IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v391y2012i21p5208-5214.html
   My bibliography  Save this article

Self-affine and ARX-models zonation of well logging data

Author

Listed:
  • Shiri, Yousef
  • Tokhmechi, Behzad
  • Zarei, Zeinab
  • Koneshloo, Mohammad

Abstract

Zonation of time series into models which their parameters are piecewise constant are important and well-studied problems. Geophysical well logging data often show a complex pattern due to their multifractal nature. In a multifractal system, any pieces of it are established by a distinct exponent that can characterize them. This feature has the capability to cluster them. Self-affine zonation by Auto Regressive model with exogenous inputs (ARX) is a new approach which places well logging segments in the clusters which are more self-affine against the other clusters. This approach was performed and compared with a conventional ARX zonation in the well logging data of three different oilfields in southern parts of Iran. The results showed a good accuracy for detecting homogeneous lithological segments and led to a precise interpretation process to update the reservoir architecture.

Suggested Citation

  • Shiri, Yousef & Tokhmechi, Behzad & Zarei, Zeinab & Koneshloo, Mohammad, 2012. "Self-affine and ARX-models zonation of well logging data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(21), pages 5208-5214.
    • Handle: RePEc:eee:phsmap:v:391:y:2012:i:21:p:5208-5214
    DOI: 10.1016/j.physa.2012.05.025
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437112003962
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2012.05.025?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Dashtian, Hassan & Jafari, G. Reza & Sahimi, Muhammad & Masihi, Mohsen, 2011. "Scaling, multifractality, and long-range correlations in well log data of large-scale porous media," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(11), pages 2096-2111.
    2. Moktadir, Z. & Kraft, M. & Wensink, H., 2008. "Multifractal properties of Pyrex and silicon surfaces blasted with sharp particles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(8), pages 2083-2090.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hernandez-Martinez, Eliseo & Velasco-Hernandez, Jorge X. & Perez-Muñoz, Teresa & Alvarez-Ramirez, Jose, 2013. "A DFA approach in well-logs for the identification of facies associations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(23), pages 6015-6024.
    2. Wei, Yu & Wang, Yudong & Huang, Dengshi, 2011. "A copula–multifractal volatility hedging model for CSI 300 index futures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(23), pages 4260-4272.
    3. Wang, Weiyun & Li, Aimin & Zhang, Xiaomin & Yin, Yulei, 2011. "Multifractality analysis of crack images from indirect thermal drying of thin-film dewatered sludge," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(14), pages 2678-2685.
    4. Henriques, M.V.C. & Leite, F.E.A. & Andrade, R.F.S. & Andrade, J.S. & Lucena, L.S. & Neto, M. Lucena, 2015. "Improving the analysis of well-logs by wavelet cross-correlation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 417(C), pages 130-140.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:391:y:2012:i:21:p:5208-5214. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.