The use of discrete Markov random fields in reservoir characterization

被引:7
|
作者
Salomao, MC [1 ]
Remacre, AZ [1 ]
机构
[1] Univ Estadual Campinas, Inst Geosci, Dept Mineral Resources Policy & Management, BR-13083970 Campinas, SP, Brazil
关键词
Markov random fields; Metropolis algorithm; Gaussian stochastic process; spatial correlation;
D O I
10.1016/S0920-4105(01)00166-8
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
In this work, we discuss relevant aspects concerning the use of discrete Markov random fields (MRF) in the simulation of rock properties in petroleum reservoirs. The Strauss multi-color model is useful to describe complex image configurations, by handling with parameters of repulsion between the different rock facies, symbolized in this case, by the different colors. The transition between the facies and the porous medium anisotropy are imposed to the system, and it is possible to generate various types of arrangement of the facies on the image, in contrast to Gaussian stochastic process, that can only simulate diffusion-type images. Another point focused is the behavior of the spatial correlation in discrete Markov random fields, here studied by the calculus of the practical semivariogram. function in the binary Markov images, generated by using the Metropolis algorithm. These images have a correspondence to Gaussian images with Gaussian-type correlation, after truncated in binary facies. This similarity is validated by analysis in the behavior of the semivariogram function of the discrete Gaussian processes. (C) 2001 Elsevier Science B.V. All rights reserved.
引用
收藏
页码:257 / 264
页数:8
相关论文
共 50 条
  • [21] Strong Markov properties for Markov random fields
    Kinateder, KKJ
    JOURNAL OF THEORETICAL PROBABILITY, 2000, 13 (04) : 1101 - 1114
  • [22] Markov Argumentation Random Fields
    Tang, Yuqing
    Oren, Nir
    Sycara, Katia
    THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, 2016, : 4397 - 4398
  • [23] MARKOV TIMES FOR RANDOM FIELDS
    EVSTIGNEEV, IV
    THEORY OF PROBABILITY AND ITS APPLICATIONS, 1977, 22 (03) : 563 - 569
  • [24] On Functions of Markov Random Fields
    Geiger, Bernhard C.
    Al-Bashabsheh, Ali
    2020 IEEE INFORMATION THEORY WORKSHOP (ITW), 2021,
  • [25] HIDDEN MARKOV RANDOM FIELDS
    Kuensch, Hans
    Geman, Stuart
    Kehagias, Athanasios
    ANNALS OF APPLIED PROBABILITY, 1995, 5 (03): : 577 - 602
  • [26] Factorial Markov random fields
    Kim, J
    Zabih, R
    COMPUTER VISION - ECCV 2002 PT III, 2002, 2352 : 321 - 334
  • [27] Combinatorial Markov random fields
    Bekkerman, Ron
    Sahami, Mehran
    Learned-Miller, Erik
    MACHINE LEARNING: ECML 2006, PROCEEDINGS, 2006, 4212 : 30 - 41
  • [28] Dynamic Markov Random Fields
    Torr, P. H. S.
    2008 INTERNATIONAL MACHINE VISION AND IMAGE PROCESSING CONFERENCE, PROCEEDINGS, 2008, : 21 - 26
  • [29] An information-theoretic characterization of Markov random fields and its applications
    Yeung, RW
    Lee, TT
    Ye, ZX
    1998 IEEE INTERNATIONAL SYMPOSIUM ON INFORMATION THEORY - PROCEEDINGS, 1998, : 73 - 73
  • [30] ONE-DIMENSIONAL MARKOV RANDOM FIELDS, MARKOV CHAINS AND TOPOLOGICAL MARKOV FIELDS
    Chandgotia, Nishant
    Han, Guangyue
    Marcus, Brian
    Meyerovitch, Tom
    Pavlov, Ronnie
    PROCEEDINGS OF THE AMERICAN MATHEMATICAL SOCIETY, 2014, 142 (01) : 227 - 242