Data Integration For Electrofacies Classification Using Self-organizing Maps [integração De Dados Para A Classificação De Eletrofácies Por Mapas Auto-organizáveis]

dc.creatorKuroda M.C.
dc.creatorVidal A.C.
dc.creatorBueno J.F.
dc.creatorHonorio B.C.Z.
dc.creatorLeite E.P.
dc.creatorDrummond R.D.
dc.date2011
dc.date2015-06-30T20:35:13Z
dc.date2015-11-26T14:51:46Z
dc.date2015-06-30T20:35:13Z
dc.date2015-11-26T14:51:46Z
dc.date.accessioned2018-03-28T22:03:32Z
dc.date.available2018-03-28T22:03:32Z
dc.identifier
dc.identifierBoletim De Geociencias Da Petrobras. , v. 20, n. 01/02/15, p. 327 - 348, 2011.
dc.identifier1029304
dc.identifier
dc.identifierhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84891752970&partnerID=40&md5=bb829cbe99594519e77fa10c9dd8a584
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/108514
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/108514
dc.identifier2-s2.0-84891752970
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1254592
dc.descriptionIn order to minimize uncertainties inherent to electrofacies classification from well log data, this paper describes nine different strategies for classification using unsupervised neural networks, self-organizing maps (SOM), which combine geophysical information with data derived from well logs, Hölder exponent and acoustic impedance. The method was applied to the Albian carbonate reservoir of the Campos Basin, for which the following well logs were used: sonic (DT), neutron porosity (NPHI), density (RHOB) and gamma ray (GR). Due to the scarcity of core data, the classifications were performed in two steps: first, the classification of well logs and acoustic impedance were performed, which were then used as an additional variable in tests with the algorithm seeking to classify the core data that describe four core data facies (reservoir, possible-reservoir, non-reservoir and cement). The best results of the analysis are associated with the insertion of acoustic impedance information and of such new variable. Adding the new variable to well log samples in the training dataset resulted in a 16% increase in accuracy in core data classification. The results allow the potential integration of seismic data to be quantified in the automatic classification of well data by the SOM method.
dc.description20
dc.description01/02/15
dc.description327
dc.description348
dc.descriptionÁlvarez, G., Sansó, B., Michelena, R.J., Jiménez, J.R., Lithologic characterization of a reservoir using continuous-wavelet transforms (2003) IEEE Transactions on Geoscience and Remote Sensing, 41 (1), pp. 59-65. , New York Jan
dc.descriptionArzuman, S., (2009) Comparison of Geostatistics and Artificial Neural Networks in Reservoir Property Estimation, p. 145. , Tese (Doutorado) - Departamento de Engenharia Geológica, The Middle East Technical University, Ankara-Eskişehir, 2009
dc.descriptionAvseth, P., Mukerju, T., Mavko, G., (2005) Quantitative Seismic Interpretation, 359p. , Cambridge: Cambridge University Press
dc.descriptionBerteussen, K.A., Ursin, B., Approximate computation of the acoustic impedance from seismic data (1983) Geophysics, 48 (10), pp. 1351-1358. , Tulsa Oct
dc.descriptionBhatt, A., Helle, H.B., Determination of facies from well logs using modular neural networks (2002) Petroleum Geoscience, 8 (3), pp. 217-228. , London Aug
dc.descriptionBroadhead, M.K., The impact of random noise on seismic wavelet estimation (2008) The Leading Edge, 27 (2), pp. 226-230. , Los Angeles Feb
dc.descriptionColéou, T., Poupon, M., Azbel, K., Unsupervised seismic facies classification: A review and comparison of techniques and implementation (2003) The Leading Edge, 22 (10), pp. 942-953. , Los Angeles Oct
