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CHARACTERIZING BEDDING-PARALLEL
FRACTURES IN SHALE:
Aperture-size distributions and spatial organization
Qiqi Wang1,2 and Julia F. W. Gale2
1. Jackson School of Geosciences, University of Texas at Austin
2. Bureau of Economic Geology, University of Texas at Austin
Opening-mode
Bed-parallel
fracture with
fibrous calcite
cement
500 µm
Thickest fracture: 8.7 cm
Thinnest fracture: ~15 µm (SEM image) Greatest lateral extent: > 32.6 m
Calcareous
concretion
Bed-Parallel Fractures
Motivation
• Bed-parallel fractures are common in shales and may
impact fluid flow in hydrocarbon reservoirs and
propagation of hydraulic fractures
- hydraulic fracture height growth inhibition
- hydraulic fracture horizontal propagation
• No published systematic studies of size scaling and
spatial distribution of bed-parallel fractures.
• Knowing the aperture-size scaling and spatial
organization of bed-parallel fractures will contribute to
modeling of fracture networks
- They are important for quantifying the
mechanical behavior of fractured rock masses
- Fracture density, size, and orientation obtained
from field and core help defining fracture
distributions when simulating stochastic fracture
network
Motivation
Locations
Vaca Muerta outcrop and well
locations
- 1 field area including 3 Vaca Muerta
outcrop
- 5 wells; datasets including core scan
images, sampled cores, and well-logs
Field Area
50 Km
Marcellus Fm.
- 2 wells datasets including
continuous core
(2 vertical scanlines)
Locations
Wolfcamp shaleMarcellus shale
Wolfcamp Fm.
- 1 well dataset including
continuous core
(1 vertical scanline)
Scanline Method
Field data Direct core measurement
Core scan mosaic data
Spatial Organization
• Where do fractures occur within the rock column?
• Are they more likely to occur within certain
lithology than others?
Spatial Organization Hypotheses:
• Bed-parallel fractures are more intense in
organic-rich layers
• Bed-parallel fractures form preferentially along
mechanical interfaces
GR TOC-KerogenLab TOC
Fracture intensity
Fracs/m
Intensity - Core vs. Log
• Lab-TOC spikes
match with 4 major
fracture intensity
peaks.
• TOC-KER is also a
good match to
intensity, but
generally not as
well matched as
lab TOC.
TOC-Kerogen: Amount of TOC based on Kerogen volume. Estimated from GR, sonic, density,
neutron and NMR well log data
0 20
Another well:
At some depth high TOC
correlate to high intensity
but not always the case.
No lab TOC data for this
depth column.
Intensity - Core vs. LogTOC-Kerogen
Core 2
Photos of fractures along interfaces
BPF associated
with cncretions
BPF associated
with host-rock
lithology change
concretion
concretion
Spatial Organization and Interfaces
occurrence % of material
interfaces with
fracture
occurrence
wells Total # of
fractures
At material
interfaces
At lithology
change
At concretion
Vaca Muerta well #1 341 105 (30.7%) 35 71 64%
Vaca Muerta well #2 229 55
(24.0%)
44 11 76%
Vaca Muerta well #3 142 44
(30.9%)
44 N/A 70%
• Material interfaces includes abrupt lithology change and concretion
margins
• 25-30% of the bed-parallel fractures occur at material interfaces.
• ~ 65-75% of material interfaces have bed-parallel fractures
Aperture Size Population
1mm
5
c
m
0.05mm
Aperture-size scaling hypotheses
For bed-parallel fractures aperture-size scaling :
• is power-law
– Vertical fracture aperture-size distribution is
commonly power-law
• follows a different function (e.g. exponential)
• has preferred size(s)
• follows no pattern
VM_Well #1
340 fracs,
73.28 m
Best fit:
Negative Exponential
Correlation Coefficient:
R²=0.9923
From core scan mosaics
Results
Marcellus_Well #2
82 fracs,
163.46 m
Best fit:
Negative Exponential
Correlation Coefficient:
R²=0.9348
From direct observation of core
Results
VM_Field #2
54 fracs,
13.2m
Best fit:
Negative Exponential
Correlation Coefficient:
R²=0.9801
From outcrop
Results
Results
Core/Well # Scanline Length
(m)
# of fracs Best Fit Model
(N.E. or P.L)
Best Fit Equation Correlation
Coefficient
(R²)
VM_Well #1 73.28 340 N.E. y = 4.6538e-0.655x 0.9923
VM_Well #2 34.69 230 N.E. y = 6.2313e-0.465x 0.9744
VM_Well #3 28.00 142 N.E. y = 6.3e-0.663x 0.9939
VM-Well #4 18.99 12 P.L. (?) y = 0.3317x-1.143 0.9537
VM_Field #1 17.84 88 N.E. y = 3.5994e-0.056x 0.9507
VM_Field #2 13.20 54 N.E. y = 3.9522e-0.067x 0.9801
VM_Field #3 48.46 30 N.E. (?) y = 0.5092e-0.224x 0.9549
Marcellus Well #1 89.18 47 N.E. y = 0.3543e-0.825x 0.918
Marcellus Well #2 163.46 82 N.E. y = 0.3386e-0.492x 0.9348
Wolfcamp Well #1 141.88 68 N.E. (?) y = 0.3557e-0.185x 0.9165
Total 628.98 1093 N.E.
