Velocity modeling and inversion techniques for locating microseismic events in unconventional reservoirs

Jianzhong Zhang , Han Liu , Zhihui Zou , Zhonglai Huang

Journal of Earth Science ›› 2015, Vol. 26 ›› Issue (4) : 495 -501.

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Journal of Earth Science ›› 2015, Vol. 26 ›› Issue (4) : 495 -501. DOI: 10.1007/s12583-015-0565-4
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Velocity modeling and inversion techniques for locating microseismic events in unconventional reservoirs

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Abstract

A velocity model is an important factor influencing microseismic event locations. We review the velocity modeling and inversion techniques for locating microseismic events in exploration for unconventional oil and gas reservoirs. We first describe the geological and geophysical characteristics of reservoir formations related to hydraulic fracturing in heterogeneity, anisotropy, and variability, then discuss the influences of velocity estimation, anisotropy model, and their time-lapse changes on the accuracy in determining microseismic event locations, and then survey some typical methods for building velocity models in locating event locations. We conclude that the three tangled physical attributes of reservoirs make microseismic monitoring very challenging. The uncertainties in velocity model and ignoring its anisotropies and its variations in hydraulic fracturing can cause systematic mislocations of microseismic events which are unacceptable in microseismic monitoring. So, we propose some potential ways for building accurate velocity models.

Keywords

microseismic location / velocity model building / velocity error / anisotropy / unconventional reservoir

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Jianzhong Zhang, Han Liu, Zhihui Zou, Zhonglai Huang. Velocity modeling and inversion techniques for locating microseismic events in unconventional reservoirs. Journal of Earth Science, 2015, 26(4): 495-501 DOI:10.1007/s12583-015-0565-4

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References

[1]

Al-Harrasi O. H. A.-, Anboori A. W. A., . Seismic Anisotropy in a Hydrocarbon Field Estimated from Microseismic Data. Geophysical Prospecting, 2011, 59(2): 227-243.

[2]

Bardainne T., Gaucher E. Constrained Tomography of Realistic Velocity Models in Microseismic Monitoring Using Calibration Shots. Geophysical Prospecting, 2010, 58(5): 739-753.

[3]

Coates R. T., Schoenberg M. Finite-Difference Modeling of Faults and Fractures. Geophysics, 1995, 60(5): 1514-1526.

[4]

Duncan P. M. Is There a Future for Passive Seismic?. First Break, 2005, 23: 111-115.

[5]

Erwemi A., Walsh J., Bennett L., . Anisotropic Velocity Modeling for Microseismic Processing: Part 3—Borehole Sonic Calibration Case Study. SEG Technical Program Expanded Abstracts, 2010, 29: 508-512.

[6]

Fehler M., House L., Phillips W. S., . A Method to Allow Temporal Variation of Velocity in Travel-Time Tomography Using Microearthquakes Induced during Hydraulic Fracturing. Tectonophysics, 1998, 289(1–3): 189-201.

[7]

Fish A. M. Microseismic Velocity Inversion and Event Location Using Reverse Time Imaging: [Dissertation]. Colorado School of Mines, Golden, 2012, 53-69.

[8]

Grechka V., Singh P., Das I. Estimation of Effective Anisotropy Simultaneously with Locations of Microseismic Events. Geophysics, 2011, 76(6): WC143-WC155.

[9]

Grechka V., Yaskevich S. Inversion of Microseismic Data for Triclinic Velocity Models. Geophysical Prospecting, 2013, 61(6): 1159-1170.

[10]

Grechka V., Yaskevich S. Azimuthal Anisotropy in Microseismic Monitoring: A Bakken Case Study. Geophysics, 2014, 79(1): KS1-KS12.

[11]

Jones G. A., Kendall J. M., Bastow I. D., . Locating Microseismic Events Using Borehole Data. Geophysical Prospecting, 2014, 62(1): 34-49.

[12]

Kiselevitch V. L., Nikolaev A. V., Troitskiy P. A., . Emission Tomography: Main Ideas, Results, and Prospects. SEG Technical Program Expanded Abstracts, 1991, 10 1602.

[13]

Kochnev I. V., Polyakov V. S., Murtayev I., . Imaging Hydraulic Fracture Zones from Surface Passive Microseismic Data. First Break, 2007, 25: 77-80.

