"

This service is provided byAnhui Southland Cold & Heat Integrated Energy Co.(Click for details)title sponsor

The company's main business scope is: regional centralized cooling and heating intelligent operation, energy Internet, building and industrial energy saving, data center cooling and emergency cold source construction, IC clean room, operating room laboratory purification engineering, central air-conditioning engineering, intelligent engineering, information technology system integration and other high-tech services.



author | Siyuan Li a, Tianfu Xu a b, Zubin Chen c, Zhenjiao Jiang a b

caption | Efficient fracture network characterization in enhanced geothermal reservoirs by the integration of microseismic and borehole logs data

source (of information etc) | Geothermics

compiler | Geothermal small core (add micro-signal: geothermalAI, can get relevant information)

This is the Geothermal Energy Online AIgeothermal coreEditor's first1article


01

full text guide



A recent study presents an efficient method for characterizing fracture networks from hydraulic fracturing-induced microseismic events in enhanced geothermal reservoirs. The method includes denoising microseismic events using a density-based spatial clustering algorithm (DBSCAN), random fracture localization via a Monte Carlo algorithm, estimation of fracture sizes via planar projection, and determination of the number of fractures via elbow analysis. In this method, the fracture network can be reconstructed based on the source location of the microseismic events as well as the major geostresses detected through downhole logging and the a priori orientation of the initial fractures. The method was validated with a synthetic case consisting of two fractures and microseismic events, where the microseismic events were located with errors as high as 1.01 TP3T of the reservoir depth.The results show that the method is able to accurately determine the number of fractures and reliably estimate the fracture inclination and tilt angle with an error of less than 10°. The maximum error of the estimated fracture size is significantly lower than the noise of the microseismic event source location. In an application to a granitic hot-dry rock reservoir, two major fracture clusters were identified from more than 2,000 microseismic events: one with a large dip angle and a northeast strike, and the other with a small dip angle and a northwest strike. The fracture geometries are robustly interpreted for different a priori strike ranges. In addition, the proposed method is computationally efficient, allowing real-time generation of cracks in minute computation times. The crack network characterization method is based on the spatial distribution of denoised microseismic events. As the error in the positioning of microseismic events increases (given below 40 m in this study), the error in the generation of the fracture network also increases. In addition, microseismic events are weakly related to the hydraulic connectivity of the fracture network and the internal structure of the fractures. In order to improve the quality of the fracture network characterization to better accommodate accurate fluid and heat transport models, there is a need to further optimize the fracture network by fusing microseismic data with hydraulic test data, which will be investigated in the near future.



02

HIGHLIGHT Pictures


Fig. 1 . Algorithm flowchart of characterizing the fracture network from microseismic events cloud.

Fig. 2 . (a) Injection rates during hydraulic fracturing simulation, (b) two stimulated fractures and microseismic events distribution on fracture planes, (c ) and (d) microseismic events with white noises at the scale of 0.5% and 1.0% of burial depth, respectively.

Fig. 3 . Percentage of microseismic events fitted by fractures under varying number of fractures in responses to (a) 0.5% white noises and (b) 1.0% white noises in source locations of microseismic events; (c) and (d) best-fitted fracture networks generated from microseismic events with 0.5% and 1.0% white noises, respectively. TP3T white noises, respectively.

Fig. 4 . Percentage of microseismic events fitted by fractures under varying number of fractures in responses to the prior fracture orientation ranges estimated at (a) ±15° and (b) ±20°; (c) and (d) best-fitted fracture networks generated from microseismic events under varying prior fracture orientation ranges. (c) best-fitted fracture networks generated from microseismic events under varying prior fracture orientation ranges.

Fig. 5 . (a) Original microseismic events induced by hydraulic fracturing, (b) the microseismic events denoised by DBSCAN, (c) elbow analysis of relationship between the number of fractures and percentage of microseismic events fitted by fractures, and (d) the fracture network generated from microseismic events. events.

Fig. 6 . Fracture network characterization under the prior range of fracture strikes reduced by -6° (b), increased by 6° (d) and 12° (f), respectively, and number of fractures (a, c, e) identified stably by elbow method. respectively, and number of fractures (a, c, e) identified stably by elbow method.