dc.descriptionDavies, D.L., Bouldin, D.W., A cluster separation measure (1979) IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-1 (2), pp. 224-227. , New York Apr
dc.descriptionDrummond, R.D., Vidal, A.C., Comparação entre as técnicas multivariadas MAF e PCA aplicadas na classificação de eletrofácies (2011) Revista Brasileira de Geofísica, 29 (3), pp. 497-509. , São Paulo jul./set
dc.descriptionEastley, K., Unstead, P., Kloosterman, H.J., Case study: Using seismic inversion to constrain "proved area," definition (2012) The Leading Edge, 31 (10), pp. 1082-1089. , Los Angeles Sept
dc.descriptionEsposito, A.M., Giudicepietro, F., D'Auria, L., Scarpetta, S., Martini, M.G., Coltelli, M., Marinaro, M., Unsupervised neural analysis of very-long-period events at stromboli volcano using the self-organizing maps (2008) Bulletin of the Seismological Society of America, 98 (5), pp. 2449-2459. , Stanford Oct
dc.descriptionGardner, G.H.F., Gardner, L.W., Gregory, A.R., Formation velocity and density - The diagnostic basics for stratigraphic traps (1974) Geophysics, 39 (6), pp. 770-780. , Tulsa Dec
dc.descriptionGrossman, A., Morlet, J., Decomposition of hardy functions into square integrable wavelets of constant shape (1984) SIAM Journal on Mathematical Analysis, 15 (4), pp. 723-736. , Philadelphia July
dc.descriptionHaykin, S., (2001) Redes Neurais: Princípios e Práticas, 900p. , 2. ed. Porto Alegre: Bookman
dc.descriptionKhedairia, S., Khadir, M.T., Self-organizing map and k-means for meteorological day type identification for the region of Annaba-Algeria (2008) COMPUTER INFORMATION SYSTEMS and INDUSTRIAL MANAGEMENT APPLICATIONS, 7, pp. 91-96. , Ostrava. Proceedings⋯ Ostrava: The IEEE Computer Society, 2008
dc.descriptionHerrmann, F., Stark, C., A scale attribute for texture in well and seismic data (2000) SEG Annual International Meeting, 70, pp. 2063-2066. , Calgary. Proceedings⋯ [Tulsa]: Society of Exploration Geophysicists, 2000
dc.descriptionKohonen, T., (2001) Self-organizing Maps, 501p. , 3. ed. Berlin: Springer
dc.descriptionKuroda, M.C., Vidal, A.C., Leite, E.P.L., Drummond, R.D., Electrofacies characterization using self-organizing maps (2012) Revista Brasileira de Geofísica, 30 (3), pp. 287-299. , São Paulo
dc.descriptionLeite, E.P., Souza Filho, C.R., Mapas auto-organizáveis aplicados ao mapeamento do potencial mineral na região de serra leste, província mineral de carajás, pará (2010) Revista Brasileira de Geofísica, 28 (3), pp. 397-409. , jul./set
dc.descriptionLi, C.F., (2002) Scaling and Wavelet-based Singularity Analyses for Geological and Geophysical Interpretation, p. 130. , Tese (Doutorado) - University of Tulsa, Tulsa, 2002
dc.descriptionLi, C.F., Liner, C., Singularity exponent from wavelet-based multiscale analysis: A new seismic attribute (2005) Chinese Journal of Geophysics, 48 (4), pp. 953-959. , New York
dc.descriptionLi, C.F., Liner, C., Wavelet-based detection of singularities in acoustic impedances from surface seismic reflection data (2008) Geophysics, 73 (1), pp. v1-v9. , Tulsa Jan./Feb
dc.descriptionLoubes, J.M., Van De Geer, S., Adaptive estimation in regression, using soft thresholding type penalties (2002) Statistica Neerlandica, 56 (4), pp. 453-478. , Rotterdam Nov
dc.descriptionLyons, W.J., Herrmann, F., Grotzinger, J., Singularity analysis: A tool for extracting lithologic and stratigraphic content from seismic data (2001) SEG Annual International Meeting, 71, pp. 1-14. , San Antonio. Proceedings⋯ [Tulsa]: Society of Exploration Geophysicists, 2001
