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An Information Integrated Approach for Reservoir Characterization

Joint Lithologic Inversion
  • Li-Yun Fu
Part of the Modern Approaches in Geophysics book series (MAGE, volume 21)

Abstract

Ambiguous dependence of observed data related to lithologic parameters suggests that practical lithologic inversion problems are characterized by both deterministic mechanism and statistical behavior. The Caianiello neural network method is presented in this paper, including neural wavelet estimation, input signal reconstruction, and nonlinear factor optimization. A joint inversion scheme for porosity and clay-content estimations is established based on the combination of the Caianiello neural network with some deterministic petrophysical models. First, inverse neural wavelets are extracted using known solutions, and then the inverse-operator-based inversion is used to estimate an initial parameter model. Second, forward neural wavelets are estimated likewise, and then the forward-operator-based reconstruction can improve the initial parameter model. The scheme has been applied in a complex continental deposit in western China and significantly improves the spatial description of reservoirs.

Keywords

Seismic Data Joint Inversion Seismic Attribute Reservoir Characterization Nonlinear Factor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. An, P., and Moon, W.M., 1993, Reservoir characterization using feedforward neural networks: 63th Ann. Internat. Mtg., Soc. Expl. Geophys., Expanded Abstracts, 258–262.Google Scholar
  2. Angeleri, G.P., and Carpi, R., 1982, Porosity prediction from seismic data: Geophys. Prosp., 30, 580–607.CrossRefGoogle Scholar
  3. Baldwin, J.L., Bateman, R.M., and Wheatley, C.L., 1990, Application of a neural network to the problem of mineral identification from well logs: The Log Analyst, 31, 279–293.Google Scholar
  4. Bolt, D.G., Daneel, G., and Clare, A., 1997, Prospect Explorer: an exploration neural analysis tool: 67th Ann. Intemat. Mtg., Soc. Expl. Geophys., Expanded Abstracts, 2041–2044.Google Scholar
  5. Burge, D.W., and Neff, D.B., 1998, Well-based seismic lithology inversion for porosity and pay-thickness mapping: The Leading Edge, 17, 166–171.Google Scholar
  6. Caianniello, E.R., 1961, Outline of a theory of thought-processes and thinking machines: J. Theoret. Biol., 2, 204–235.CrossRefGoogle Scholar
  7. Calderon-Macias, C., Sen, M.K., and Stoffa, P.L., 1996, A neural network optimization approach for automatic NMO correction and velocity estimation: 66th Ann. Internat. Mtg., Soc. Expl. Geophys., Expanded Abstracts, 1979–1982.Google Scholar
  8. Clark, G.A., and Glinsky, M.E., Sandhya Devi, K.R., Robinson, J.H., Cheng, P.K.Z., and Ford, G.E., 1996, Automatic event picking in pre-stack migrated gathers using a probabilistic neural network: 66th Ann. Intemat. Mtg., Soc. Expl. Geophys., Expanded Abstracts, 735–738.CrossRefGoogle Scholar
  9. de Buyl, M., Guidish, T., and Bell, F., 1986, Reservoir description from seismic lithologie parameter estimation: J. Petr. Tech., 40, 475–482.Google Scholar
  10. Doyen, P.M., 1988, Porosity from seismic data: A geostatistical approach: Geophysics, 53, 1263–1275.Google Scholar
  11. Duijndam, A.J.W., 1988a, Bayesian estimation in seismic inversion. Part I: Principles: Geophys. Prosp., 36, 878–898.CrossRefGoogle Scholar
  12. Duijndam, A.J.W., 1988b, Bayesian estimation in seismic inversion. Part II: Uncertainty analysis: Geophys. Prosp., 36, 899–918.CrossRefGoogle Scholar
  13. Ecoublet, P., Symes, W.W., and Levin, S., 1997, Seismic inversion for porosity using a backpropagation neural network: TRIP Annual Report 97, Rice University.Google Scholar
  14. Essenreiter, R., and Karrenbach, M., 1996, Deconvolution using neural networks: 66th Ann. Intemat. Mtg., Soc. Expl. Geophys., Expanded Abstracts, 1611–1614.Google Scholar
  15. Fish, B., and Kasuma, T., 1994, A neural network approach to automate velocity picking: 64th Ann. Intemat. Mtg., Soc. Expl. Geophys., Expanded Abstracts, 185–188.Google Scholar
