1887
  1. Home
  2. A-Z Publications
  3. Geophysical Prospecting
  4. Volume 70, Issue 5
  5. Article
Volume 70, Issue 5
  • E-ISSN: 1365-2478

Abstract

ABSTRACT

This study presents a workflow of using a convolutional neural network to automatically classify microseismic events originating from a more productive oil and gas‐bearing formation (the Eagle Ford Shale), as compared to events originating in less productive formations (the Austin Chalk and Buda Limestone). These microseismic events occur due to hydraulic stimulation and are recorded by fibre optic–distributed acoustic sensing measurements from a horizontal monitoring well. The convolutional neural network is trained to recognize guided wave energy in distributed acoustic sensing seismograms, since microseismic events originating within or close to a low‐velocity reservoir (such as the Eagle Ford) generate significant guided wave energy. The training of convolutional neural network is conducted using synthetic seismograms overlain with real noise profiles from field data. Field events with guided waves are then classified by the convolutional neural network as occurring within or close to the Eagle Ford, while events without guided waves are classified as occurring far outside the Eagle Ford. Noise attenuation steps (including a bandpass filter, median filter and non‐local means filter) are implemented to increase the signal‐to‐noise ratio of the field data and improve the classification accuracy. The accuracy of the convolutional neural network is measured by comparison with labels of the events determined by human inspection of guided wave presence. We also evaluate the impact of different network architectures and noise attenuation methods on classification accuracy. The accuracy and F1‐score of the final classification are both 0.85 when tested on a high signal‐to‐noise ratio subset of the field data. On the complete dataset including low signal‐to‐noise ratio events, an accuracy and F1‐score of 0.80 are achieved. These results demonstrate the high effectiveness of the trained convolutional neural network on guided wave detection and classification of microseismic events inside and outside the Eagle Ford formation.

© 2022 European Association of Geoscientists & Engineers. © 2022 European Association of Geoscientists & Engineers.
Loading

Article metrics loading...

