Array Processing in Microseismic Monitoring: Detection, Enhancement, and Localization of Induced Seismicity

Current development of unconventional resources (such as shale gas, shale oil, and tight sands) requires hydraulic fracturing, which involves injecting fluid at high pressure into the subsurface reservoir. Such injections (or fluid production) cause stress changes in the reservoir. These stress changes often result in failure of the rocks with a concurrent release of seismic energy as seismic waves. The nature of seismic wave propagation in the subsurface media is complex. Based on the direction of propagation and the particle motion, body waves can be classified into P-waves (primary, compressional wave) and S-waves (secondary, shear wave). Passive seismic monitoring is based on recording these emitted waves and then using signal processing to extract characteristics such as amplitude, polarity, and arrival time, from which it is then feasible to estimate the location and character of the failure events.

[1]  D. Eaton,et al.  Breakdown of the Gutenberg‐Richter relation for microearthquakes induced by hydraulic fracturing: influence of stratabound fractures , 2014 .

[2]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[3]  James H. McClellan,et al.  An Automatic Arrival Time Picking Method Based on RANSAC Curve Fitting , 2016 .

[4]  Mauricio D. Sacchi,et al.  Simultaneous recovery of origin time, hypocentre location and seismic moment tensor using sparse representation theory , 2012 .

[5]  Michael Fehler,et al.  Petroleum reservoir characterization using downhole microseismic monitoring , 2010 .

[6]  V. Oye,et al.  Automated microearthquake location using envelope stacking and robust global optimization , 2010 .

[7]  P. Roux,et al.  Increasing the accuracy of microseismic monitoring using surface patch arrays and a novel processing approach , 2014 .

[8]  James H. McClellan,et al.  A multi-channel approach for automatic microseismic event localization using RANSAC-based arrival time event clustering(RATEC) , 2017, ArXiv.

[9]  Brian Fuller,et al.  Diffraction Processing of Downhole Passive Monitoring Data to Image Hydrofracture Locations , 2007 .

[10]  Ted Shuck,et al.  Surface microseismic in an extreme environment , 2015 .

[11]  Detection and analysis of microseismic events using a Matched Filtering Algorithm (MFA) , 2016 .

[12]  S. Lasocki,et al.  Violations of Gutenberg–Richter Relation in Anthropogenic Seismicity , 2016, Pure and Applied Geophysics.

[13]  C. Chapman.  Fundamentals of Seismic Wave Propagation: Frontmatter , 2004 .

[14]  Leo Eisner,et al.  Comparison of surface and borehole locations of induced seismicity , 2010 .

[15]  Václav Vavryčuk,et al.  On the retrieval of moment tensors from borehole data , 2007 .

[16]  James H. McClellan,et al.  Iterative interferometry-based method for picking microseismic events , 2017, 1703.03154.

[17]  A. Kushnir,et al.  Statistically Optimal Technique of Simultaneous Event Location and Focal Mechanism Determination of Weak Microseismicity Using Surface Arrays , 2012 .

[18]  James H. McClellan,et al.  Enhancement of Microseismic Events Using Tensor Decomposition and Time-frequency Representation , 2017 .

[19]  Lijun Zhu,et al.  Microseismic events enhancement and detection in sensor arrays using autocorrelation‐based filtering , 2016, ArXiv.

[20]  L. Eisner,et al.  DOWNHOLE MICROSEISMIC MONITORING OF SHALE DEPOSITS : CASE STUDY FROM NORTHERN POLAND , 2017 .

[21]  B. Gutenberg,et al.  Frequency of Earthquakes in California , 1944, Nature.

[22]  M. Fink,et al.  Time reversal acoustics , 1996, IEEE Ultrasonics Symposium, 2004.

[23]  Jan Valenta,et al.  Semblance for Microseismic Event Detection , 2014 .

[24]  Microseismic event detection: Comparing P-wave migration with P- and S-wave cross-correlation , 2014 .

[25]  Leo Eisner,et al.  Simultaneous microseismic event localization and source mechanism determination , 2015 .

[26]  Azzedine Zerguine,et al.  Automated SVD filtering of time-frequency distribution for enhancing the SNR of microseismic/microquake events , 2016 .

[27]  Emmanuel Gaucher,et al.  Constrained tomography of realistic velocity models in microseismic monitoring using calibration shots , 2010 .

[28]  Kit Chambers,et al.  Testing the ability of surface arrays to monitor microseismic activity , 2010 .

[29]  Jan-Michael Frahm,et al.  A Comparative Analysis of RANSAC Techniques Leading to Adaptive Real-Time Random Sample Consensus , 2008, ECCV.

[30]  J. Quirein,et al.  Velocity calibration for microseismic monitoring: A very fast simulated annealing (VFSA) approach for joint-objective optimization , 2009 .

[31]  Narrow-angle representations of the phase and group velocities and their applications in anisotropic velocity-model building for microseismic monitoring , 2011 .

[32]  Jacek Trojanowski,et al.  Comparison of migration‐based location and detection methods for microseismic events , 2017 .

[33]  Gregory C. Beroza,et al.  Reverse-Time Migration for Microseismic Sources Using the Geometric Mean as an Imaging Condition , 2015 .

[34]  Jan Valenta,et al.  Joint location and source mechanism inversion of microseismic events: benchmarking on seismicity induced by hydraulic fracturing , 2014 .

[35]  Larry J. Ruff,et al.  Moment tensor inversion of microseisms from the B-sand propped hydrofracture, M-site, Colorado , 2001 .

[36]  Leo Eisner,et al.  Reservoir characterization using surface microseismic monitoring , 2010 .

[37]  K. Chambers,et al.  Moment tensor migration imaging , 2014 .

[38]  Jubran Akram,et al.  A review and appraisal of arrival-time picking methods for downhole microseismic data , 2016 .