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A DFA approach in well-logs for the identification of facies associations

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  • Hernandez-Martinez, Eliseo
  • Velasco-Hernandez, Jorge X.
  • Perez-Muñoz, Teresa
  • Alvarez-Ramirez, Jose

Abstract

Well-log analysis is a useful tool for the lithological description of wells. Its adequate interpretation allows determining different rock properties such as permeability, density, resistivity and porosity among others. However, given the complexity inherent in the signals, the identification of lithological properties from well-log analysis is not an easy task. In this work, an alternative methodology for sedimentary facies identification based on the detrended fluctuation analysis (DFA) is presented. Our methodology has been calibrated using information from a reference well located at the Chicontepec formation of the Tampico-Misantla basin in Mexico. Its characterization includes the interpretation of different well-logs and cores. Our results indicate that well-log signals present fractal characteristics exhibiting long-range memory. For the gamma ray, resistive and sonic logs a direct relationship between the scaling exponent as a function of the depth and rock types is observed. In this way, the fractal scaling exponents estimated with DFA can be used to identify different sedimentary facies.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:phsmap:v:392:y:2013:i:23:p:6015-6024
    DOI: 10.1016/j.physa.2013.07.052
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    References listed on IDEAS

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    1. Alvarez-Ramirez, Jose & Alvarez, Jesus & Dagdug, Leonardo & Rodriguez, Eduardo & Carlos Echeverria, Juan, 2008. "Long-term memory dynamics of continental and oceanic monthly temperatures in the recent 125 years," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(14), pages 3629-3640.
    2. Marinho, E.B.S. & Sousa, A.M.Y.R. & Andrade, R.F.S., 2013. "Using Detrended Cross-Correlation Analysis in geophysical data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(9), pages 2195-2201.
    3. 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.
    4. Telesca, Luciano & Lovallo, Michele & Lapenna, Vincenzo & Macchiato, Maria, 2007. "Long-range correlations in two-dimensional spatio-temporal seismic fluctuations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 377(1), pages 279-284.
    5. Kantelhardt, Jan W & Koscielny-Bunde, Eva & Rego, Henio H.A & Havlin, Shlomo & Bunde, Armin, 2001. "Detecting long-range correlations with detrended fluctuation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 295(3), pages 441-454.
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    Cited by:

    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).
    3. 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.

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