  16. Fournier, F., and Derain, J.F., 1995, A statistical methodology for deriving reservoir properties from seismic data: Geophysics, 60, 1437–1450.Google Scholar
  17. Fu, L.Y., 1995, An artificial neural network theory and its application to seismic data processing: PhD thesis, University of Petroleum, Beijing, PRC.Google Scholar
  18. Fu, L.Y., Chen, S., and Duan, Y., 1997, ANNLOG technique for seismic wave impedance inversion and its application effect: Oil Geophysical Prospecting: 32, 34–44.Google Scholar
  19. Fu, L.Y., 1997, Application of the Caianiello neuron-based network to joint inversion: 67th Ann. Internat. Mtg., Soc. Expl. Geophys., Expanded Abstracts, 1624–1627.Google Scholar
  20. Fu, L.Y., 1999a, A neuron filtering model and its neural network for space-and time-varying signal processing: Third International Conference on Cognitive and Neural systems, Boston University, Paper Vision B03.Google Scholar
  21. Fu L.Y., 1999b, Joint inversion for acoustic impedance: Geophysics, accepted.Google Scholar
  22. Gelfand, V.A., and Lamer, K.L., 1984, Seismic lithologic modeling: The leading Edge, 3, no. 11, 30–34.CrossRefGoogle Scholar
  23. Han, D.H., Nur, A., and Morgan, D., 1986, Effects of porosity and clay content on wave velocities in sandstones: Geophysics, 51, 2093–2107.Google Scholar
  24. Hart, D.I., 1996, Improving the reliability of first-break picking with neural networks: 66th Ann. Internat. Mtg., Soc. Expl. Geophys., Expanded Abstracts, 1662–1665.Google Scholar
  25. He, N.Q., and Reynolds, A.C., 1995, Estimation of porosity in thin-layered reservoirs by seismic inversion: 65th Ann. Internat. Mtg., Soc. Expl. Geophys., Expanded Abstracts, 1022–1024.Google Scholar
  26. Himmer, P., and Link, C., 1997, Reservoir porosity prediction from 3-D seismic data using neural networks: 67th Ann. Internat. Mtg., Soc. Expl. Geophys., Expanded Abstracts, 842–845.Google Scholar
  27. Huang, K. -Y., Chang, W.R.I., and Yen, H.T., 1990, Self-organising neural network for picking seismic horizons: 60th Ann. Intemat. Mtg., Soc. Expl. Geophys., Expanded Abstracts, 313–315.Google Scholar
  28. Huang, Z., Shimeld, J., Williamson, M., and Katsube, J., 1996, Permeability prediction with artificial neural network modeling in the Venture gas field, offshore eastern Canada: Geophysics, 61, 422–436.Google Scholar
  29. Jackson, D.D., and Matsu’ura, M., 1985, A Bayesian approach to nonlinear inversion: J. Geophys. Res., 90, 581–591.CrossRefGoogle Scholar
  30. Klimentos, T., and McCann, C., 1990, Relationships among compressional wave attenuation, porosity, clay content, and permeability in sandstones: Geophysics, 55, 998–1014.Google Scholar
  31. Kowallis, B., Jones, L.E., and Wang, H.F., 1984, Velocity-porosity-clay content; systematics of poorly consolidated sandstones: J. Geophys. Res., 89, 10355–10364.CrossRefGoogle Scholar
  32. Leggett, M., Sandham, W.A., and Durrani, T.S., 1994, 3-D Seismic horizon tracking using an artificial neural network: 56th Meeting, Eur. Assoc. Expl. Geophys., Paper B049.Google Scholar
  33. Lines, L.R. and Treitel, S., 1984, A Review of least square inversion and its application to geophysical problems: Geophys. Prosp., 32, 159–186.CrossRefGoogle Scholar
  34. Lortzer, G.J.M., and Berkhout, A.J., 1992, An integrated approach to lithologie inversion. Part I: Theory: Geophysics, 57, 233–244.Google Scholar
  35. Martinez, R. D., 1985, Deterministic estimation of porosity and formation pressure from seismic data: 55th Ann. Internat. Mtg., Soc. Expl. Geophys., Expanded Abstracts, 461–464.Google Scholar
  36. Maureau, G.T.F.R., and Van Wijhe, D.H., 1979, The prediction of porosity in the Permian carbonate of eastern Netherlands using seismic data: Geophysics, 44, 1502–1517.Google Scholar
  37. McCormack, M.D., Zuacha, D.E., and Dushek, D.W., 1993, First-break refraction event picking and seismic data trace editing using neural networks: Geophysics, 58, 67–78.Google Scholar
  38. McCulloch, W.S., and Pitts, W., 1943, A logical calculus of the ideas immanent in nervousactivity: Bull. of Math. Bio., 5, 115–133.CrossRefGoogle Scholar
