Self-similar segmentation and multifractality of post-stack seismic data

  • Hedayati Rad ELYAS ,
  • Hassani HOSSEIN ,
  • Shiri YOUSEF ,
  • Jamal Sheikh Zakariaee SEYED
Expand
  • 1. Department of Petroleum Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
    2. Department of Mining and Metallurgy Engineering, Amirkabir University of Technology (Polytechnic of Tehran), Tehran, Iran
    3. Shahrood University of Technology, Faculty of Mining, Petroleum and Geophysics Engineering, Shahrood, Iran

Received date: 2020-04-01

  Revised date: 2020-05-28

  Online published: 2020-08-28

Abstract

Layering detection is an important step in petroleum engineering. Time series of post-stack seismic data and wire-line log data belong to subsurface layering. They exhibit multifractal properties with complex patterns because of the heterogeneity and different genetic properties in the earth layers. In a multifractal configuration, any piece of a series has a distinct Hurst exponent that reflects its nature and can be used for zone detection. Time series are post-stack seismic traces and wire-line log data near the well-bores. Self-similar Autoregressive Exogenous (SAE) model is a modified method which can place self-similar post-stack seismic and wire-line log segments across layers with the same lithology. The results satisfy the capability of layering identification from seismic data by SAE model.

Cite this article

Hedayati Rad ELYAS , Hassani HOSSEIN , Shiri YOUSEF , Jamal Sheikh Zakariaee SEYED . Self-similar segmentation and multifractality of post-stack seismic data[J]. Petroleum Exploration and Development, 2020 , 47(4) : 781 -790 . DOI: 10.1016/S1876-3804(20)60093-3

