Geomechanics and Engineering

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Coupling relevance vector machine and response surface for geomechanical parameters identification

  • Zhao, Hongbo (School of Civil Engineering, Henan Polytechnic University) ;
  • Ru, Zhongliang (School of Civil Engineering, Henan Polytechnic University) ;
  • Li, Shaojun (State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences)
  • Received : 2016.10.14
  • Accepted : 2018.04.25
  • Published : 2018.08.30
⟨ Previous Next ⟩

Abstract

Geomechanics parameters are critical to numerical simulation, stability analysis, design and construction of geotechnical engineering. Due to the limitations of laboratory and in situ experiments, back analysis is widely used in geomechancis and geotechnical engineering. In this study, a hybrid back analysis method, that coupling numerical simulation, response surface (RS) and relevance vector machine (RVM), was proposed and applied to identify geomechanics parameters from hydraulic fracturing. RVM was adapted to approximate complex functional relationships between geomechanics parameters and borehole pressure through coupling with response surface method and numerical method. Artificial bee colony (ABC) algorithm was used to search the geomechanics parameters as optimal method in back analysis. The proposed method was verified by a numerical example. Based on the geomechanics parameters identified by hybrid back analysis, the computed borehole pressure agreed closely with the monitored borehole pressure. It showed that RVM presented well the relationship between geomechanics parameters and borehole pressure, and the proposed method can characterized the geomechanics parameters reasonably. Further, the parameters of hybrid back analysis were analyzed and discussed. It showed that the hybrid back analysis is feasible, effective, robust and has a good global searching performance. The proposed method provides a significant way to identify geomechanics parameters from hydraulic fracturing.

Keywords

Acknowledgement

Supported by : University of Henan Province, National Natural Science Foundation of China

