Fracture-development identification using trajectory-based clustering algorithm with time-spatial constraints
- Qingfeng Xue (Key Laboratory of Shale Gas and Geoengineering, Institute of Geology and Geophysics, Chinese Academy of Sciences) | Yibo Wang (Key Laboratory of Shale Gas and Geoengineering, Institute of Geology and Geophysics, Chinese Academy of Sciences) | Huhong Zhai (Key Laboratory of Shale Gas and Geoengineering, Institute of Geology and Geophysics, Chinese Academy of Sciences) | Xu Chang (Key Laboratory of Shale Gas and Geoengineering, Institute of Geology and Geophysics, Chinese Academy of Sciences)
- Document ID
- Society of Exploration Geophysicists
- 2018 SEG International Exposition and Annual Meeting, 14-19 October, Anaheim, California, USA
- Publication Date
- Document Type
- Conference Paper
- 2018. Society of Exploration Geophysicists
- Hydraulic fracturing, Microseismic, Fractures
- 1 in the last 30 days
- 13 since 2007
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In the shale gas development, how to efficiently acquire the dynamic expansion process of fracture during fracturing has been a problem that has perplexed academia and industry. At present, the conventional method usually uses the obtained microseismic event positioning results to carry out the qualitative analysis or uses various simulation tools to do the numerical simulation analysis. Because the presentation ability of point information is insufficient, the microseismic event location analyzing method is still having many problems. For the numerical simulation method, due to the complexity of underground media, there is always a deviation between calculation and practical issues. In this article, we focus on the data-driven approach. We transform the acoustic emission point data into the fracture trajectory data by using spatiotemporal attribute constraint. Then, the density-based trajectory clustering algorithm is introduced. Based on the spatiotemporal property constraints, the algorithm of dynamic crack propagation is proposed, and the validity of this method is verified by hydraulic fracturing experiment data.
Presentation Date: Wednesday, October 17, 2018
Start Time: 9:20:00 AM
Location: Poster Station 16
Presentation Type: Poster
|File Size||1 MB||Number of Pages||5|
Benhamou,S.,2006,Detecting an orientation component in animal paths when the preferred direction is individual-dependent:Ecology,87,518–528,10.1890/05-0495.
Fayyad,U.,G.Piatetsky-Shapiro, andP.Smyth,1996,From data mining to knowledge discovery in databases:AI Magazine,17,37,10.1609/aimag.v17i3.1230.
Technitis,G.,W.Othman,K.Safi, andR.Weibel,2015,From A to B, randomly: a point-to-point random trajectory generator for animal movement:International Journal of Geographical Information Science,29,912–934,10.1080/13658816.2014.999682.
Thornton,M., andL.Eisner,2011,Uncertainty in surface microseismic monitoring:81st Annual International Meeting, SEG,Expanded Abstracts,1524–1528,10.1190/1.3627492.
Xue,Q.,Y.Wang,H.Zhai, andX.Chang,2018,Automatic identification of fractures using a density-based clustering algorithm with time-spatial constraints:Energies,11,563,10.3390/en11030563.