geotechnical surveys for geothermal applications. During Phase I, we will acquire relevant high definition distributed acoustic sensing data sets for processing. Early on we will test and develop new algorithms on existing geotechnical data from the Brady Hot Spring lower-resolution distributed acoustic sensing data. Afterwards we will utilize both traditional and novel algorithms to process high definition distributed acoustic sensing data. During Phase II, the technology will be further advanced and demonstrated, and commercial transition partners will be identified. Distributed acoustic sensing is already utilized in the oil and gas and geothermal markets. The proposed technology will improve near surface measurements resulting in better deep measurements, improving reservoir parameter uncertainty and decreasing financial risk.