IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v445y2016icp57-65.html
   My bibliography  Save this article

Reservoir characterization using multifractal detrended fluctuation analysis of geophysical well-log data

Author

Listed:
  • Subhakar, D.
  • Chandrasekhar, E.

Abstract

The spatio-temporal variations in geophysical well-log signals, which often reflect their scale invariant properties, can be well studied with multifractal analysis. In this study, we have carried out fractal and multifractal studies using detrended fluctuation analysis (DFA) and multifractal DFA (MFDFA) respectively. While the DFA primarily facilitates to understand the intrinsic self-similarities in non-stationary signals like well-logs by determining the fractal scaling exponents in a modified least-squares sense, the MFDFA, which in fact, is a generalization of DFA, provides a comprehensive understanding of the multifractal behaviour of the signals through multifractal singularity spectrum as well as the Hurst exponents. DFA and MFDFA have been applied to gamma-ray log and neutron porosity logs of two wells (well B and well C), located in the western offshore basin, India, to study the nature of the subsurface formation properties, vis-à-vis their multifractal behaviour. The estimated DFA fractal scaling exponents, represented in the form of contour plots enable easy identification of the depths to the tops of reservoir zones. On the other hand, the multifractal singularity spectra provide a unique platform for an improved interpretation of logs in terms of their sedimentation pattern and lithological differences. This has been tested with gamma-ray log data of wells B and C. We show that the multifractal behaviour of gamma-ray log is largely influenced by the presence of shale and variations in the subsurface sedimentation pattern. Similarly, the role of gas in a pay zone on the multifractal behaviour was established by comparing the multifractal singularity spectra of the original neutron porosity log and a synthetic neutron log (which we call gas-corrected log), generated using density log. The MFDFA of only that portion of the original neutron log representing the pay zone and its gas-corrected equivalent unequivocally suggest that the presence of gas in the reservoir zones weakens the multifractal behaviour of neutron porosity logs. This emphasizes the significance of multifractal studies of well-logs for effective reservoir characterization. The observed multifractal behaviour in all logs is found to be due to the presence of long-range correlations in the data.

Suggested Citation

  • Subhakar, D. & Chandrasekhar, E., 2016. "Reservoir characterization using multifractal detrended fluctuation analysis of geophysical well-log data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 445(C), pages 57-65.
    • Handle: RePEc:eee:phsmap:v:445:y:2016:i:c:p:57-65
    DOI: 10.1016/j.physa.2015.10.103
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437115009620
    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.2015.10.103?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. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Fernandes, Leonardo H.S. & de Araújo, Fernando H.A. & Silva, Igor E.M., 2020. "The (in)efficiency of NYMEX energy futures: A multifractal analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 556(C).
    2. Bhardwaj, Shivam & Gadre, Vikram M. & Chandrasekhar, E., 2020. "Statistical analysis of DWT coefficients of fGn processes using ARFIMA(p,d,q) models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 547(C).
    3. Charutha, S. & Gopal Krishna, M. & Manimaran, P., 2020. "Multifractal analysis of Indian public sector enterprises," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 557(C).
    4. Penghui Su & Zhaohui Xia & Ping Wang & Wei Ding & Yunpeng Hu & Wenqi Zhang & Yujie Peng, 2019. "Fractal and Multifractal Analysis of Pore Size Distribution in Low Permeability Reservoirs Based on Mercury Intrusion Porosimetry," Energies, MDPI, vol. 12(7), pages 1-15, April.
    5. Bhardwaj, Shivam & Chandrasekhar, E. & Seemala, Gopi K. & Gadre, Vikram M., 2020. "Characterization of ionospheric total electron content data using wavelet-based multifractal formalism," Chaos, Solitons & Fractals, Elsevier, vol. 134(C).
    6. Shaw, Pankaj Kumar & Saha, Debajyoti & Ghosh, Sabuj & Janaki, M.S. & Iyengar, A.N. Sekar, 2017. "Investigation of multifractal nature of floating potential fluctuations obtained from a dc glow discharge magnetized plasma," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 363-371.
    7. Gairola, Gaurav S. & Chandrasekhar, E., 2017. "Heterogeneity analysis of geophysical well-log data using Hilbert–Huang transform," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 478(C), pages 131-142.

    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. Lahmiri, Salim, 2017. "Multifractal analysis of Moroccan family business stock returns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 183-191.
    2. Zenteno-Catemaxca, Rolando & Moguel-Castañeda, Jazael G. & Rivera, Victor M. & Puebla, Hector & Hernandez-Martinez, Eliseo, 2021. "Monitoring a chemical reaction using pH measurements: An approach based on multiscale fractal analysis," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).

    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:445:y:2016:i:c:p:57-65. 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.