Challenges and Strategies for Monitoring Induced Seismic Activity

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1 Challenges and Strategies for Monitoring Induced Seismic Activity Designing and operating induced seismic monitoring networks to meet regulations Dario Baturan Geophysical Society of Tulsa 2015

2 Introduction Induced seismicity is a well-known phenomenon Felt events are rare Number of felt event examples in Alberta, BC, Ohio and Oklahoma Following increased public awareness a number of regulations and protocols have been put in place to mitigate risk associated with induced seismicity Regulations to date have the following common points: Characterize the risk of induced seismicity ahead of time Establish local seismic monitoring Develop and be ready to implement a mitigation plan (driven by seismic monitoring network data products)

3 ISM Network Challenges Network design Monitoring protocol robustness Magnitude type Instrumentation ISM ArrayApplications How many stations are required to meet magnitude or location accuracy requirements at minimum cost? When does a monitoring network not meet its mandate? What is the best magnitude scale and equation to use in magnitude-based traffic light protocols? Which instrument types have the most appropriate characteristics suitable for ISM and whatimpact do they have on the accuracy of data products? Are there benefits for ISM networks and data sets beyond regulatory compliance?

4 Nanometrics Example Regulations Key Points Alberta Energy Regulator Subsurface Order # 2 Continuous monitoring 24/7 traffic light system Magnitude scale: Local (Richter) ML Yellow light: M2.0, Red light <4.0, Mc <M2.0 Location uncertainty: better than 5 km Defines specific monitoring region AER has a 10-station backbone monitoring network Ohio Department of Natural Resources Applies to wells within 3 miles of a known fault or area of seismic activity with M>2.0 Continuous monitoring 24/7 system Pause operations if >M1.0 detected and investigate Location uncertainty: unspecified Backbone network in place Ohio Seismic Network How do we ensure seismic monitoring network will meet the criteria set out by the regulator?

5 Network Design and Performance Modeling Event Detection SNR Event Spectra Brune Modeling Number of Stations and Distribution Area of interest: Location uncertainty Magnitude of completeness Velocity Model Noise Field

6 Network Performance Modeling OGS Network Modeling results for the network Oklahoma Geological Survey uses to locate earthquakes within the state: 18 stations (with data available on IRIS DMC) Mc of 1.8 (OKC) and up Epicentral location uncertainty ~3 km (OKC) and up Magnitude of Completeness (Mc) Mc = ~2.2 Verification using the actual OGS 2015 catalog Epicentral uncertainty (km)

7 Magnitude Scales Local (Richter) Magnitude Scale, M L Proportional to peak ground motion displacement scaled by hypocentral distance Easy to compute Seismology standard Requires regional distance correction factors Site amplification can have significant effect Moment Magnitude Scale, Mw Based on the scalar moment and related to the physical properties of the earthquake Computationally more expensive Microseismic monitoring standard Several computation methods Can account for radiation pattern Requires sufficient SNR below Earthquake (time) Grant County - Oklahoma Default M L OGS M W USGS RMT M W 30/11/ :49:

8 Magnitude Scale Calibration - Oklahoma All magnitude scales contain attenuation (distance) correction terms In M L scale, distance correction terms come from Southern/Northern California Regional calibration required in some cases Oklahoma M L calibration study Empirical approach requires the presence of high-quality data sets facilitated through data sharing Earthquake (time) Grant County - Oklahoma Default M L Oklahoma M L OGS M W NMX-USGS M W 30/11/ :49:

9 Instrumentation Induced seismicity characteristics: Recorded at epicentral distances ~ 4 to 30 km Events in magnitude range ~ 0 to 4.5 Frequency range of interest ~ 0.1 to 30 Hz Appropriate instrumentation evaluation criteria: Clip level Noise floor Frequency response Maxwell (2013)

10 Manage Yellow Traffic Lights Gutenberg-Richter Law 10 Seismicity rate and b-value Increase in seismicity rate = increase in probability of occurrence of larger earthquakes Correlation between fluctuation in b-value and probability of occurrence of larger earthquakes Need rich data set for b-value computation Increase in seismicity Mc B-value Red light threshold earthquake Yellow light threshold earthquakes

11 Applications of Induced Seismic Data Sets Identify (delineate) new fault structures Refine maps of existing fault structures Perform local stress field inversion and estimate SH max direction Get a better understanding of the fracture mechanics during hydraulic fracturing Assist in evaluating completions efficiency Calibrate Magnitude equations Ground Motion Prediction Equations Site amplification maps Reduce uncertainty

12 Summary Meet monitoring criteria out of the gate Report the most robust magnitude scale Select appropriate instrumentation for the job Important to build robust monitoring protocols Potential uses of rich induced seismic monitoring data sets Model network performance Use calibrated M L /Mw Use RMT Mw for large events Robust BB seismometers Account for station outages Manage yellow traffic light Manage operations Improve data product accuracy Map fault structures

13 Thank You Dario Baturan Director, Technical Operations