Disclaimer: This article is for academic communication and dissemination only, and does not constitute investment advice

——-

References:

Alghalandis et al., 2013
Y.F. Alghalandis, P.A. Dowd, C. Xu
The RANSAC method for generating fracture networks from micro-seismic event data
Math. Geosci., 45 (2) (2013), pp. 207-224
Google Scholar

AlQassab et al., 2020
M. AlQassab, W. Yu, K. Sepehrnoori, E. Kerr, R. Scofield, A. Johnson
Estimating the size and orientation of hydraulic fractures using microseismic events
Proceedings of the SPE/AAPG/SEG Unconventional Resources Technology Conference (2020), pp. 168-179
Google Scholar

Aminzadeh et al., 2013
F. Aminzadeh, T.A. Tafti, D. Maity
An integrated methodology for sub-surface fracture characterization using microseismic data: a case study at the NW Geysers
Comput. Geosci., 54 (Apr) (2013), pp. 39-49
View PDFView articleView in ScopusGoogle Scholar

Chen et al., 2022
J. Chen, T. Xu, X. Liang, Z. Jiang
Stochastic inversion of tracer test data with seismicity constraint for permeability imaging in enhanced geothermal reservoirs
Geophysics, 87 (6) (2022), pp. M307-M319
CrossRefView in ScopusGoogle Scholar

Chen et al., 2021
Z. Chen, F. Zhao, F. Sun, H. Lu, C. Wang, H. Wu, X. Zhou
Hydraulic fracturing-induced seismicity at the hot dry rock site of the gonghe basin in China
Acta Geol. Sin. Engl. Ed., 95 (6) (2021), pp. 1835-1843
CrossRefView in ScopusGoogle Scholar

Cornet, 2000
F.H. Cornet
Comment on 'Large-scale in situ permeability tensor of rocks from induced microseismicity' by S. A. Shapiro, P. Audigane and J.J. Royer
Geophys. J. Int., 140 (2) (2000), pp. 465-469
Google Scholar

Cornette et al., 2012
B.M. Cornette, C. Telker, A. De La Pena
Refining discrete fracture networks with surface microseismic mechanism inversion and mechanism-driven event location
Proceedings of the SPE Hydraulic Fracturing Technology Conference (2012)
Google Scholar

Dershowitz et al., 1998
W. Dershowitz, J. Geier, P.R. LaPointe
FracMan: interactive discrete feature data analysis, geometric modelling and exploration simulation. user documentation, Golder Associates Inc. Seattle (1998)
Google Scholar

Didana et al., 2017
Y.L. Didana, G. Heinson, S. Thiel, L. Krieger
Magnetotelluric monitoring of permeability enhancement at enhanced geothermal system project
Geothermics, 66 (2017), pp. 23-38
View PDFView articleView in ScopusGoogle Scholar

Dorbath et al., 2009
L. Dorbath, N. Cuenot, A. Genter, M. Frogneux
Seismic response of the fractured and faulted granite of Soultz-sous-Forêts (France) to 5km deep massive water injections
Geophys. J. Int., 177 (2) (2009), pp. 653-675
View in ScopusGoogle Scholar

Ester et al., 1996
M. Ester, H.-P. Kriegel, J. Sander, X. Xu
A density-based algorithm for discovering clusters in large spatial databases with noise
Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, AAAI Press, Portland, Oregon (1996), pp. 226-231
Google Scholar

Fan et al., 2020
H. Fan, S. Li, X.T. Feng, X. Zhu
A high-efficiency 3D boundary element method for estimating the stress/displacement field induced by complex fracture networks
J. Pet. Sci. Eng., 187 (2020), Article 106815
View PDFView articleView in ScopusGoogle Scholar

Frash et al., 2015
L.P. Frash, M. Gutierrez, J. Hampton
Laboratory-scale-model testing of well stimulation by use of mechanical-impulse hydraulic fracturing
SPE J., 20 (3) (2015), pp. 536-549
View in ScopusGoogle Scholar

Guo, 2016
L. Guo
Test and Model Research of Hydraulc Fracturing and Reservoir Damage Evolution in Enhanced Geothermal System
Jilin University (2016)
in Chinese
Google Scholar

Hu et al., 2014
J.L. Hu, Z.H. Kang, L.L. Yuan
Automatic fracture identification using ant tracking in tahe oilfield
Adv. Mater. Res., 962-965 (2014), pp. 556-559
View in ScopusGoogle Scholar