• Fracture Intensity:
VM>Wolfcamp>Marc
ellus
• Vaca Muerta core
data contains large
number of thin
fractures but lacks
thick ones.
• Vaca Muerta outcrops
have high number of
thick fractures
Discussion
0.00001
0.0001
0.001
0.01
0.1
1
10
100
0.01 0.1 1 10 100
CumulativeFrequency(Fracs/m)
Fracture Aperture Size (mm)
Compiled Aperture Size Distribution Plot (10 datasets)
MarcellusWell
#1
MarcellusWell
#2
VM_Well #1
VN_Well #2
VM_Well #3
VM_Well #4
VM_Field #1
VM_Field #2
3/7/19
vertical E-W
fracturesin tuff
VM_Field_V-Frac
#1
VM_Field_V-Frac
#2
VM_Field_V-Frac
#3
• Data collected indicate bed-parallel fracture aperture size
distribution follows a negative exponential distribution.
• Studied bed-parallel fractures are more intense in organic-rich layers
in some cases, but not in others.
• 25-30% of the bed-parallel fractures occur at material interfaces.
• ~65-75% of material interfaces have bed-parallel fractures
Conclusions
Questions?
References
Bonnet, E., O. Bour, N. E. Odling, P. Davy, I. Main, P. Cowie, and B. Berkovitz, 2001, Scaling of
fracture systems in geological media: Reviews of Geophysics, v. 39, no. 3, p. 347–383.
Cobbold, P.R. and N. Rodrigues, 2007, Seepage forces, important factors in the formation of
horizontal hydraulic fractures and bedding-parallel fibrous veins (‘beef ’ and ‘cone-in- cone’):
Geofluids, 7, p. 313–332.
Gomez, L.A., Laubach, S.E., 2005, Rapid digital quantification of microfracture populations, Journal
of Structural Geology, 28 (2006) 408–420.
Gale, J. F. W., Ukar, E., Elliott, S., and Wang, Q., 2015, Bedding-parallel fractures in shales:
characterization, prediction and importance (abs.): AAPG Annual Meeting, Denver, Colorado.
Ortega, O., 2002. Fracture-size scaling and stratigraphic controls on fracture intensity. Ph.D.
dissertation, The University of Texas at Austin.
Ortega, O., Marrett, R., Laubach, S.E., 2006, A scale-independent approach to fracture intensity
and average spacing measurement, AAPG bulletin, V. 90, Issue. 2, p. 193-208.
Rodrigues, N., P. R. Cobbold, H. Loseth and G. Ruffet, 2009, Widespread Bedding-parallel Veins of
Fibrous Calcite (‘beef’) in mature source rock (Vaca Muerta Fm, Neuquen Basin, Argentina):
Evidence for overpressure and horizontal compression: Journal of the Geological Society,
London, v. 166, p. 695-709.
