Imaging source cloud in hydraulic fracturing using wavefield back-propagation methods
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segam2021-3594502.1.pdf
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Published version
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258.1 KB
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Author(s)
Lyu, Bin
Bai, Tong
Kurosawa, Isao
Nakata, Nori
Date Issued
September 1, 2021
Journal
First International Meeting for Applied Geoscience & Energy
Publisher
Society of Exploration Geophysicists
Citation
Bin Lyu, Tong Bai, Isao Kurosawa, Nori Nakata; September 1, 2021. "Imaging source cloud in hydraulic fracturing using wavefield back-propagation methods." Proceedings of the First International Meeting for Applied Geoscience & Energy. First International Meeting for Applied Geoscience & Energy. (pp. pp. 2041-2045). ASME.
Version
Final published version
Abstract
Investigation of the source cloud induced by the hydraulicfracturing can provide important information for evaluating and monitoring the unconventional reservoirs. It is challenging to image the source cloud using the conventional arrival-picking-based methods, which appear large uncertainty and error-prone for numerous weak signals induced by the fracking. To improve the imaging quality of the source cloud, we use the wavefield backpropagation methods, including both Arithmetic-mean Reverse-Time Migration (AmRTM) and time-window autocorrelation Reverse-Time Migration (RTM). These methods are based on the full wave equation, which provides a better accuracy over the high-frequency assumption based traveltime approaches. Additionally, there is no requirement of arrival picking in the wavefield back-propagation methods, which helps reduce the uncertainty of source locations. With the generated source cloud imaging, the fractured volume could be estimated. We set up several experiments to indicate the effectiveness of the source cloud imaging using the wavefield backpropagation methods, considering the influence of natural fractures, random noise, and source onset time.
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1190/segam2021-3594502.1