dc.descriptionMallat, S., (1998) A Wavelet Tour of Signal Processing, 577p. , London: Academic Press
dc.descriptionMallat, S., Hwang, W.L., Singularity detection and processing with wavelets (1992) IEEE Transactions on Information Theory, 38 (2), pp. 617-643. , New York Mar
dc.descriptionMallat, S., Zhong, S., Characterization of signals from multiscale edges (1992) IEEE Transactions on Pattern Analysis and Machine Intelligence, 14 (7), pp. 710-732. , New York July
dc.descriptionMatos, M.C., Osorio, P.L.M., Johann, P.R.S., Unsupervised seismic facies analysis using wavelet transform and self-organizing maps (2007) Geophysics, 72 (1), pp. P9-P21. , Tulsa Jan./Feb
dc.descriptionMeldahl, P., Heggland, R., Bril, B., Groot, P., Identifying faults and gas chimneys using multiattributes and neural networks (2001) The Leading Edge, 20 (5), pp. 474-482. , Los Angeles May
dc.descriptionOldenburg, D.W., Scheuer, T., Levy, T., Recovery of the acoustic impedance from reflection seismograms (1983) Geophysics, 48 (10), pp. 1318-1337. , Tulsa Oct
dc.descriptionPoulton, M.M., (2001) Computational Neural Networks for Geophysical Data Processing, 335p. , New York: Pergamon
dc.descriptionRussel, B.H., (1988) Introduction to Seismic Inversion Methods, , Tulsa: Society of Exploration Geophysicists (Course notes series, n. 2)
dc.descriptionSacchi, M.D., Ulrych, T.J., Bayesian regularization of some seismic operators (1996) Maximum Entropy and Bayesian Methods, pp. 431-436. , HANSON, K. M.
dc.descriptionSILVER, R. N. (Ed.) Boston: Kluwer Academic Publishers
dc.descriptionSaggaf, M.M., Nebrija, E.L., Estimation of lithologies and depositional facies from wire-line logs (2000) AAPG Bulletin, 84 (10), pp. 1633-1646. , Tulsa
dc.descriptionSancevero, S.S., Remacre, A.Z., Portugal, R.S., O papel da inversão para a impedância acústica no processo de caracterização sísmica de reservatórios (2006) Revista Brasileira de Geofísica, 24 (4), pp. 495-512. , São Paulo out./nov
dc.descriptionSeuret, S., Gilbert, A., Pointwise hölder exponent estimation in data network traffic (2000) International Teletraffic Congress Workshop, pp. 1-10. , Monterey. Proceedings⋯ Monterey: [s.n.], 2000
dc.descriptionStundner, M., Oberwinkler, C., Self-organizing maps for lithofacies identification and permeability prediction (2004) SPE ANNUAL TECHNICAL CONFERENCE and EXHIBITION, pp. 1-8. , Houston. Proceedings⋯ [Richardson]: Society of Petroleum Engineers, 2004
dc.descriptionTarantola, A., Valette, B., Inverse problems = quest for information (1982) Journal of Geophysics, 50, pp. 159-170. , Berlin
dc.descriptionTu, C., Hwang, W., Analysis of singularities from modulus maxima of complex wavelets (2005) IEEE Transactions on Information Theory, 51 (3), pp. 1049-1062. , New York Mar
dc.descriptionVesanto, J., Himberg, J., Alhoniemi, E., Parhankangas, J., (2000) SOM Toolbox for Matlab 5, 59p. , Finland: Helsinki University of Technology (Technical Report, A57)
dc.descriptionYang, Y., Aplin, A.C., Larter, S.R., Quantitative assessment of mudstone lithology using geophysical wireline logs and artificial neural networks (2004) Petroleum Geoscience, 10 (2), pp. 141-151. , United Kingdom Apr
dc.descriptionBjorck, A., (1996) Numerical Methods for Least Squares Problems, 408p. , Philadelphia: Society for Industrial and Applied Mathematics
dc.languagept
dc.languageen
dc.publisher
dc.relationBoletim de Geociencias da Petrobras
dc.rightsfechado
dc.sourceScopus
dc.titleData Integration For Electrofacies Classification Using Self-organizing Maps [integração De Dados Para A Classificação De Eletrofácies Por Mapas Auto-organizáveis]
dc.typeArtículos de revistas


Este ítem pertenece a la siguiente institución