y = 0.3557e-0.185x
R² = 0.9165
y = 0.2069x-0.458
R² = 0.8824
0.01
0.1
1
0.01 0.1 1 10 100
CumulativeFrequency(Fracs/m)
Fracture Aperture Size (mm)
Aperture Size Distribution
Wolfcamp_Wiggo Expon. (Wolfcamp_Wiggo)
y = 0.2396x-0.44
R² = 0.9917
y = 0.4723e-0.201x
R² = 0.9978
0.01
0.1
1
0.01 0.1 1 10 100
CumulativeFrequency(frac/m)
Fracture Aperture Size (mm)
Aperture Size DIstribution
Wolfcamp Wiggo-Power Wolfcamp Wiggo-Exponential
Power (Wolfcamp Wiggo-Power) Expon. (Wolfcamp Wiggo-Exponential)
Chracterizing bed-parallel fractures in shale

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Chracterizing bed-parallel fractures in shale

  • 1. CHARACTERIZING BEDDING-PARALLEL FRACTURES IN SHALE: Aperture-size distributions and spatial organization Qiqi Wang1,2 and Julia F. W. Gale2 1. Jackson School of Geosciences, University of Texas at Austin 2. Bureau of Economic Geology, University of Texas at Austin
  • 2. Opening-mode Bed-parallel fracture with fibrous calcite cement 500 µm Thickest fracture: 8.7 cm Thinnest fracture: ~15 µm (SEM image) Greatest lateral extent: > 32.6 m Calcareous concretion Bed-Parallel Fractures
  • 3. Motivation • Bed-parallel fractures are common in shales and may impact fluid flow in hydrocarbon reservoirs and propagation of hydraulic fractures - hydraulic fracture height growth inhibition - hydraulic fracture horizontal propagation • No published systematic studies of size scaling and spatial distribution of bed-parallel fractures.
  • 4. • Knowing the aperture-size scaling and spatial organization of bed-parallel fractures will contribute to modeling of fracture networks - They are important for quantifying the mechanical behavior of fractured rock masses - Fracture density, size, and orientation obtained from field and core help defining fracture distributions when simulating stochastic fracture network Motivation
  • 5. Locations Vaca Muerta outcrop and well locations - 1 field area including 3 Vaca Muerta outcrop - 5 wells; datasets including core scan images, sampled cores, and well-logs Field Area 50 Km
  • 6. Marcellus Fm. - 2 wells datasets including continuous core (2 vertical scanlines) Locations Wolfcamp shaleMarcellus shale Wolfcamp Fm. - 1 well dataset including continuous core (1 vertical scanline)
  • 7. Scanline Method Field data Direct core measurement Core scan mosaic data
  • 8. Spatial Organization • Where do fractures occur within the rock column? • Are they more likely to occur within certain lithology than others?
  • 9. Spatial Organization Hypotheses: • Bed-parallel fractures are more intense in organic-rich layers • Bed-parallel fractures form preferentially along mechanical interfaces
  • 10. GR TOC-KerogenLab TOC Fracture intensity Fracs/m Intensity - Core vs. Log • Lab-TOC spikes match with 4 major fracture intensity peaks. • TOC-KER is also a good match to intensity, but generally not as well matched as lab TOC. TOC-Kerogen: Amount of TOC based on Kerogen volume. Estimated from GR, sonic, density, neutron and NMR well log data 0 20
  • 11. Another well: At some depth high TOC correlate to high intensity but not always the case. No lab TOC data for this depth column. Intensity - Core vs. LogTOC-Kerogen Core 2
  • 12. Photos of fractures along interfaces BPF associated with cncretions BPF associated with host-rock lithology change concretion concretion
  • 13. Spatial Organization and Interfaces occurrence % of material interfaces with fracture occurrence wells Total # of fractures At material interfaces At lithology change At concretion Vaca Muerta well #1 341 105 (30.7%) 35 71 64% Vaca Muerta well #2 229 55 (24.0%) 44 11 76% Vaca Muerta well #3 142 44 (30.9%) 44 N/A 70% • Material interfaces includes abrupt lithology change and concretion margins • 25-30% of the bed-parallel fractures occur at material interfaces. • ~ 65-75% of material interfaces have bed-parallel fractures
  • 15. Aperture-size scaling hypotheses For bed-parallel fractures aperture-size scaling : • is power-law – Vertical fracture aperture-size distribution is commonly power-law • follows a different function (e.g. exponential) • has preferred size(s) • follows no pattern
  • 16. VM_Well #1 340 fracs, 73.28 m Best fit: Negative Exponential Correlation Coefficient: R²=0.9923 From core scan mosaics Results
  • 17. Marcellus_Well #2 82 fracs, 163.46 m Best fit: Negative Exponential Correlation Coefficient: R²=0.9348 From direct observation of core Results
  • 18. VM_Field #2 54 fracs, 13.2m Best fit: Negative Exponential Correlation Coefficient: R²=0.9801 From outcrop Results
  • 19. Results Core/Well # Scanline Length (m) # of fracs Best Fit Model (N.E. or P.L) Best Fit Equation Correlation Coefficient (R²) VM_Well #1 73.28 340 N.E. y = 4.6538e-0.655x 0.9923 VM_Well #2 34.69 230 N.E. y = 6.2313e-0.465x 0.9744 VM_Well #3 28.00 142 N.E. y = 6.3e-0.663x 0.9939 VM-Well #4 18.99 12 P.L. (?) y = 0.3317x-1.143 0.9537 VM_Field #1 17.84 88 N.E. y = 3.5994e-0.056x 0.9507 VM_Field #2 13.20 54 N.E. y = 3.9522e-0.067x 0.9801 VM_Field #3 48.46 30 N.E. (?) y = 0.5092e-0.224x 0.9549 Marcellus Well #1 89.18 47 N.E. y = 0.3543e-0.825x 0.918 Marcellus Well #2 163.46 82 N.E. y = 0.3386e-0.492x 0.9348 Wolfcamp Well #1 141.88 68 N.E. (?) y = 0.3557e-0.185x 0.9165 Total 628.98 1093 N.E.
