Detecting microseismic events on DAS fiber with super-human accuracy

Fantine Huot, Ariel Lellouch, Paige Given, Robert G. Clapp, Biondo L. Biondi, Tamas Nemeth, Kurt Nihei

Research output: Contribution to journalConference articlepeer-review

Abstract

Microseismic analysis is the primary tool available for fracture characterization in unconventional reservoirs. As DAS fibers are installed in the target reservoir and are thus close to the microseismic events, they hold vast potential for their high-resolution analysis. However, accurately detecting microseismic signals in continuous data is challenging and time-consuming. DAS acquisitions generate substantial data volumes, and microseismic events have a low signal-to-noise ratio in individual DAS channels. Herein we design, train, and deploy a deep learning model to automatically detect thousands of microseismic events in DAS data acquired inside a shale reservoir. The stimulation of two offset wells generates the microseismic activity. The deep learning model achieves an accuracy of over 98% on our benchmark dataset of manually-picked events and even detects low-amplitude events missed during manual picking.

Original languageEnglish
Pages (from-to)3174-3178
Number of pages5
JournalSEG Technical Program Expanded Abstracts
Volume2021-September
DOIs
StatePublished - 2021
Externally publishedYes
Event1st International Meeting for Applied Geoscience and Energy - Denver, United States
Duration: 26 Sep 20211 Oct 2021

Fingerprint

Dive into the research topics of 'Detecting microseismic events on DAS fiber with super-human accuracy'. Together they form a unique fingerprint.

Cite this