References

[1] KOESTER J, KULKE H . Sedimentology and petroleum geology: Book review. New York: Springer-Verlag, 1993.
[2] SHIRI Y, TOKHMECHI B, ZAREI Z , et al. Self-affine and ARX-models zonation of well logging data. Physica A: Statistical Mechanics and Its Applications, 2012,391(21):5208-5214.
[3] MALAMUD B D, TURCOTTE D L . Advances in geophysics. Amsterdam: Elsevier, 1999: 1-90.
[4] DANOS M. Fractals and chaos in geology and geophysics. 2nd ed. London: Cambridge University Press, 2006.
[5] DELIGNIèRES D . Correlation properties of (discrete) fractional Gaussian noise and fractional Brownian motion. Mathematical Problems in Engineering, 2015,2015:1-7.
[6] HURST H E . Long-term storage capacity of reservoirs. Transactions of the Amererical Society of Civil Engineers, 1951,116:770-808.
[7] TURCOTTE D L. Fractals and chaos in geology and geophysics. London: Cambridge University Press, 1992.
[8] MOKTADIR Z, KRAFT M, WENSINK H . Multifractal properties of pyrex and silicon surfaces blasted with sharp particles. Physica A: Statistical Mechanics and Its Applications, 2008,387(8/9):2083-2090.
[9] BANSAL A R, DIMRI V P . Self-affine gravity covariance model for the Bay of Bengal. Geophysical Journal International, 2005,161(1):21-30.
[10] FEDI M . Global and local multiscale analysis of magnetic susceptibility data. Pure and Applied Geophysics, 2003,160(12):2399-2417.
[11] DIMRI V. Application of fractals in earth sciences. Florida, USA: CRC Press, 2000.
[12] MILNE B T . Measuring the fractal geometry of landscapes. Applied Mathematics and Computation, 1988,27(1):67-79.
[13] DAS S . Functional fractional calculus. Berlin: Springer Science & Business Media, 2011.
[14] WITT A, MALAMUD B D . Quantification of long-range persistence in geophysical time series: Conventional and benchmark-based improvement techniques. Surveys in Geophysics, 2013,34(5):541-651.
[15] MANDELBROT B B . Intermittent turbulence in self-similar cascades: Divergence of high moments and dimension of the carrier. Journal of Fluid Mechanics, 1974,62(2):331-358.
[16] MANDELBROT B B . Pure and applied geophysics PAGEOPH. Berlin: Springer, 1989: 5-42.
[17] DIMRI V P, GANGULI S S . Fractal theory and its implication for acquisition, processing and interpretation(API) of geophysical investigation: A review. Journal of the Geological Society of India, 2019,93(2):142-152.
[18] GANGULI S S, DIMRI V P . Interpretation of gravity data using eigenimage with Indian case study: A SVD approach. Journal of Applied Geophysics, 2013,95:23-35.
[19] ALI S, SHAHZAD S J H, RAZA N, , et al. Stock market efficiency: A comparative analysis of Islamic and conventional stock markets. Physica A: Statistical Mechanics and Its Applications, 2018,503:139-153.
[20] DASHTIAN H, JAFARI G R, SAHIMI M , et al. Scaling, multifractality, and long-range correlations in well log data of large-scale porous media. Physica A: Statistical Mechanics and Its Applications, 2011,390(11):2096-2111.
[21] HERNANDEZ-MARTINEZ E, VELASCO-HERNANDEZ J X, PEREZ-MU?OZ T, , et al. A DFA approach in well-logs for the identification of facies associations. Physica A: Statistical Mechanics and Its Applications, 2013,392(23):6015-6024.
[22] TANG Y J, CHANG Y F, LIOU T S , et al. Evolution of the temporal multifractal scaling properties of the Chiayi earthquake(ML=6.4), Taiwan. Tectonophysics, 2012,546/547:1-9.
[23] TELESCA L, LOVALLO M, MAMMADOV S , et al. Power spectrum analysis and multifractal detrended fluctuation analysis of Earth’s gravity time series. Physica A: Statistical Mechanics and Its Applications, 2015,428:426-434.
[24] SUBHAKAR D, CHANDRASEKHAR E . Reservoir characterization using multifractal detrended fluctuation analysis of geophysical well-log data. Physica A: Statistical Mechanics and Its Applications, 2016,445:57-65.
[25] STANLEY H E, MEAKIN P . Multifractal phenomena in physics and chemistry. Nature, 1988,335(6189):405-409.
[26] MOLINO-MINERO-RE E, GARCíA-NOCETTI F, BENíTEZ- PéREZ H . Application of a time-scale local hurst exponent analysis to time series. Digital Signal Processing: A Review Journal, 2015,37(1):92-99.
[27] TOKHMECHI B, RASOULI V, AZIZI H , et al. Hybrid clustering-estimation for characterization of thin bed heterogeneous reservoirs. Carbonates and Evaporites, 2019,34(3):917-929.
[28] SAHIMI M . Flow and transport in porous media and fractured rock: From classical to modern approaches. New Jersey, USA: John Wiley & Sons, 2010.
[29] AMINIKHANGHAHI S, COOK D J . A survey of methods for time series change point detection. Knowledge and Information Systems, 2017,51(2):339-367.
[30] CHOPRA S, MARFURT K J . Seismic attributes for prospect identification and reservoir characterization. Tulsa: Society of Exploration Geophysicists and European Association of Geoscientists and Engineers, 2007.
[31] HUANG Y P, GENG J H, GUO T L . New seismic attribute: Fractal scaling exponent based on gray detrended fluctuation analysis. Applied Geophysics, 2015,12(3):343-352.
[32] NATH S K, DEWANGAN P . Detection of seismic reflections from seismic attributes through fractal analysis. Geophysical Prospecting, 2002,50(3):341-360.
[33] LOUIE J N, MELA K . Correlation length and fractal dimension interpretation from seismic data using variograms and power spectra. Geophysics, 2001,66(5):1372-1378.
[34] DONG J, LANG Z, GONG W , et al. Development of oil development and gas prediction software system based on fractal dimension of amplitude spectrum. Computer Applications of Petroleum, 2018,26(1):6-8.
[35] KANTELHARDT J W, KOSCIELNY-BUNDE E, REGO H H A , et al. Detecting long-range correlations with detrended fluctuation analysis. Physica A: Statistical Mechanics and Its Applications, 2001,295(3/4):441-454.
[36] HU K, IVANOV P C, CHEN Z , et al. Effect of trends on detrended fluctuation analysis. Physical Review E: Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics, 2001,64(1):1-19.