/content/journals/10.1111/1365-2478.13199
2022-05-18
2024-04-26

  • From This Site

    /content/journals/10.1111/1365-2478.13199
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Journal -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Aki, K. & Richards, P.G. (2002) Quantitative Seismology. Sausalito, CA: University Science Books.
     [Google Scholar]
  2. Al Mulhim, A.K. (2019) Optimization of hydraulic fracture treatments and landing zone intervals within the Eagle Ford. Master's Thesis, Colorado School of Mines, https://hdl.handle.net/11124/173962.
     [Google Scholar]
  3. Baird, A., Stork, A., Horne, S., Naldrett, G., Kendall, J.M., Wookey, J.et al. (2020) Characteristics of microseismic data recorded by distributed acoustic sensing (DAS) systems in anisotropic media. Geophysics, 85, 1–37.
     [Google Scholar]
  4. Binder, G. & Tura, A. (2020) Convolutional neural networks for automated microseismic detection in downhole distributed acoustic sensing data and comparison to a surface geophone array. Geophysical Prospecting, 68(9), 2770–2782, https://doi.org/10.1111/1365‐2478.13027.
     [Google Scholar]
  5. Buades, A., Coll, B. & Morel, J.M. (2005) A non‐local algorithm for image denoising. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, https://doi.org/10.1109/CVPR.2005.38.
  6. Cadzow, J.A. (1988) Signal enhancement – a composite property mapping algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing, 36(1), 49–62. https://doi/org/10.1109/29.1488.
     [Google Scholar]
  7. Canales, L.L. (1984) Random noise reduction. SEG Technical Program Expanded Abstracts, 525–527. https://doi.org/10.1190/1.1894168.
     [Google Scholar]
  8. Candès, E., Demanet, L., Donoho, D. & Ying, L. (2006) Fast discrete curvelet transforms. Multiscale Modeling and Simulation, 5(3), 861–899. https://doi.org/10.1137/05064182X.
     [Google Scholar]
  9. Consolvo, B. & Thornton, M. (2020) Microseismic event or noise: automatic classification with convolutional neural networks. SEG Technical Program Expanded Abstracts, 1616–1620. https://doi.org/10.1190/segam2020‐3414896.1
     [Google Scholar]
  10. Daley, T.M., Freifeld, B.M., Ajo‐Franklin, J., Dou, S., Pevzner, R., Shulakova, V.et al. (2013) Field testing of fiber‐optic distributed acoustic sensing (DAS) for subsurface seismic monitoring. The Leading Edge, 32(6), 699–706. https://doi.org/10.1190/tle32060699.1.
     [Google Scholar]
  11. Dumont, V., Tribaldos, V.R., Ajo‐Franklin, J. & Wu, K. (2020) Deep learning for surface wave identification in distributed acoustic sensing data. 2020 IEEE International Conference on Big Data, 1293–1300, https://doi.org/10.1109/BigData50022.2020.9378084.
  12. Eisner, L., Hulsey, B.J., Duncan, P., Jurick, D., Werner, H. & Keller, W. (2010) Comparison of surface and borehole locations of induced seismicity. Geophysical Prospecting, 58(5), 809–820. https://doi.org/10.1111/j.1365‐2478.2010.00867.x.
     [Google Scholar]
  13. Fomel, S., Sava, P., Vlad, I., Liu, Y., and Bashkardin, V. (2013) Madagascar: open‐source software project for multidimensional data analysis and reproducible computational experiments. Journal of Open Research Software, 1(1), e8. http://doi.org/10.5334/jors.ag.
     [Google Scholar]
  14. Gibbons, S.J. & Ringdal, F. (2006) The detection of low magnitude seismic events using array‐based waveform correlation. Geophysical Journal International, 165(1), 149–166. https://doi.org/10.1111/j.1365‐246X.2006.02865.x.
     [Google Scholar]
  15. Gomez, J.L. & Velis, D.R. (2016) A simple method inspired by empirical mode decomposition for denoising seismic data. Geophysics, 81(6), 403–413. https://doi.org/10.1190/geo2015‐0566.1.
     [Google Scholar]
  16. Hartog, A.H. (2017) An Introduction to Distributed Optical Fibre Sensors. London: CRC Press.
     [Google Scholar]