  39. Neff D.B., 1990a, Incremental pay thickness modeling of hydrocarbon reservoirs: Geophysics, 55, 558–566.Google Scholar
  40. Neff D.B., 1990b, Estimated pay mapping using three-dimensional seismic data and incremental pay thickness modeling: 55, 567–575..Google Scholar
  41. Neff D.B., 1993, Amplitude map analysis using forward modeling in sandstone and carbonate reservoirs: Geophysics, 58, 1428–1441.Google Scholar
  42. Nur, A., 1992, The role of critical porosity in the physical response of rocks: EOS, Trans. AGU, 43, 66.Google Scholar
  43. Nur, A., Mavko, G., Dvorkin, J., and Galmudi, D., 1998, Critical porosity: A key to relating physical properties to porosity in rocks: The Leading Edge, 17, 357–362.Google Scholar
  44. Pan, G.C., 1996, Recent development for optimum data integration in mineral exploration: 66th Ann. Internat. Mtg., Soc. Expl. Geophys., Expanded Abstracts, 646–649.Google Scholar
  45. Poulton, M.M.; Sternberg, B.K., and Glass, C.E., 1992, Location of subsurface targets in geophysical data using neural networks: Geophysics, 57, 1534–1544.Google Scholar
  46. Raymer, D.S., Hunt, E.R., and Gardner, J.S., 1980, An improved sonic transit time-to-porosity transform: Presented at the Soc. Prof. Well Log Anal. 21st Ann. Mtg., Paper P.Google Scholar
  47. Robinson, E.A., 1957, Predictive decomposition of seismic traces: Geophysics, 22, 767–778.Google Scholar
  48. Rogers, S., Fang, J.H., Karr, C.L., and Stanley, D.A., 1992, Determination of lithology from well logs using a neural network: AAPG Bull., 76, 731–739.Google Scholar
  49. Russell, B., Hampson, D., Schuelke, J., and Quirein, J., 1997, Multiattribute seismic analysis: The Leading Edge, 16, 1439–1443.CrossRefGoogle Scholar
  50. Schultz, P.S., Ronen, S., Hattori, M., and Corbett, C., 1994, Seismic guided estimation of log properties, Part 1: The Leading Edge, 13, part 1, 305–310, part 2, 674–678, part 3, 770–776.Google Scholar
  51. Tarantola, A., and Valette, B., 1982, Inverse problems: Quest for information: J. Geophys., 50, 159–170.Google Scholar
  52. Thadani, S.G., Alabert, F., and Journel, A.G., 1987, An integrated geostatistical/pattern recognition technique for characterization of reservoir spatial variability: 57th Ann. Internat. Mtg., Soc. Expl. Geophys., Expanded Abstracts, 372–375.Google Scholar
  53. Tosaya, C., and Nur, A., 1982, Effects of diagenesis and clays on compressional velocities in rocks: Geophys. Res. Letts., 9, 5–8.CrossRefGoogle Scholar
  54. Vernik, L., 1994, Predicting lithology and transport properties from acoustic velocities based on petrophysical classification of siliciclastics: Geophysics, 63, 420–427.Google Scholar
  55. Vernik, L., and Nur, A., 1992, Petrophysical classification of siliciclastics for lithology and porosity prediction from seismic velocities: AAPG Bull., 76, 1295–1309.Google Scholar
  56. Wang, B., Pann, K., Schuelke, J., Shirley, T., and Ferguson, B., 1997, View of neural network training as constrained optimization and applications to rock porosity prediction: 67th Ann. Internat. Mtg., Soc. Expl. Geophys., Expanded Abstracts, 838–841.Google Scholar
  57. Wang, L.X., and Mendel, J.M., 1992, Adaptive minimum prediction-error deconvolution and source wavelet estimation using Hopfield neural networks: Geophysics, 57, 670–679.Google Scholar
  58. Wyllie, M.R.J., Gregory, A.R., and Gardner, G.H.F., 1958, An experimental investigation of factors affecting elastic wave velocities in porous media: Geophysics, 23, 459–493.Google Scholar
  59. Zhang, X., Li, Y., Hu, Q., and Feng, D., 1995, Early-stage reservoir analysis with SOMA: A neural network approach: 65th Ann. Internat. Mtg., Soc. Expl. Geophys., Expanded Abstracts, 138–141.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2003

Authors and Affiliations

  • Li-Yun Fu
    • 1
    • 2
  1. 1.CSIRO PetroleumBentley, PerthW. Australia
  2. 2.Formerly Institute of TectonicsUniversity of California at Santa CruzSanta CruzUSA

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