ARTICLE

Calibrating anisotropic velocity models for Vaca Muerta

DANIEL O. PÉREZ1,2,3 SOLEDAD R. LAGOS1,2 DANILO R. VELIS1,2 JUAN C. SOLDO3
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1 Facultad de Ciencias Astronómicas y Geofísicas - Universidad Nacional de La Plata, La Plata, Argentina.,
2 CONICET, La Plata, Argentina.,
3 Ecopetrol, Bogota, Colombia.,
JSE 2019, 28(5), 495–511;
Submitted: 22 August 2018 | Accepted: 12 August 2019 | Published: 1 October 2019
© 2019 by the Authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
Abstract

Pérez, D.O., Lagos, S.R., Velis, D.R. and Soldo, J.C., 2019. Calibrating anisotropic velocity models for Vaca Muerta. Journal of Seismic Exploration, 28: 495-511. We provide well-calibrated VTI velocity models useful to locate microseismic events in the Vaca Muerta shale formation, Neuquén, Argentina. Assuming layered models with weak anisotropy, we make use of the information provided by well logs and perforation shots of known position to estimate the layer velocities, depths and anisotropy. This leads to a constrained nonlinear inverse problem that consists of minimizing the discrepancies between the observed and calculated P- and S-wave arrival time differences. To avoid local minima and other convergence issues, we minimize the resulting objective function using very fast simulated annealing (VFSA). We test the proposed strategy on field data and estimate a set of velocity models that honor the observed data, which we validate carrying out a simulated microseismic event location. The results show that the proposed strategy is capable of estimating layered VTI velocity models suitable to accurately locate microseismic events during a hydraulic stimulation in the Vaca Muerta shale formation.