  17. Huff, O., Luo, B., Lellouch, A. & Jin, G. (2021) An eigenfunction representation of deep waveguides with application to unconventional reservoirs. Geophysics, 86(6), T509–T521. https://doi.org/10.1190/geo2021‐0201.1
     [Google Scholar]
  18. Huff, O., Lellouch, A., Luo, B., Jin, G. & Biondi, B. (2020) Validating the origin of microseismic events in target reservoir using guided waves recorded by DAS. The Leading Edge, 39(11), 828–833. https://doi.org/10.1190/tle39110776.1.
     [Google Scholar]
  19. Huot, F., Lellouch, A., Given, P., Clapp, R.G., Biondi, B.L., Nemeth, T., and Nihei, K. (2021) Detecting microseismic events on DAS fiber with super‐human accuracy. SEG Technical Program Expanded Abstracts, 3174–3178. https://doi.org/10.1190/segam2021‐3583060.1.
     [Google Scholar]
  20. Ioffe, S. & Szegedy, C. (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. Proceedings of the 32nd International Conference on Machine Learning, 37, 448–456. https://dl.acm.org/doi/10.5555/3045118.3045167.
     [Google Scholar]
  21. Jakkampudi, S., Shen, J., Li, W., Dev, A., Zhu, T. & Martin, E.R. (2020) Footstep detection in urban seismic data with a convolutional neural network. The Leading Edge, 39(9), 654–660, https://doi.org/10.1190/tle39090654.1.
     [Google Scholar]
  22. Karrenbach, M., Cole, S., Ridge, A., Boone, K., Kahn, D., Rich, J.et al. (2019) Fiber‐optic distributed acoustic sensing of microseismicity, strain and temperature during hydraulic fracturing. Geophysics, 84(1), D11–D23. https://doi.org/10.1190/geo2017‐0396.1.
     [Google Scholar]
  23. Krey, T.C. (1963) Channel waves as a tool of applied geophysics in coal mining. Geophysics, 28(5), 701–714. https://doi.org/10.1190/1.1439258.
     [Google Scholar]
  24. Kingma, D. & Ba, J. (2015) Adam: a method for stochastic optimization. Proceedings of the 3rd International Conference on Learning Representations.
  25. Krizhevsky, A., Sutskever, I. & Hinton, G. (2012) ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84–90. https://dl.acm.org/doi/10.1145/3065386.
     [Google Scholar]
  26. LeCun, Y., Bottou, L., Bengio, Y. & Haffner, P. (1998) Gradient‐based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278–2324. https://doi.org/10.1109/5.726791.
     [Google Scholar]
  27. Lellouch, A., Biondi, E., Biondi, B., Luo, B., Jin, G. & Meadows, M.A. (2021) Properties of a deep seismic waveguide measured with an optical fiber. Physical Review Research, 3(1), 013164. https://doi.org/10.1103/PhysRevResearch.3.013164.
     [Google Scholar]
  28. Lellouch, A., Lindsey, N.J., Ellsworth, W.L. & Biondi, B.L. (2020) Comparison between distributed acoustic sensing and geophones: downhole microseismic monitoring of the FORGE geothermal experiment. Seismological Research Letters, 91(6), 3256–3268. https://doi.org/10.1785/0220200149.
     [Google Scholar]
  29. Lever, J., Krzywinski, M. & Altman, N. (2016) Points of significance: model selection and overfitting. Nature Methods, 13(9), 703–704. https://doi.org/10.1038/nmeth.3968.
     [Google Scholar]
  30. Li, Q., Cai, W., Wang, X., Zhou, Y., Feng, D.D. & Chen, M. (2014) Medical image classification with convolutional neural network. 13th International Conference on Control Automation Robotics and Vision, 844–848. https://doi.org/10.1109/ICARCV.2014.7064414.
     [Google Scholar]
  31. Liang, C., Thornton, M.P., Morton, P., Hulsey, B.J., Hill, A. & Rawlins, P. (2009) Improving signal‐to‐noise ratio of passive seismic data with an adaptive FK filter. SEG Technical Program Expanded Abstracts, 1703–1707. https://doi.org/10.1190/1.3255179.
     [Google Scholar]
  32. Lim, I., Nemeth, T., Zhang, Z., Tan, Y., Bevc, D., Bartel, D.et al. (2021) Image‐based fracture hit warning system with distributed acoustic sensing. SEG Technical Program Expanded Abstracts, 397–401. https://doi.org/10.1190/segam2021‐3587266.1.
     [Google Scholar]
