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Acoustic Emission Response of Laboratory Hydraulic Fracturing in Layered Shale

  • Ning Li
  • Zhang Shicheng 
  • Yushi Zou
  • Xinfang Ma
  • Zhaopeng Zhang
  • Sihai Li
  • Ming Chen
  • Yueyue Sun
Original Paper
  • 49 Downloads

Abstract

Understanding the generation process of complex fracture network is essential for optimizing the hydraulic fracturing strategy in shale formations. In this study, laboratory fracturing was performed on shale specimens containing multiple bedding planes (BPs) combined with acoustic emission (AE) monitoring and computerized tomography scanning techniques. The injection pressure curve and the time dependency and hypocenter mechanisms of AE events in different stages were analyzed in detail. The relationship between AE spatial localization and hydraulically connected region were then further quantitatively discussed. Experimental results show that the characteristics of the pressure curve and AE response reflect well the hydraulic fracture (HF) growth behavior in layered shale. Shear events were detected around some weak BPs far away from the wellbore before the HF initiation. Stable injection pressure and a few AE events with low amplitude along the BP may indicate the stage of fluid leak-off. Numerous shear and tensile AE events and drastic pressure changes occur during the generation of a fracture network including breakdown in rock matrix and activation of multiple BPs. The shear instability of weak BPs caused by the stress perturbation during pressurization and HF growth tends to result in overestimation of the effective stimulated reservoir volume/hydraulically connected region.

Keywords

Hydraulic fracturing Complex fracture network Dynamic growth Acoustic emission Hypocenter mechanism 

Abbreviations

\({\sigma _{\text{h}}}\)

Horizontal minimum principal stress

\({\sigma _{\text{H}}}\)

Horizontal maximum principal stress

\({\sigma _{\text{v}}}\)

Vertical stress

\(Q\)

Injection rate

\(\mu\)

Fluid viscosity

\(\lambda\)

Proportion of dilatational first motions

\(t\)

Injection time

\({N_{{\text{AE}}}}\)

Accumulative number of AE events

SRV

Stimulated reservoir volume

ESRV

Effective stimulated reservoir volume

Ra

Accuracy rate of estimation

Notes

Acknowledgements

This paper was supported by the Major National Science and Technology Projects of China (No. 2016ZX05046-004; No. 2017ZX05039002-003), the National Basic Research Program of China (No. 2015CB250903) and the National Natural Science Foundation of China (No. 51704305).

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Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2018

Authors and Affiliations

  1. 1.China University of PetroleumBeijingChina
  2. 2.State Key Laboratory of Petroleum Resource and ProspectingChina University of PetroleumBeijingChina

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