Illman, 2014
W.A. Illman
Hydraulic tomography offers improved imaging of heterogeneity in fractured rocks
Groundwater, 52 (5) (2014), pp. 659-684
CrossRefView in ScopusGoogle Scholar

Kodinariya and Makwana, 2013
T.M. Kodinariya, P.R.D. Makwana
Review on determining of cluster in K-means clustering
Int. J. Adv. Res. Comput. Sci. Manag. Stud., 1 (6) (2013), pp. 90-95
Google Scholar

Koepke et al., 2020
R. Koepke, E. Gaucher, T. Kohl
Pseudo-probabilistic identification of fracture network in seismic clouds driven by source parameters
Geophys. J. Int., 223 (3) (2020), pp. 2066-2084
CrossRefView in ScopusGoogle Scholar

Lei, 2020
Z. Lei
Study On the Characteristics of Hot Dry Rock Reservoir and Fracturing Test Model in the Gonghe Basin, Qinghai Province
Jilin University (2020)
in Chinese
Google Scholar

Long et al., 1985
J.C.S. Long, P. Gilmour, P.A. Witherspoon
A model for steady fluid flow in random three-dimensional networks of disc-shaped fractures
Water Resour. Res., 21 (8) (1985), pp. 1105-1115
View in ScopusGoogle Scholar

Lu et al., 2019
H. Lu, Q. Shen, J. Chen, X. Wu, X. Fu
Parallel multiple-chain DRAM MCMC for large-scale geosteering inversion and uncertainty quantification
J. Pet. Sci. Eng., 174 (2019), pp. 189-200
View PDFView articleView in ScopusGoogle Scholar

MacFarlane et al., 2014
J. MacFarlane, S. Thiel, J. Pek, J. Peacock, G. Heinson
Characterization of induced fracture networks within an enhanced geothermal system using anisotropic electromagnetic modelling
J. Volcanol. Geotherm. Res., 288 (2014), pp. 1-7
View PDFView articleView in ScopusGoogle Scholar

Matas and Chum, 2004
J. Matas, O. Chum
Randomized RANSAC with T_(d,d) test
Image Vis. Comput., 22 (10) (2004), pp. 837-842
View PDFView articleView in ScopusGoogle Scholar

Maxwell et al., 2002
S.C. Maxwell, T.I. Urbancic, N. Steinsberger, R. Zinno
Microseismic imaging of hydraulic fracture complexity in the barnett shale
Proceedings of the SPE Annual Technical Conference and Exhibition, San Antonio, Texas (2002)
Google Scholar

McKean et al., 2019
S.H. McKean, J.A. Priest, J. Dettmer, D.W. Eaton
Quantifying fracture networks inferred from microseismic point clouds by a gaussian mixture model with physical constraints
Geophys. Res. Lett., 46 (20) (2019), pp. 11008-11017
CrossRefView in ScopusGoogle Scholar

Nguyen et al., 2001
T.S. Nguyen, L. Borgesson, M. Chijimatsu, J. Rutqvist, L. Jing
Hydro-mechanical response of a fractured granitic rock mass to excavation of a test pit - the kamaishi mine experiment in Japan
Int. J. Rock Mech. Min. Sci., 38 (1) (2001), pp. 79-94
View PDFView articleView in ScopusGoogle Scholar

Peacock et al., 2013
J.R. Peacock, S. Thiel, G.S. Heinson, P. Reid
Time-lapse magnetotelluric monitoring of an enhanced geothermal system
Geophysics, 78 (3) (2013), pp. B121-B130
CrossRefView in ScopusGoogle Scholar

Peacock et al., 2012
J.R. Peacock, S. Thiel, P. Reid, G. Heinson
Magnetotelluric monitoring of a fluid injection: example from an enhanced geothermal system
Geophys. Res. Lett., 39 (17) (2012), pp. 1-5
L18403
Google Scholar

Redhaounia et al., 2016
B. Redhaounia, M. Bedir, H. Gabtni, B. Ilondo, M. Dhaoui, A. Chabaane, S. Khomsi
Hydro-geophysical characterization for groundwater resources potential of fractured limestone reservoirs in Amdoun Monts (North-western Tunisia)
J. Appl. Geophy., 128 (2016), pp. 150-162
View PDFView articleView in ScopusGoogle Scholar

Rose et al., 2009
P. Rose, K. Leecaster, P. Drakos, A. Robertson-Tait
Tracer testing at the desert peak EGS project
Trans. Geotherm. Resour. Counc., 33 (2009), pp. 217-220
View in ScopusGoogle Scholar