Keywords
VTI
Vaca Muerta
microseismic
calibration
velocity model
VFSA
References
  1. Aki, K., and P. Richards, 1980, Quantitative Seismology: Theory and Methods. W.H.Freeman and Co., Sausalito.
  2. Akram, J. and Eaton, D., 2013. Impact of velocity model calibration on microseismiclocations. Expanded Abstr., 83rd Ann. Internat. SEG Mtg., Houston: 1982-1986.
  3. Bachrach, R., 2014. Linearized orthorhombic AVAz inversion: Theoretical and practicalconsideration. Expanded Abstr., 84th Ann. Internat. SEG Mtg., Denver: 528-532.
  4. Backus, G.E., 1962. Long-wave elastic anisotropy produced by horizontal layering. J.Geophys. Res., 67: 4427-4440.
  5. Bardainne, T. and Gaucher, E., 2010. Constrained tomography of realistic velocitymodels in microseismic monitoring using calibration shots. Geophys. Prosp., 58:739-753.
  6. Berryman, J.G., 1979, Long-wave elastic anisotropy in transversely isotropic media.Geophysics, 44: 896-917.
  7. Berryman, J.G., Grechka, V.Y. and Berge, P.A., 1999. Analysis of Thomsen parametersfor finely layered VTI media. Geophys. Prosp., 47: 959-978.
  8. Chen, H., Zhang, G. and Yin, X., 2012. AVAz inversion for elastic parameter andfracture fluid factor. Expanded Abstr., 82nd Ann. Internat. SEG Mtg., Las Vegas:1-5.
  9. Cooke, D. and Schneider, W., 1983. Generalized linear inversion of reflection seismicdata. Geophysics, 48: 665-676.
  10. Corona, W.W. and Mavko, G., 2008. In: Predicting Clay Content and Porosity fromGamma-ray and Conductivity Logs: 425-433.
  11. Djikpesse, H.A., 2015. Ci3 and Thomsen anisotropic parameter distributions forhydraulic fracture monitoring. Interpretation, 3: SW1-SW10.
  12. Downton, J. and Gray, D., 2006. AVAz parameter uncertainty estimation. Expanded
  13. Abstr., 76th Ann. Internat. SEG Mtg., New Orleans: 234-238.
  14. Eisner, L., Hulsey, B.J., Duncan, P., Jurick, D., Werner, H. and Keller, W., 2010.
  15. Comparison of surface and borehole locations of induced seismicity. Geophys.Prosp., 58: 809-820.
  16. Ingber, L., 1989. Very fast simulated re-annealing. J. Mathemat. Computat. Modell., 12:967-973.
  17. Lay, T. and Wallace, T.C., 1995. Modern Global Seismology. Academic Press, NewYork.
  18. Leaney, S., Chapman, C. and Yu, X., 2014a. Anisotropic moment tensor inversion,decomposition and visualization. Expanded Abstr., 84th Ann. Internat. SEGMtg., Denver: 2250-2255.
  19. Leaney, S., 2014b. Microseismic Source Inversion in Anisotropic Media. Ph.D. thesis,University of British Columbia, Vancouver, BC.
  20. Li, J., Li, C., Morton, S.A., Dohmen, T., Katahara, K. and Tokséz, M.N., 2014.
  21. Microseismic joint location and anisotropic velocity inversion for hydraulicfracturing in a tight bakken reservoir. Geophysics, 79: C111-C122.
  22. Liu, E. and Martinez, A., 2012. Seismic Fracture Characterization, Concepts andPractical Applications. EAGE, Houten.
  23. Mahmoudian, F., Margrave, G.F., Wong, J. and Henley, D.C., 2013. Fracture orientationand intensity from AVAz inversion: A physical modeling study. Expanded Abstr.,83rd Ann. Internat. SEG Mtg., Houston: 483-487.
  24. Maxwell, S., Bennett, L., Jones, M. and Walsh, J., 2010. In: Anisotropic Velocity
  25. Modeling for Microseismic Processing: Part 1 - Impact of Velocity ModelUncertainty. SEG, Tulsa, OK: 2130-2134.
  26. Mizuno, T., Leaney, S. and Michaud, G., 2010. Anisotropic velocity model inversion forimaging the microseismic cloud. Extended Abstr., 72nd EAGE Conf., Barcelona.
  27. Pei, D., Carmichael, J., Waltman, C. and Warpinski, N., 2014. Microseismic anisotropicvelocity calibration by using both direct and reflected arrivals. Expanded Abstr.,84th Ann. Internat. SEG Mtg., Denver: 2278-2282.
  28. Pei, D., Quirein, J.A., Cornish, B.E., Quinn, D. and Warpinski, N.R., 2009. Velocitycalibration for microseismic monitoring: A very fast simulated annealing (vfsa)approach for joint-objective optimization. Geophysics, 74, WCB47-WCBS5S.
  29. Pérez, D.O., Lagos, S.R., Velis, D.R. and Soldo, J.C., 2016. Inversion of seismicanisotropic parameters using very fast simulated annealing with application tomicroseismic event location. Mecanica Computacional, Vol. XXXIV, AsociacionArgentina de Mecanica Computacional: 3351-3367.
  30. Rauzi, R.S., Santiago, M.F., Alvarado, O.A. and Bertoldi, F., 2014. Estrategia determinacion y ensayo del primer pozo exploratorio no convencional de laformacion Vaca Muerta en la subcuenca de Picun Leufi. Neuquén, Argentina.
  31. Presented at the IX Congreso de Exploracién y Desarrollo de Hidrocarburos.
  32. Rider, M. and Kennedy, M., 2011. The Geological Interpretation of Well Logs. Rider-French, Cambridge.
  33. Sabbione, J.I. and Velis, D.R., 2013. A robust method for microseismic event detectionbased on automatic phase pickers. J. Appl. Geophys., 99: 42-50.
  34. Sheriff, R.E. and Geldart, L.P., 1995. Exploration Seismology, 2nd Ed., CambridgeUniversity Press.
  35. Thomsen, L., 1986, Weak elastic anisotropy. Geophysics, 51: 1954-1966.
  36. Tsvankin, I. 2012. Seismic Signatures and Analysis of Reflection Data in AnisotropicMedia, 3rd Ed. SEG, Tulsa, OK..
  37. Velis, D.R., Sabbione, JI and Sacchi, M.D., 2015. Fast and automatic microseismicphase-arrival detection and denoising by pattern recognition and reduced-rankfiltering. Geophysics, 80: WC25-WC38.
  38. Velis, D.R., 2003. Estimating the distribution of primary reflection coefficients.Geophysics, 68: 1417-1422.
  39. Velis, D.R., 2007. Statistical segmentation of geophysical log data. Mathemat. Geol., 39:409-417:
  40. Velis, D.R., 2010. Multivariate zonation of logging data. In: Advances in Environmental
  41. Research, 2. Nova Science Publishers Inc., Hauppage, NY: 349-365.S11
  42. Walden, A. and Hosken, J., 1986. The nature of the non-Gaussianity of primary reflectioncoefficients and its significance for deconvolution. Geophys. Prosp., 34: 1038-
  43. Warpinski, N.R., Sullivan, R.B., Uhl, J., Waltman, C. and Machovoie, S., 2005.
  44. Improved microseismic fracture mapping using perforation timing measurementsfor velocity calibration. SPE J., 10: 14-23.
  45. Weaver, C.E., 1931. Paleontology of the Jurassic and Cretaceous of west and central
  46. Argentina. University of Washington Press., Seattle, WA.
  47. Willis, M., 2013. Upscaling anisotropic geomechanical properties using Backusaveraging and petrophysical clusters in the Vaca Muerta. M.Sc. Thesis, ColoradoSchool of Mines, Golden, CO.
  48. Yilmaz, O., 2001, Seismic Data Analysis: Processing, Inversion, and Interpretation of
  49. Seismic Data. SEG Investigations in Geophysics. Tulsa, OK.
  50. Yue, T. and Chen, X.-F., 2005. A rapid and accurate two-point ray tracing method inhorizontally layered velocity model: Acta Seismol. Sin., 18: 154-161.
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Journal of Seismic Exploration, Electronic ISSN: 0963-0651 Print ISSN: 0963-0651, Published by AccScience Publishing