References

  1. Agarwal, A. and Triggs, B. (2004), "3D human pose from silhouettes by relevance vector regression", Proc. IEEE Int. Conf. Computer Vision and Pattern Recognition, 882-888.
  2. Chen, S., Gunn, S. and Harris, C. (2001), "The relevance vector machine technique for channel equalization application", IEEE Tran. Neur. Network., 12(6), 1529-1532. https://doi.org/10.1109/72.963792
  3. Cividini, A. (1988), "Parameter estimation of a static geotechnical model using a Bayes approach", Int. J. Rock Mech. Min. Sci., 20(5), 215-226.
  4. Deng, J., Gu, D., Li, X. and Yue, Z.Q. (2005), "Structural reliability analysis for implicit performance functions using artificial neural network", Struct. Saf., 27(1), 25-48. https://doi.org/10.1016/j.strusafe.2004.03.004
  5. Deng, J.H. and Lee, C.F. (2001), "Displacement back analysis for a steep slope at the Three Gorges Project site", Int. J. Rock Mech. Min. Sci., 38, 259-268. https://doi.org/10.1016/S1365-1609(00)00077-0
  6. Fang, Z. and Khaksar, A. (2011), "Complexity of minifrac tests and implications for in-situ horizontal stresses in coalbed methane reservoirs', IPTC14630, 1-13.
  7. Farkhondeh, K., Hamid, S. and Mahabadi, E.S. (2016), "Relevance Vector Machine for Survival Analysis", IEEE Tran. Neur. Network. Learn. Syst., 27(3), 648-660. https://doi.org/10.1109/TNNLS.2015.2420611
  8. Feng, X.T., Zhao, H. and Li, S. (2004), "A new displacement back analysis to identify mechanical geo-material parameters based on hybrid intelligent methodology", Int. J. Numer. Anal. Meth. Geomech., 28, 1141-1165. https://doi.org/10.1002/nag.381
  9. Ferrero, A.M., Migliazza, M., Segalini, A. and Guli, D. (2013), "In-situ stress measurements interpretations in large underground marble quarry by 3D modeling", Int. J. Rock Mech. Min. Sci., 60, 103-113. https://doi.org/10.1016/j.ijrmms.2012.12.008
  10. Ghorbani, M. and Sharifzadeh, M. (2009), "Long term stability assessment of Siah Bisheh powerhouse cavern based on displacement back analysis method", Tunnel. Underg. Space Technol., 24(5), 574-583. https://doi.org/10.1016/j.tust.2009.02.007
  11. Gioda, G. and Jurina, L. (1981), "Numerical identification of soil structureinteraction pressures", Int. J. Numer. Anal. Meth. Geomech., 5, 33-56. https://doi.org/10.1002/nag.1610050105
  12. Gioda, G. and Maier, G. (1980), "Direct search solution of an inverse problem in elastoplasticity: identification of cohesion, friction angle and in situ stress by pressure tunnel tests", Int. Numer. Meth. Eng., 15, 1823-48. https://doi.org/10.1002/nme.1620151207
  13. Gomes, H.M. and Awruch, A.M. (2004), "Comparison of response surface and neural network with other methods for structural reliability analysis", Struct. Saf., 26, 49-67. https://doi.org/10.1016/S0167-4730(03)00022-5
  14. Haimson, B.C. (1978), "The hydrofracturing stress measuring method and recent field results", Int. J. Rock Mech. Min. Sci. Geomech. Abstr, 15, 167-178. https://doi.org/10.1016/0148-9062(78)91223-8
  15. Haimson, B.C. (1993), "The hydraulic fracturing method of stress measurement: theory and practice", Ed. Hudson, J.A., Comprehensive Rock Engineering, Pergamon Press, Oxford, 395-412.
  16. Haimson, B.C. and Fairhurst, C. (1969), "In-situ stress determination at great depth by means of hydraulic fracturing", The 11th U.S. Symposiumon RockMechanics (USRMS), Berkeley, CA.
  17. Hubbert, M.K. and Willis, D.G. (1957), "Mechanics of hydraulic fracturing", Tran. AIME, 210, 153-163.
  18. Karaboga, D. (2005), "An idea based on honey bee swarm for numerical optimization", Technical Report-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department.
  19. Karaboga, D. and Basturk, B. (2008), "On the performance of artificial bee colony (ABC) algorithm", Appl. Soft Comput., 8, 687-697. https://doi.org/10.1016/j.asoc.2007.05.007
  20. Karaboga, D. and Ozturk, C. (2011), "A novel clustering approach: Artificial Bee Conoly (ABC) algorithm", Appl. Soft Comput., 11, 652-657. https://doi.org/10.1016/j.asoc.2009.12.025
  21. Khuri, A. and Cornell, J.A. (1996), Response Surfaces. Designs and Analyses, Marcel Dekker Inc., New York.
  22. Lewis, R.W. and Schrefler, B.A. (1998), The Finite Element Method in the Static and Dynamic Deformation and Consolidation of Porous Media, 2nd Edition, Wiley, London.
  23. Mirzaei, Z., Akbarpour, A., Khatibinia, M. and Siuki, A.K. (2015), "Optimal design of homogeneous earth dams by particle swarm optimization incorporating support vector machine approach", Geomech. Eng., 9(6), 709-727. https://doi.org/10.12989/gae.2015.9.6.709
  24. Okabe, T. (1998), "Inverse of drilling-induced tensile fracture data obtained from a single inclined borehole", Int. J. Rock Mech. Min. Sci., 35(6), 747-758. https://doi.org/10.1016/S0148-9062(98)00001-1
