waste water injection and possibly induced seismicity in ...induced seismicity consortium...
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Waste water injection and possibly induced seismicity in central California
T.H.W. Goebel1, F. Aminzadeh2, E.Hauksson1, J.-P. Ampuero1 1 Earth Sciences, Caltech, 2 Petroleum Engineering, USC
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Outline
1. Objectives of the ISC: An overview
2. Induced seismicity in central CA:
a) Identification criteria b) Fluid migration and the role of fault
structure
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Induced Seismicity Consortium
ISC purpose:
• Unique combination of expertise in earthquake seismology and petroleum engineering
• Interaction between members of industry, regulatory agencies and academia
• Advance understanding of subsurface fluid injection using empirical and theoretical approaches
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Induced Seismicity Consortium
Original source: D.Dillion
Class II injection well
Injection zone
Confining zone
Confining zone
Confining zone
Aquifer
1. larger volume 2. longer duration 3. vertically confined injection zone
Chen Q., Tiwari A., Aminzadeh F. (2014) Pacific Section American Association of Petroleum Geologists Convention,Bakersfield CA
San Joaquin Valley
Hydraulic fracturing
Source: NRC Report, (2012), Induced seismicity potential in energy technologies
Hydraulic fracturing and WD wells
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Induced Seismicity Consortium
Magnitude-frequency statistics (b-value)
Start of
injection
Sumy, et al. 2014
Temporal b-value changes
Induced seismicity, earthquake triggering & stress transfer in OK
Differences in spatial-temporal clustering between tectonic and induced earthquakes
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Stochastic fault representation and fluid injection
Hosseini et al., in prep.
Cumulative moment release Instead of 𝑀0
𝑚𝑎𝑥
Induced Seismicity Consortium
Maximum expected magnitude as fct. of injected volume
Diffusion in fractal fault network
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2. Potentially induced seismicity in central California
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Kern county, Joaquin valley*: • 74% of oil production in CA • 81% of active wells in CA • 2010 production: 148 Mbbl oil &
1,623 Mbbl water • 80% of hydraulic fracturing wells
Study Area
*Department of Conservation: ftp://ftp.consrv.ca.gov/pub/oil/annual_reports/
Los Angeles
San Fransico
*CA Division of Oil, Gas, & Geothermal Resources, Annual Prod. & Injec. Report 2012
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central US
Ellsworth, 2013
A comparison between fluid injection and seismicity in central US and CA
central US, M ≥ 3
Data: ANSS earthquake catalog Accessed through Northern California Data Center
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central US
Ellsworth, 2013
A comparison between fluid injection and seismicity in central US and CA
central US, M ≥ 3
Injection Data: California Division of Oil, Gas, & Geothermal Resources
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central CA
central US
central CA, M ≥ 2
central US, M ≥ 3
Ellsworth, 2013
A comparison between fluid injection and seismicity in central US and CA
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2b. Identification criteria
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Identification criteria: Quantitative expansion of Davis & Frohlich, 1993
1. Spatial, temporal correlation
2. Different from background seismicity, first events within a particular area
3. Pressure changes caused by injection are high enough to encourage seismicity ? ?
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Spatial correlation: WD wells and M>3 events
1983, M6.4 Coalinga
2004, M6 Parkfield
Oil fields
1980-2013
Example 2 Example 3
Example 1
Example 4 Example 5
Example 6
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Temporal correlation
Example2: Sl1
No. of Earthquakes
Inject. Rate
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Background injection and seismic activity
Earthquake Rate
Injection Rate
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Temporal and spatial proximity to well Rate-change compared to background
• Poisson background probability
• Probability of random coincidence of injection peak and seismic activity
• Significant rate-change
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Rate changes: Do injections clock-advance the following seismic events?
R = t2/(t1+t2) Inter-event time ratio:
• largely insensitive to space/time window • insensitive to secondary aftershocks • even small rate changes are detectable • suitable for plate-boundary and intra-plate
regions Felzer & Brodsky, 2005 Van Der Elst & Brodsky, 2010
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Probabilistic assessment: Example 1
Pran Ppoi
0.01 7*10-3
R-ratio
0.37 (0.42), p = 0.01
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2c. Migration patterns and fault structure
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Event migration: Pore pressure diffusion
Example 2: Rk4 & Rk6
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b-value variations during injection
Example 3: Jn05
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Coupling between fault structure and permeability
Fracture density
Fault structure schematic
Permeability
Faulkner et al. 2010
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Realistic reservoir and fault structures: Faults as fluid conduits and barriers
Hosseini et al., in prep.
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Conclusion 1. We developed a method to detect likely
induced events based on correlations between injection and seismic activity
2. Induced seismicity may show pronounced
foreshock activity over diffusive space-time scales
3. Fault structures may control diffusive processes and maximum reach of injections
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Future work
1. Both high and low b-values observed during
injection … ? statistical and physical models needed
2. Potential for fault activation as a function of injection operations and distance
3. Probability of exceeding, e.g., M>4 as a function of tectonic setting and injection volumes
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- Thank you -
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Additional Slides
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grad
ual
abru
pt
Triggering type/criteria: gradual (3 mo.) vs. abrupt (1 mo) increase in injection rates Threshold: 10-600 kbbl
Temporal correlation: a-priori defined injection activity
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Rate changes: Do injections clock-advance the following seismic events?
Percentile rate change compared to background
No
rate ch
ange
decrease increase
Incre
ase b
y factor 2
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Probabilistic assessment: Example 2, no detection
Pran Ppoi R-ratio
0.11 0.01 0.43 (0.48), p = 0.14
2955750
• long injection activity
• no significant rate change
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Well-head pressure and injected fluid-volume
Skoumal, 2014, M.Sc. Thesis Holtkamp, et al. [in review]
Cu
mu
lati
ve In
ject
ion
[kb
bl]
Cumulative Injection [kbbl]
Seismicity
Injection-stop
Kim, 2013
Injection Pressure
Cumulative Moment
Injection Volume
injection pressure decrease
Ohio
injection pressure increase
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Example 2: Long-term correlation
Example1: Eb1
Temporal correlation: Injection and seismicity rates
Eq. Rate
Inject. Rate
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Probabilistic assessment
Earthquake sequence
Pran Ppoi R-ratio
1: LH1 0.03 7*10-3 0.37 (0.42), p = 0.01
2: KR4 1*10-3 2*10-5 0.40 (0.45), p = 0.10
3: KR6 1*10-3 2*10-5 0.43 (0.48), p = 0.12
4: JT1 0.02 0.04 0.37 (0.45), p = 0.02