[37] CHEN Z, IVANOV P C, HU K , et al. Effect of nonstationarities on detrended fluctuation analysis. Physical Review E: Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics, 2002,65(4):1-17.
[38] HAVLIN S, BULDYREV S V, BUNDE A , et al. Scaling in nature: From DNA through heartbeats to weather. Physica A: Statistical Mechanics and Its Applications, 1999,273(1/2):46-69.
[39] OSSADNIK S M, BULDYREV S V, GOLDBERGER A L , et al. Correlation approach to identify coding regions in DNA sequences. Biophysical Journal, 1994,67(1):64-70.
[40] BILLAT V L, MILLE-HAMARD L, MEYER Y , et al. Detection of changes in the fractal scaling of heart rate and speed in a marathon race. Physica A: Statistical Mechanics and Its Applications, 2009,388(18):3798-3808.
[41] BUDINSKI-PETKOVI? L, LON?AREVI? I, JAK?I? ZM, , et al. Fractal properties of financial markets. Physica A: Statistical Mechanics and Its Applications, 2014,410:43-53.
[42] SU Z Y, WU T . Music walk, fractal geometry in music. Physica A: Statistical Mechanics and Its Applications, 2007,380(1/2):418-428.
[43] KANTELHARDT J W . Encyclopedia of complexity and systems science. Berlin: Springer, 2015: 1-37.
[44] CAO G, HE L Y, CAO J . Multifractal detrended analysis method and its application in financial markets. Beilin: Springer, 2018.
[45] MATIA K, ASHKENAZY Y, STANLEY H E . Multifractal properties of price fluctuations of stocks and commodities. Europhysics Letters, 2003,61(3):422-428.
[46] THEILER J, GALDRIKIAN B, LONGTIN A , et al. Using surrogate data to detect nonlinearity in time series. Nonlinear Modeling and Forecasting, 1992(XII):163-188.
[47] HENRY B, LOVELL N, CAMACHO F . Nonlinear dynamics time series analysis. Nonlinear Biomedical Signal Processing: Dynamic Analysis and Modeling, 2001(2):1-39.
[48] THEILER J, EUBANK S, LONGTIN A , et al. Testing for nonlinearity in time series: The method of surrogate data. Physica D: Nonlinear Phenomena, 1992,58(1/2/3/4):77-94.
[49] ZENG J G, SHU YQ, ZHONG Y . Fractal and chaotic characteristics of seismic data. Oil Geophysical Prospecting, 1995,30(6):743-748.
[50] WU Chao, CHEN Mian, JIN Yan . Real-time prediction method of borehole stability. Petroleum Exploration and Development, 2008,35(1):80-84.
[51] LI W K, TONG H . Time series: Advanced methods. Amsterdam: Elsevier, 2015.
[52] YAO R, PAKZAD S N . Autoregressive statistical pattern recognition algorithms for damage detection in civil structures. Mechanical Systems and Signal Processing, 2012,31:355-368.
[53] CHAMOLI A, RAM BANSAL A, DIMRI V P . Wavelet and rescaled range approach for the Hurst coefficient for short and long time series. Computers and Geosciences, 2007,33(1):83-93.
[54] MADAN S, SRIVASTAVA K, SHARMILA A , et al. A case study on discrete wavelet transform based Hurst exponent for epilepsy detection. Journal of Medical Engineering and Technology, 2018,42(1):9-17.
[55] HOSSEINI S A, AKBARZADEH T M R, NAGHIBI- SISTANI M B, . Intelligent technologies and techniques for pervasive computing. Pennsylvania, USA: IGI Global, 2013: 20-36.
[56] LJUNG L . MATLAB system identification toolbox. Massachusetts, USA: MathWorks, 2007.
[57] OHLSSON H, LJUNG L, BOYD S . Segmentation of ARX- models using sum-of-norms regularization. Automatica, 2010,46(6):1107-1111.
[58] BASSEVILLE M . Detection of abrupt changes in signal processing. New Jersey, USA: Prentice Hall, Englewood Cliffs, 1990: 99-101.
[59] GUSTAFSSON F . Adaptive filtering and change detection. New York: Citeseer, 2000.
[60] BALAKRISHNAN V . System identification: Theory for the user. 2nd ed. New Jersey, USA: Prentice-Hall, 2002.
[61] DOCTORALE E, SCIENCES D E S, INGENIEUR P L, . Initialization of output error identification algorithms. Beilin: Springer, 2006.
[62] MCQUARRIE N . Crustal scale geometry of the Zagros fold- thrust belt, Iran. Journal of Structural Geology, 2004,26(3):519-535.
[63] RAHIMI R, BAGHERI M, MASIHI M . Characterization and estimation of reservoir properties in a carbonate reservoir in Southern Iran by fractal methods. Journal of Petroleum Exploration and Production Technology, 2018,8(1):31-41.
[64] BO Y Y, LEE G H, KIM H J , et al. Comparison of wavelet estimation methods. Geosciences Journal, 2013,17(1):55-63.
[65] ASHCROFT W . A petroleum geologist’s guide to seismic reflection. New Jersey, USA: John Wiley & Sons, 2011.
[66] LIU Ming, ZHANG Shicheng, MOU Jianye . Fractal nature of acid-etched wormholes and the influence of acid type on wormholes. Petroleum Exploration and Development, 2012,39(5):630-635.
[67] CHEN Y, ZHANG L, LI J . Nano-scale pore structure and fractal dimension of lower Silurian Longmaxi Shale. Chemistry and Technology of Fuels and Oils, 2018,54(3):354-366.
[68] JIANG F, CHEN D, CHEN J , et al. Fractal analysis of shale pore structure of continental gas shale reservoir in the Ordos Basin, NW China. Energy & Fuels, 2016,30(6):4676-4689.
[69] CHEN L, JIANG Z X, JIANG S , et al. Nanopore structure and fractal characteristics of lacustrine shale: Implications for shale gas storage and production potential. Nanomaterials, 2019,9(3):390.
[70] WANG X, JIANG Z, JIANG S , et al. Full-scale pore structure and fractal dimension of the Longmaxi shale from the southern Sichuan Basin: Investigations using FE-SEM, gas adsorption and mercury intrusion porosimetry. Minerals, 2019,9(9):543-555.
[71] KROHN C E . Fractal measurements of sandstones, shales, and carbonates. Journal of Geophysical Research: Solid Earth, 1988,93(B4):3297-3305.
[72] SONG Z, MA C, ZHANG M . The fractal properties of calcination of limestone and its sulfidation with H2S. Wuhan, China: 2010 Asia-Pacific Power and Energy Engineering Conference, 2010.
[73] WU X, LONG S, LI G . Fractal study on the complexity of limestone surface pore structure. Advanced Materials Research, 2012,548:275-280.
Outlines

/