  • 20. • Fracture Intensity: VM>Wolfcamp>Marc ellus • Vaca Muerta core data contains large number of thin fractures but lacks thick ones. • Vaca Muerta outcrops have high number of thick fractures Discussion
  • 21. 0.00001 0.0001 0.001 0.01 0.1 1 10 100 0.01 0.1 1 10 100 CumulativeFrequency(Fracs/m) Fracture Aperture Size (mm) Compiled Aperture Size Distribution Plot (10 datasets) MarcellusWell #1 MarcellusWell #2 VM_Well #1 VN_Well #2 VM_Well #3 VM_Well #4 VM_Field #1 VM_Field #2 3/7/19 vertical E-W fracturesin tuff VM_Field_V-Frac #1 VM_Field_V-Frac #2 VM_Field_V-Frac #3
  • 22. • Data collected indicate bed-parallel fracture aperture size distribution follows a negative exponential distribution. • Studied bed-parallel fractures are more intense in organic-rich layers in some cases, but not in others. • 25-30% of the bed-parallel fractures occur at material interfaces. • ~65-75% of material interfaces have bed-parallel fractures Conclusions
  • 24. References Bonnet, E., O. Bour, N. E. Odling, P. Davy, I. Main, P. Cowie, and B. Berkovitz, 2001, Scaling of fracture systems in geological media: Reviews of Geophysics, v. 39, no. 3, p. 347–383. Cobbold, P.R. and N. Rodrigues, 2007, Seepage forces, important factors in the formation of horizontal hydraulic fractures and bedding-parallel fibrous veins (‘beef ’ and ‘cone-in- cone’): Geofluids, 7, p. 313–332. Gomez, L.A., Laubach, S.E., 2005, Rapid digital quantification of microfracture populations, Journal of Structural Geology, 28 (2006) 408–420. Gale, J. F. W., Ukar, E., Elliott, S., and Wang, Q., 2015, Bedding-parallel fractures in shales: characterization, prediction and importance (abs.): AAPG Annual Meeting, Denver, Colorado. Ortega, O., 2002. Fracture-size scaling and stratigraphic controls on fracture intensity. Ph.D. dissertation, The University of Texas at Austin. Ortega, O., Marrett, R., Laubach, S.E., 2006, A scale-independent approach to fracture intensity and average spacing measurement, AAPG bulletin, V. 90, Issue. 2, p. 193-208. Rodrigues, N., P. R. Cobbold, H. Loseth and G. Ruffet, 2009, Widespread Bedding-parallel Veins of Fibrous Calcite (‘beef’) in mature source rock (Vaca Muerta Fm, Neuquen Basin, Argentina): Evidence for overpressure and horizontal compression: Journal of the Geological Society, London, v. 166, p. 695-709.
  • 25. y = 0.3557e-0.185x R² = 0.9165 y = 0.2069x-0.458 R² = 0.8824 0.01 0.1 1 0.01 0.1 1 10 100 CumulativeFrequency(Fracs/m) Fracture Aperture Size (mm) Aperture Size Distribution Wolfcamp_Wiggo Expon. (Wolfcamp_Wiggo) y = 0.2396x-0.44 R² = 0.9917 y = 0.4723e-0.201x R² = 0.9978 0.01 0.1 1 0.01 0.1 1 10 100 CumulativeFrequency(frac/m) Fracture Aperture Size (mm) Aperture Size DIstribution Wolfcamp Wiggo-Power Wolfcamp Wiggo-Exponential Power (Wolfcamp Wiggo-Power) Expon. (Wolfcamp Wiggo-Exponential)