  25. Oreste, P. (2005), "Backanalysis techniques for the improvement of the understanding of rock in underground constructions", Tunnel. Underg. Space Technol., 201, 7-21.
  26. Pichler, B., Lackner, R. and Mang, H.A. (2003), "Back analysis of model parameters in geotechnical engineering by means of soft computing", Int. J. Numer. Meth. Eng., 57, 1943-1978. https://doi.org/10.1002/nme.740
  27. Puller, J.W., Mills, K.W., Jeffrey, R.G. and Walker, R.J. (2016), "In-situ stress measurements and stress change monitoring to monitor overburden caving behaviour and hydraulic fracture pre-conditioning", Int. J. Min. Sci. Technol., 26(1), 103-110. https://doi.org/10.1016/j.ijmst.2015.11.017
  28. Rechea, C., Levasseur, S. and Finno, R. (2008), "Inverse analysis techniques for parameter identification in simulation of excavation support systems", Comput. Geotech., 35, 331-345. https://doi.org/10.1016/j.compgeo.2007.08.008
  29. Rodriguez, R.F., Nicieza, C.G., Gayarre, F.L. and Lopez, F.L.R. (2015), "Application of hydraulic cylinder testing to determine the geotechnical properties of earth-filled dams", Geomech. Eng., 9(4), 483-498. https://doi.org/10.12989/gae.2015.9.4.483
  30. Sakurai, S. and Takeuchi, K. (1983), "Back analysis of measured displacements of tunnels", Rock Mech. Rock Eng., 16, 173-180. https://doi.org/10.1007/BF01033278
  31. Seyed, E.S., Reza, K. and Kaveh, A. (2014), "A new geomechanical approach to investigate the role of in-situ stresses and pore pressure on hydraulic fracture pressure profile in vertical and horizontal oil wells", Geomech. Eng., 7(3), 233-246. https://doi.org/10.12989/gae.2014.7.3.233
  32. Tipping, M. (2001), "Sparse Bayesian learning and the relevance vector machine", J. Mach Learn. Res., 1, 211-244.
  33. Vardakos, S., Gutierrez, M. and Xia, C. (2012), "Parameter identification in numerical modeling of tunneling using the Differential Evolution Genetic Algorithm (DEGA)", Tunnel. Underg. Space Technol., 28, 109-123. https://doi.org/10.1016/j.tust.2011.10.003
  34. White, A.J. (2002), "The use of leak-off tests as means of predicting minimum in-situ stress", Pet. Geosci, 8, 189-193. https://doi.org/10.1144/petgeo.8.2.189
  35. William, W.G.Y. (1981), "Aquifer parameter identification with optimum dimension in parameterization", Water Resour. Res., 17(3), 664-672. https://doi.org/10.1029/WR017i003p00664
  36. Witherspoon, P.A., Wang, J.S., Iwai, K. and Gale, J.E. (1980), "Validity of cubic law for fluid flow in a deformable rock fracture", Water Resour. Res., 6, 1016-1024.
  37. Xu, H., Sang, S., Yang, J., Jin, J., Hu, Y., Liu, H., ... and Gao, W. (2016), "In-situ stress measurements by hydraulic fracturing and its implication on coalbed methane development in Western Guizhou, SW China", J. Unconvent. Oil Gas Resour., 15, 1-10. https://doi.org/10.1016/j.juogr.2016.04.001
  38. Yu, Y.Z., Zhang, B.Y. and Yuan, H.N. (2007), "An intelligent displacement back-analysis method for earth-rockfill dams", Comput. Geotech., 34(6), 423-434. https://doi.org/10.1016/j.compgeo.2007.03.002
  39. Zhang, S. and Yin, S. (2013), "Reservoir geomechanical parameters identification based on ground surface movements", Acta Geotech., 8, 279-292. https://doi.org/10.1007/s11440-012-0196-1
  40. Zhang, X., Last, N., Powrie, W. and Harkness, R. (1999), "Numerical modeling of wellbore behavior in fractured rock masses", J. Petrol. Sci. Eng., 23(2), 95-115. https://doi.org/10.1016/S0920-4105(99)00010-8
  41. Zhao, H. and Yin, S. (2016), "Inverse analysis of geomechanical parameters by artificial bee colony algorithm and multi-output support vector machine", Inver. Prob. Sci. Eng., 24(7), 1266-1281. https://doi.org/10.1080/17415977.2016.1178257
  42. Zhao, H., Ru, Z. and Yin, S. (2012), "Relevance vector machine applied to slope stability analysis", Int. J. Numer. Anal. Meth. Geomech., 36(5), 643-652. https://doi.org/10.1002/nag.1037
  43. Zhao, H., Ru, Z., Chang, X., Yin, S. and Li, S. (2014), "Reliability analysis of tunnel using least square support vector machine", Tunnel. Underg. Space Technol., 41, 14-23. https://doi.org/10.1016/j.tust.2013.11.004
  44. Zhao, H.B. and Yin, S.D. (2009), "Geomechanical parameters identification by particle swarm optimization and support vector machine", Appl. Math. Model., 33, 3997-4012. https://doi.org/10.1016/j.apm.2009.01.011
  45. Zhou, W., Li, S.L., Ma, G., Chang, X.L., Cheng, Y.G. and Ma, X. (2016), "Assessment of the crest cracks of the Pubugou rockfill dam based on parameters back analysis", Geomech. Eng., 11(4), 571-585. https://doi.org/10.12989/gae.2016.11.4.571
  46. Zhu, H.Y., Guo, J.C., Zhao, X., Lu, Q., Luo, B. and Feng, Y.C. (2014), "Hydraulic fracture initiation pressure of anisotropic shale gas reservoirs", Geomech. Eng., 7(4), 403-430. https://doi.org/10.12989/gae.2014.7.4.403