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Characterization of Youngstown Induced Seismicity

Characterization of Youngstown Induced Seismicity. Robert L. Walker Arman Khodabakhshnejad. Mork Family Department of Chemical Engineering and Materials Science Induced Seismicity Consortium. Background Problem Statement Correlation between Fluid Injection and Seismicity Prior Work

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Characterization of Youngstown Induced Seismicity

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  1. Characterization of Youngstown Induced Seismicity Robert L. Walker ArmanKhodabakhshnejad Mork Family Department of Chemical Engineering and Materials Science Induced Seismicity Consortium

  2. Background Problem Statement Correlation between Fluid Injection and Seismicity Prior Work Our Strategy Conclusion Outline

  3. Event Background (Credit Ohio Department of Natural Resources, 2012) (Credit USGS ,Youngstown 7½-minute quadrangle)

  4. Potential Examples of Induced Seismicity In the Continental US…. In Northeast Ohio…. 1986? (From Nicholson et. Al, 1988) 1987 & 2001? (Credit National Research Council, 2012) (From Seeber et. al, 2004)

  5. Why is This Important? (Credit Organization of American States) (From W. Y. Kim, 2008) (Credit University of Arizona)

  6. So You Want to Cause an Earthquake…. The time tested approach: • Literature has suggested that Seismic Tremors can also be generated from…. • Mining • Other Earthquakes • Geothermal Energy Generation • Dams • Hydrocarbon Reservoir Depletion • Reservoir Changes [i.e. Lake] • Nuclear Tests • Skyscraper Construction [Taipei 101] • Hydraulic Fracturing • Wastewater Disposal • Rainfall and Snowmelt (one study, at least)

  7. Mitigation: Spotlight on Ohio • It is thought that larger (M3-4) events are preceded by micro-earthquakes • Key to anticipating [and hopefully avoiding] larger events is a sensitive, robust seismometer network • Ohio has ~180 Class II injection wells, now classified as either “shallow” or “deep,” respective to a 7000 foot reference depth. • Effective Oct. 2012, all “deep” wells are required to submit a seismic monitoring plan, or if possible create their own seismic network.

  8. The Problem Unfortunately, seismometer networks may not be up to the task (Credit National Research Council)

  9. Objective Development of a model that can detect an increased likelihood of Induced Seismic events This model will take inspiration from previous correlations, as well as the proposed “Traffic Light Model” of M. D. Zoback (From M.D. Zoback, 2012)

  10. Current Thinking • For an to occur, the stress must exceed the critical shear stress on the fault: τcritical= c + (σn - p) • Function of Hydraulic Stress only Water/fluid pressure in fault = p Majer, 2011

  11. Mechanisms of Seismicity Mechanical based seismicity Lubrication based seismicity Working Hypothesis (Credit Ohio Department of Natural Resources, 2012) Youngstown Seismicity Over Time Water/fluid pressure in fault affects coefficient of friction µ • τ critical= c + µ(σn - p)

  12. Correlations Between Fluid Injection and Seismicity (Ake et. al, 2005) (Majer, 2011, Relation after McGarr, 1976)

  13. Past Seismic Records Model Development Flowchart Qualitative & Quantitative Models Injection Attributes Artificial Intelligence Adaptive NeuroFuzzy Inference System will be employed to predict seismic events Probabilistic Model PREDICTION ANFIS Generated Catalog from Gathered Data Injection data b – Value analysis from generated catalog Nearby Seismic Events B Value Analysis Pressure Fluctuations ∑ Energy Transfer Flow Rate Analogy Energy Data Analysis has yielded attributes related to seismic activity Pressure Corrected Pressure Data Lubrication Model Physically derived model will test predictive model

  14. Northstar #1 Data

  15. QC/Initial Testing: Surface Pressure Estimation

  16. QC/Initial Testing: Energy Transfer Check Looks awful lonely, doesn’t it? After removing the high energy outlier…. Hm. Guess not. Suppose there’s more to it.

  17. Conclusion • Based upon initial analysis of available data, a pattern of seismicity can be identified. • Cumulative fluid injection, injection pressure, and past seismic events can serve as fundamental components of a predictive model. • We believe that the “dual mechanism” source of seismicity may be able to explain certain patterns of seismicity and perhaps large seismic events. • If so, this pattern could as a basis for a predictive model.

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