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Energies 2018, 11(3), 563; https://doi.org/10.3390/en11030563

Automatic Identification of Fractures Using a Density-Based Clustering Algorithm with Time-Spatial Constraints

1,2,3
,
1,2,* , 1,2
and
1,2
1
Key Laboratory of Shale Gas and Geoengineering, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China
2
Institutions of Earth Science, Chinese Academy of Sciences, Beijing 100029, China
3
University of Chinese Academy of Sciences, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Received: 23 January 2018 / Revised: 26 February 2018 / Accepted: 1 March 2018 / Published: 6 March 2018
(This article belongs to the Special Issue Unconventional Natural Gas (UNG) Recoveries 2018)
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Abstract

In shale gas hydraulic fracture monitoring or rock acoustic emission experiments, fracture plane identification is always a complex task. Conventional approaches typically use the source locating results derived from the micro-seismic event and then interpret the fracture plane using manual qualitative analysis. Large errors typically occur due to manual operations. On the other hand, the density-based clustering algorithm with spatial constraints is widely used in geographic information science, biological cells science and astronomy. It is an automated algorithm and can achieve good classification results. In this paper, we introduced the above-mentioned clustering algorithm with spatial constraints to fracture identification applications. Moreover, because micro-seismic events are 4D in nature, every micro-seismic event has both time and space information. Hence, we improve the conventional clustering algorithm by incorporating a time constraint. We test the proposed method using rock acoustic emissions data and compare our fracture identification results with CT scan images; the comparison clearly shows the effectiveness of the proposed method. View Full-Text
Keywords: hydraulic fracturing; fractures identification; clustering analysis; acoustic emission; Longmaxi shale hydraulic fracturing; fractures identification; clustering analysis; acoustic emission; Longmaxi shale
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Xue, Q.; Wang, Y.; Zhai, H.; Chang, X. Automatic Identification of Fractures Using a Density-Based Clustering Algorithm with Time-Spatial Constraints. Energies 2018, 11, 563.

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