  33. Liu, Y., Huff, O. & Jin, G. (2021) Convolutional neural network for guided‐wave energy identification in microseismic DAS data. SEG Technical Program Expanded Abstracts, 332–336. https://doi.org/10.1190/segam2021‐3573385.1.
     [Google Scholar]
  34. Luo, B., Lellouch, A., Jin, G., Biondi, B. & Simmons, J. (2021) Seismic inversion of shale reservoir properties using microseismic‐induced guided waves recorded by distributed acoustic sensing. Geophysics, 86(4), R383–R397. https://doi.org/10.1190/geo2020‐0607.1.
     [Google Scholar]
  35. Maxwell, S.C. (2009) Microseismic location uncertainty. CSEG Recorder, 34(4), 41–46.
     [Google Scholar]
  36. Maxwell, S.C. (2014) Microseismic Imaging of Hydraulic Fracturing. Tulsa, OK: Society of Exploration Geophysicists.
     [Google Scholar]
  37. Maxwell, S.C., Chorney, D. & Goodfellow, S.D. (2015) Microseismic geomechanics of hydraulic‐fracture networks: insights into mechanisms of microseismic sources. The Leading Edge, 34(8), 904–910, https://doi.org/10.1190/tle34080904.1.
     [Google Scholar]
  38. Mokhtari, M., Honarpour, M., Tutuncu, A. & Boitnott, G. (2016) Characterization of elastic anisotropy in Eagle Ford shale: impact of heterogeneity and measurement scale. SPE Reservoir Evaluation and Engineering, 19(3), 429–439. https://doi.org/10.2118/170707‐PA.
     [Google Scholar]
  39. Nielsen, M.A. (2018) Neural Networks and Deep Learning, Determination Press.
     [Google Scholar]
  40. Ovcharenko, O., Kazei, V., Peter, D. & Alkhalifah, T. (2021) Transferring Elastic Low Frequency Extrapolation from Synthetic to Field Data. 82nd EAGE Annual Conference and Exhibition.
     [Google Scholar]
  41. Paoletti, M.E., Haut, J.M., Plaza, J. & Plaza, A. (2017) A new deep convolutional neural network for fast hyperspectral image classification. ISPRS Journal of Photogrammetry and Remote Sensing, 145(A), 120–147. https://doi.org/10.1016/j.isprsjprs.2017.11.021.
     [Google Scholar]
  42. Phillips, W.S., Rutledge, J.T., Fairbanks, T.D., Gardner, T.L., Miller, M.E. & B.K., Schuessler (1998) Reservoir fracture mapping using microearthquakes: Austin Chalk, Giddings Field, TX and 76 Field, Clinton Co., KY. SPE Reservoir Evaluation and Engineering, 1(1998), 114–121.
     [Google Scholar]
  43. Ravasi, M. & Vasconcelos, I. (2020) PyLops–a linear‐operator Python library for scalable algebra and optimization. Software X, 11, 100361. https://doi.org/10.1016/j.softx.2019.100361.
     [Google Scholar]
  44. Richter, P., Parker, T., Woerpel, C., Wu, Y., Rufino, R. & Farhadiroushan, M. (2019) Hydraulic fracture monitoring and optimization in unconventional completions using a high‐resolution engineered fibre‐optic distributed acoustic sensor. First Break, 37(4), 63–68. https://doi.org/10.3997/1365‐2397.n0021.
     [Google Scholar]
  45. Sayers, C.M., Walsh, J.J. & Fisher, K. (2014) Sonic anisotropy of the Lower Eagle Ford Shale. SEG Technical Program Expanded Abstracts, 295–300. https://doi.org/10.1190/segam2014‐0113.1.
     [Google Scholar]
  46. Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. (2017) Grad‐CAM: visual explanations from deep networks via gradient‐based localization. 2017 IEEE International Conference on Computer Vision (ICCV), pp. 618–626.
  47. Sokolova, M. & Lapalme, G. (2009) A systematic analysis of performance measures for classification tasks. Information Processing and Management, 45(4), 427–437. https://doi.org/10.1016/j.ipm.2009.03.002.
     [Google Scholar]
  48. Stork, A.L., Baird, A.F., Horne, S.A., Naldrett, G., Lapins, S., Kendall, J.M.et al. (2020) Application of machine learning to microseismic event detection in distributed acoustic sensing data. Geophysics, 85(5), KS149–KS160, https://doi.org/10.1190/geo2019‐0774.1.
     [Google Scholar]
  49. Thomsen, L. (1986) Weak elastic anisotropy. Geophysics, 51(6), 1954–1966, https://doi.org/10.1190/1.1442051.
     [Google Scholar]