[14]

Kushnir A., Rozhkov N., Varypaev A. Statistically-Based Approach for Monitoring of Micro-Seismic Events. GEM—International Journal on Geomathematics, 2013, 4(2): 201-225.

[15]

Kushnir A., Varypaev A., Dricker I., . Passive Surface Microseismic Monitoring as a Statistical Problem: Location of Weak Microseismic Signals in the Presence of Strongly Correlated Noise. Geophysical Prospecting, 2014, 62(4): 819-833.

[16]

Liu H., Zhang J. STA/LTA Algorithm Analysis and Improvement of Microseismic Signal Automatic Detection. Progress in Geophysics, 2014, 29: 1708-1714.

[17]

Maxwell S. C., Rutledge J., Jones R., . Geophysics, 2010, 75(5): 75A129-75A137.

[18]

Maxwell S. C., Bennett L., Jones M., . Anisotropic Velocity Modeling for Microseismic Processing: Part 1—Impact of Velocity Model Uncertainty. SEG Technical Program Expanded Abstracts, 2010, 29: 2130-2134.

[19]

Maxwell S. C., Urbancic T. I., Steinsberger N., . Microseismic Imaging of Hydraulic Fracture Complexity in the Barnett Shale. SPE Annual Technical Conference and Exhibition, 2002

[20]

Pei D. H., Carmichael J., Waltman C., . Microseismic Anisotropic Velocity Calibration by Using both Direct and Reflected Arrivals. SEG Technical Program Expanded Abstracts, 2014, 33: 2278-2282.

[21]

Sena A., Castillo G., Chesser K., . Seismic Reservoir Characterization in Resource Shale Plays: Stress Analysis and Sweet Spot Discrimination. The Leading Edge, 2011, 30(7): 758-764.

[22]

Teanby N., Kendall J. M., Jones R. H., . Stress-Induced Temporal Variations in Seismic Anisotropy Observed in Microseismic Data. Geophysical Journal International, 2004, 156(3): 459-466.

[23]

Usher P. J., Angus D. A., Verdon J. P. Influence of a Velocity Model and Source Frequency on Microseismic Waveforms: Some Implications for Microseismic Locations. Geophysical Prospecting, 2013, 61(1): 334-345.

[24]

Dok R. V., Fuller B., Engelbrecht L., . Seismic Anisotropy in Microseismic Event Location Analysis. The Leading Edge, 2011, 30(7): 766-770.

[25]

Verdon J. P., Kendall J. M. Detection of Multiple Fracture Sets Using Observations of Shear-Wave Splitting in Microseismic Data. Geophysical Prospecting, 2011, 59(4): 593-608.

[26]

Walsh J., Sinha B., Plona T., . Derivation of Anisotropy Parameters in a Shale Using Borehole Sonic Data. SEG Technical Program Expanded Abstracts, 2007, 26: 323-327.

[27]

Warpinski N. R., Waltman C. K., Du J., . Anisotropy Effects in Microseismic Monitoring. SPE Annual Technical Conference and Exhibition, Paper SPE 124208, 2009

[28]

Woerpel J. C. Anisotropic Velocity Modeling for Microseismic Processing: Part 2—Fast and Accurate Model Calibration with a Cross-Well Source. SEG Technical Program Expanded Abstracts, 2010, 29: 2135-2139.

[29]

Yin C., Liu H., Li Y., . The Precision Analysis of the Microseismic Location. Progress in Geophysics, 2013, 28: 800-807.

[30]

Yin C., Liu H., Wu F. R., . The Effect of the Calibrated Velocity on the Microseismic Event Location Precision. SEG Technical Program Expanded Abstracts, 2013, 32: 1977-1981.

[31]

Zhang H. J., Sarkar S., Toksöz M. N., . Passive Seismic Tomography Using Induced Seismicity at a Petroleum Field in Oman. Geophysics, 2009, 74(6): WCB57-WCB69.

[32]

Zhang H., Thurber C. H. Double-Difference Tomography: The Method and Its Application to the Hayward Fault, California. Bulletin of the Seismological Society of America, 2003, 93(5): 1875-1889.

[33]

Zhang X. L., Zhang F., Li X. Y. The Influence of Fracturing Process on Microseismic Propagation. Geophysical Prospecting, 2014, 62(4): 797-805.

[34]

Zhang Y., Eisner L., Barker W., . Effective Anisotropic Velocity Model from Surface Monitoring of Microseismic Events. Geophysical Prospecting, 2013, 61(5): 919-930.

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