Sanjuan et al., 2010
B. Sanjuan, R. Millot, C. Dezayes, M. Brach
Main characteristics of the deep geothermal brine (5km) at Soultz-sous-Forêts (France) determined using geochemical and tracer test data
C.R. Geosci., 342 (7-8) (2010), pp. 546-559
View PDFView articleCrossRefView in ScopusGoogle Scholar

Sanjuan et al., 2006
B. Sanjuan, J.L. Pinault, P. Rose, A. Gerard, M. Brach, G. Braibant, C. Crouzet, J.C. Foucher, A. Gautier, S. Touzelet
Tracer testing of the geothermal heat exchanger at Soultz-sous-Forêts (France) between 2000 and 2005
Geothermics, 35 (5-6) (2006), pp. 622-653
View PDFView articleView in ScopusGoogle Scholar

Shapiro et al., 2002
S.A. Shapiro, E. Rothert, V. Rath, J. Rindschwentner
Characterization of fluid transport properties of reservoirs using induced microseismicity
Geophysics, 67 (1) (2002), pp. 212-220
View in ScopusGoogle Scholar

Tan and He, 2016
Y. Tan, C. He
Improved methods for detection and arrival picking of microseismic events with low signal-to-noise ratios
Geophysics, 81 (2) (2016), pp. KS93-KS111
CrossRefGoogle Scholar

Tiedeman and Barrash, 2020
C.R. Tiedeman, W. Barrash
Hydraulic tomography: 3D hydraulic conductivity, fracture network, and connectivity in mudstone
Groundwater, 58 (2) (2020), pp. 238-257
CrossRefView in ScopusGoogle Scholar

Williams-Stroud et al., 2013
S. Williams-Stroud, C. Ozgen, R.L. Billingsley
Microseismicity-constrained discrete fracture network models for stimulated reservoir simulation
Geophysics, 78 (1) (2013), pp. B37-B47
CrossRefView in ScopusGoogle Scholar

Xu et al., 2022
T. Xu, X. Liang, Y. Xia, Z. Jiang, F. Gherardi
Performance evaluation of the Habanero enhanced geothermal system, Australia: optimization based on tracer and induced micro-seismicity data
Renew. Energy, 181 (2022), pp. 1197-1208
View PDFView articleView in ScopusGoogle Scholar

Xue et al., 2018
Q. Xue, Y. Wang, H. Zhai, X. Chang
Automatic identification of fractures using a density-based clustering algorithm with time-spatial constraints
Energies, 11 (3) (2018), p. 563
CrossRefView in ScopusGoogle Scholar

Yu et al., 2021
J. Yu, J. Byun, S.J. Seol
Imaging discrete fracture networks using the location and moment tensors of microseismic events
Explor. Geophys., 52 (1) (2021), pp. 42-53
CrossRefView in ScopusGoogle Scholar

Yu et al., 2016
X. Yu, J. Rutledge, S. Leaney, S. Maxwell
Discrete-fracture-network generation from microseismic data by use of moment-tensor-and event-location-constrained hough transforms
SPE J., 21 (1) (2016), pp. 221-232
View in ScopusGoogle Scholar

Zhang et al., 2018
C. Zhang, G. Jiang, Y. Shi, Z. Wang, Y. Wang, S. Li, X. Jia, S. Hu
Terrestrial heat flow and crustal thermal structure of the Gonghe-Guide area, northeastern Qinghai-Tibetan plateau
Geothermics, 72 (2018), pp. 182-192
View PDFView articleGoogle Scholar

Zhao et al., 2019
Y. Zhao, T. Yang, P. Zhang, H. Xu, J. Zhou, Q. Yu
Method for generating a discrete fracture network from microseismic data and its application in analyzing the permeability of rock masses: a case study
Rock Mech. Rock Eng., 52 (9) (2019), pp. 3133-3155
CrossRefView in ScopusGoogle Scholar

Zheng et al., 2014
J. Zheng, J. Deng, X. Yang, J. Wei, H. Zheng, Y. Cui
An improved Monte Carlo simulation method for discontinuity orientations based on Fisher distribution and its program implementation
Comput. Geotech., 61 (2014), pp. 266-276
View PDFView articleView in ScopusGoogle Scholar