  50. Titov, A., Binder, G., Liu, Y., Jin, G., Simmons, J., Tura, A.et al. (2021) Modeling and interpretation of scattered waves in interstage distributed acoustic sensing vertical seismic profiling survey. Geophysics, 86(2), D93–D102. https://doi.org/10.1190/geo2020‐0293.1.
     [Google Scholar]
  51. Torrey, L. & Shavlik, J. (2010) Transfer Learning. Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques, 242–264. https://doi.org/10.4018/978‐1‐60566‐766‐9.ch011.
     [Google Scholar]
  52. Tuppen, A. (2019) Nine‐component seismic amplitude inversion: a case study in the Eagle Ford shale. Master's thesis, Colorado School of Mines, https://hdl.handle.net/11124/173298.
     [Google Scholar]
  53. Vaezi, Y. & Van der Baan, M. (2015) Comparison of the STA/LTA and power spectral density methods for microseismic event detection. Geophysical Journal International, 203(3), 1896–1908, https://doi.org/10.1093/gji/ggv419.
     [Google Scholar]
  54. Verdon, J.P., Horne, S.A., Clarke, A., Stork, A.L., Baird, A.F. & Kendall, J.M. (2020) Microseismic monitoring using a fibre‐optic distributed acoustic sensor array. Geophysics, 85(3), KS89–KS99. https://doi.org/10.1190/geo2019‐0752.1.
     [Google Scholar]
  55. Wang, H. & Chen, Y. (2021) Adaptive frequency‐domain nonlocal means for seismic random noise attenuation. Geophysics, 86(2), V143–V152. https://doi.org/10.1190/geo2019‐0798.1.
     [Google Scholar]
  56. Webster, P., Wall, J., Perkins, C. & Molenaar, M. (2013) Micro‐seismic detection using distributed acoustic sensing. SEG Technical Program Expanded Abstracts, 2459–2463. https://doi.org/10.1190/segam2013‐0182.1.
     [Google Scholar]
  57. Withers, M., Aster, R., Young, C., Beiriger, J., Harris, M., Moore, S. & Trujillo, J. (1998) A comparison of select trigger algorithms for automated global seismic phase and event detection. Bulletin of the Seismological Society of America, 88(1), 95–106. https://doi.org/10.1785/BSSA0880010095.
     [Google Scholar]
  58. Xia, K., Hilterman, F. & Hu, H. (2018) Unsupervised machine learning algorithm for detecting and outlining surface waves on seismic shot gathers. Journal of Applied Geophysics, 157(C), 73–86. https://doi.org/10.1016/j.jappgeo.2018.07.003.
     [Google Scholar]
  59. Zhang, C. & Van der Baan, M. (2020) Microseismic denoising and reconstruction by unsupervised machine learning. IEEE Geoscience and Remote Sensing Letters, 17(7), 1114–1118. https://doi.org/10.1109/LGRS.2019.2943851.
     [Google Scholar]
  60. Zhang, H., Ma, C., Jiang, Y., Li, T., Pazzi, V. & Casagli, N. (2021) Integrated processing method for microseismic signal based on deep neural network. Geophysical Journal International, 226(3), 2145–2157. https://doi.org/10.1093/gji/ggab099.
     [Google Scholar]
  61. Zhang, H., Ma, C., Pazzi, V., Zou, Y. & Casagli, N. (2020) Microseismic signal denoising and separation based on fully convolutional encoder‐decoder network. Applied Sciences, 10(18), 6621. https://doi.org/10.3390/app10186621.
     [Google Scholar]
  62. Zhou, B., Khosla, A., Lapedriza, A., Oliva, A. & Torralba, A. (2016) Learning deep features for discriminative localization. 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2921–2929. https://doi.org/10.1109/CVPR.2016.319.
http://instance.metastore.ingenta.com/content/journals/10.1111/1365-2478.13199
Loading
Convolutional neural network‐based classification of microseismic events originating in a stimulated reservoir from distributed acoustic sensing data
Geophysical Prospecting 70, 904 (2022); https://doi.org/10.1111/1365-2478.13199
/content/journals/10.1111/1365-2478.13199
/content/journals/10.1111/1365-2478.13199
Loading

Data & Media loading...

  • Article Type: Research Article
Keyword(s): Data processing; Distributed acoustic sensing; Microseismic; Neural network; Reservoir geophysics

Most Cited This Month Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error