Abstract The high resolution 3D nonlinear integrated inversion method is based on nonlinear theory. Under layer control, the log data from several wells (or all wells) in the study area and seismic trace data adjacent to the wells are input to a network with multiple inputs and outputs and are integratedly trained to obtain an adaptive weight function of the entire study area. Integrated nonlinear mapping relationships are built and updated by the lateral and vertical geologic variations of the reservoirs. Therefore, the inversion process and its inversion results can be constrained and controlled and a stable seismic inversion section with high resolution with velocity inversion, impedance inversion, and density inversion sections, can be gained. Good geologic effects have been obtained in model computation tests and real data processing, which verified that this method has high precision, good practicality, and can be used for quantitative reservoir analysis.
The work is supported by the Key Project of the National Natural Scientific Foundation (Grant No. 40839909).
Cite this article:
LI Yong,WANG Xu-Ben,LI Zhi-Rong et al. High resolution 3D nonlinear integrated inversion[J]. APPLIED GEOPHYSICS, 2009, 6(2): 159-165.
[1]
An, H. W., 2002, Chaotic dynamical and detection method for seismic reservoir information: Phd Thesis, Chengdu University of Technology, Chengdu.
[2]
Brac, J., 1988, Inversion with a priori information; an approach to integrated stratigraphic interpretation: 58th Ann. Internat. Mtg., Soc. Expl. Geophys., Expanded Abstracts,?841 - 844.
[3]
Carron, D., and Schlumberger, E. P., 1988, Well guided stratigraphic inversion of borehole and surface seismic sections: 58th Ann. Internat. Mtg., Soc. Expl. Geophys., Expanded Abstracts,?837 - 840.
[4]
Cooke, D. A., and Schneider, W. A., 1983, Generalized linear inversion of reflection seismic data: Geophysics, 48(6), 665 - 676.
[5]
Glover, F., 1986, Future paths for integer programming and links to artificial intelligent: Computers and Operations Research, 13(5),533 - 549.
[6]
Goldberg, D. E., 1989, Genetic algorithms in search, optimization, and machine learning: Addison-Wesley Publishing Company, Inc., Reading, MA, USA.
[7]
Gluck, S., Juve, E., and Lafet, Y., 1997, High-resolution impedance layering through 3-D stratigraphic inversion of poststack seismic data: The Leading Edge, 16(9), 1309 - 1315.
[8]
Jang, S. R., 1993, ANFIS: Adaptive-Network-Based Fuzzy Inference System: IEEE Transaction on System Man and Cybernetics, 23(3), 665 - 685.
Li, Y., 2007, Reservoir imaging mergence and nonlinear detection method for gas-filled reservoirs: Phd Thesis, Chengdu University of Technology, Chengdu, China.
[11]
Li, Y., Li, Z. W., Li, Z. R., and Li, Q., 2007, Reservoir density prediction techniques: Oil Geophysical Prospecting (in Chinese), 42(2),216 - 219.
[12]
Li, Y., Song, Z. P., Li, Q., and Li, Z. W., 2008, Volcanics body seismic recognition technique and applications: Jouranl of Mineralogy and Petrology (in Chinese), 28(3), 105 - 110.
[13]
Martinez, R. D., Cornish, B. E., and Rebec, A. J.,1992, Complex reservoir characterization by multiparameter constrained inversion: Investigations in Geophysics, 7, 224 - 234.
[14]
Oldenburg, D. W., Scheuer, T., and Levy, S., 1983, Recovery of the acoustic impedance from reflection seismograms: Geophysics, 48(10), 1318 - 1337.
[15]
Shen, G. Q., Meng, X. J., Xia, J. Z., Zhang, X. F., and Li, X., 2007, Detailed reservoir inversion addressing geological problems in reservoir development: Applied Geophysics, 4(1), 58 - 65.
[16]
Ulrych, T. J., 1999, The whiteness hypothesis: reflectivity, chaos, inversion and Enders: Geophysics, 64, 1512-1523.
[17]
Ursin, B., and Holberg, O., 1985, Maximum-likelihood estimation of seismic impulse response: Geophysical Prospecting, 33(2), 233 - 251.
[18]
Walker, C., and Ulrych, T. J., 1983, Autoregressive recovery of the acoustic impedance: Geophysics, 48(10), 1338 - 1350.
[19]
Wang, X. P., and Cao, L. M., 2002, Genetic algorithm-theory, application and software realization (in Chinese): Xi’an Communication University Press, ,Xi’an, China.
[20]
Whitleyet, D., 1990, Genetic algorithms and neural networks: optimizing connections and connectivity: Parallel Computation, 14(3), 347 - 361.
[21]
Xiong, Z., 2006, Expectation for oil/gas geophysics in the middle of early 21 century: (in Chinese) Petroleum Industry Press, Beijing.
[22]
Yang, W.C.,1993, Nonlinear chaotic inversion of seismic traces: II. Lyapunov exponents and attractors: Chinese Journal of Geophysics (in Chinese), 36(3),376 - 387.
[23]
Zhang, X. J., Li, Q. X., Yang, L., Li, X. M., and Fu, L., 1999, Seismic trace inversion using borehole restraint and chaotic control: (in Chinese) Oil Geophysical Prospecting, 34(1), 8 - 13.
[24]
Zhou, Z. S., and Zhou, X. X., 1993, Band-constrained inversion: Oil Geophysical Prospecting (in Chinese), 28(5), 523 - 536.