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Honorable Mention for Best Paper in The Leading Edge in 2020

Congratulations to Ariel Lellouch, Bin Luo, Biondo Biondi, Owen Huff and Ge Jin
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Congratulations to Ariel Lellouch, Bin Luo, Biondo Biondi, Owen Huff and Ge Jin for receiving Honorable Mention for Best Paper in The Leading Edge in 2020 for their paper “Validating the origin of microseismic events in target reservoir using guided waves recorded by DAS”.

Congratulations on your excellent contributions to The Leading Edge!

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We develop a new algorithm that uses guided-wave energy in distributed acoustic sensing (DAS) records to identify microseismic events originating within or very close to a shale reservoir. Guided waves are dispersive waves that propagate in a low-velocity layer bounded by two high-velocity layers. This is a geologic structure that is seen for some shale reservoirs, most notably the Eagle Ford. Only microseismic events originating within or close to the low-velocity layer will excite significant guided-wave energy, which can be observed in DAS records. We confirm the relationship between guided-wave energy and event depth relative to the reservoir by using synthetic modeling. Given the known velocity structure, we can predict the dispersion curves for guided waves and use them to separate body and guided waves. We demonstrate a method to quantify the amplitude of guided waves in field DAS data recorded directly above the Eagle Ford Shale. Using this technique, we can separate events that originate within or close to the Eagle Ford from events that do not, thus circumventing the large depth uncertainty in a microseismic catalog derived from surface geophones. Our analysis shows that events classified as originating within or close to the Eagle Ford are horizontally closer to the stimulating well than non-Eagle Ford events. This is interpreted as representing different hydraulic fracture geometries in the Eagle Ford compared to its bounding formations, the Buda Limestone and Austin Chalk. The application of our method yields a new catalog that highlights the events relevant to stimulation and production in the target reservoir. It also provides a strong depth constraint that can improve relocation attempts using surface data, enabling a more accurate estimation of stimulated rock volume geometry.