Fractal analysis of logs to characterize the hydrocarbon and non-hydrocarbon zones of Bhogpara oil field, Northeast India
Tóm tắt
Identification with accuracy of prospective and dry zone from well log data is of prime importance in reservoir or hydrocarbon studies. This issue has greater stake, where in return many conventional methods have been established. The purpose of this study is to recognize the hydrocarbon and non-hydrocarbon bearing portion within a reservoir by using a non-conventional technique. Application of rescaled range (R/S) analysis and wavelet-based fractal analysis (WBFA) on the wire-line log data to obtain the pre-defined hydrocarbon (HC) and non-hydrocarbon (NHC) zones by their fractal nature is demonstrated in this paper. Among these two techniques, the WBFA tool has provided more prolific results. Applicability of the proposed approach is tested with the help of the most commonly used well log data like self-potential, gamma ray, and porosity log responses. These are used in the industry to distinguish several HC and NHC zones of all wells in the study region belonging to the Upper Assam Basin, India. The results are found to be of lower fractal dimension in this study for a particular log response having HC-bearing zones, which are mainly situated in a variety of sandstone lithology. On the other hand, NHC-bearing zones correspond to lithology with higher shale content categorized with higher fractal dimension. Hence, the WBFA technique can overcome the chance of misinterpretation, which is quite possible in the case of conventional reservoir characterization.
Tài liệu tham khảo
Akay M (1995) Wavelets in biomedical engineering. Wavelet transforms in biomedical engineering. Biomed Eng 23:531–542
Barton C C, La Pointe P R (1995) Fractals in petroleum geology and earth processes (Plenum Press, New York 1995)
Berry MV (1979) Diffractals. J Phys A: Math G E N 2:781–797
Chamoli A, Bansal AR, Dimri VP (2007) Wavelet and rescaled range approach for the Hurst coefficient for short and long time series. Comput Geosci 33:83–93
Chamoli A, Dimri VP (2007) Evidence of continental crust over Laxmi Basin (Arabian Sea) using wavelet analysis. Indian J Mar Sci 36:117–121
Crane S E, Tubman K M (1995) Reservoir variability and modeling with fractals. SPE Paper 20606, SPE Ann. Tech.Conf., New Orleans
Dimri VP (2005) Fractal behaviour of the earth system. Springer, New York, p 207
Feder J (1988) Fractals. Plenum Press, New York
Hardy HH (1992) The generation of reservoir property distribution in cross section for reservoir simulation based on core and outcrop photos, SPE paper 23968, presented at SPE Permian Basin Oil and gas recovery conf., Midland, Texas
Hardy HH, Beier RA (1994) Fractals in reservoir engineering. World Scientific, Singapore, p 359
Hewett T A (1986) Fractal distributions of reservoir heterogeneity and their influence on fluid transport, SPE Paper no. 15386, New Orleans
Hurst HE, Black RP, Simaika YM (1965) Long term storage: an experimental study. Constable, London
John A, Lake L W, Torres-Verdin C and Srinivasan S (2005) Seismic facies identification and classification using simple statistic, Proc. SPE Ann. Tech. Conf. Exh., SPE paper no. 96577
Jimenez J R, Peinado A, Michelena R J, (1999) Facies recognition using wavelet-based fractal analysis on compressed seismic data. in Proc 69 th Annu Int Meeting Soc Expl Geophysics 1922–1925
Kumar P, Fufoula-Georgiou E (1994) Wavelet analysis in geophysics. Wavelets in geophysics, ed. Foufoula-Georgiou, E. and Kumar, P. 4, Academic Press, pp 1–43
Lopez M, Aldana M (2007) Facies recognition using wavelet based fractal analysis and waveform classifier at the Oritupano-A field, Venezuela. Nonlinear Process Geophys 14:325–335
Mandelbrot BB (1977) Fractals. Freeman, San Francisco
Mandelbrot B B, Van JW (1968) Ness SIAM Rev 10, 422
Misiti M, Misiti Y, Oppenheim G, Poggi JM (2000) Wavelet toolbox for use with MATLAB user’s guide. The Math Works Inc, Natick, MA, USA, p 572
Mukherjee B, Srivhardhan V, Roy PNS (2016) Identification of bed boundary by using wavelet and Fourier transforms. J Appl Geophys 128:140–149
Pang J, North CP (1996) Fractals and their application in geological wireline log analysis. J Pet Geol 19:339–350
Percival DB, Guttorp P (1994) Long-memory processes, the Allan variance and wavelets. In: Foufoula-Georgiou E, Kumar P (eds) Wavelets in geophysics, vol 4. Academic Press, pp 325–344
Polikar R (1999) The story of wavelets. In: Mastorakis N (ed) Physics and modern topics in mechanical and electrical engineering. World Scientific and Engineering Society Press, Greece, pp 192–197
Rioul O, Vetterli M (1991) Wavelets and signal processing. IEEE Sig. Proc Magazine 8:14–38
Schlumberger (1989) Log interpretation principles/applications. Schlumberger Educational Services, Houston
Serra O (1984) Fundamentals of well-log interpretation-1. Acquisition of logging data. Elsevier, Amsterdam
Shiomi K, Sato H, Ohtake M (1997) Broad-band power-law spectra of well log in Japan. Geophys J Int 130:57–64
Simonsen I, Hanesn A, Nes OM (1998) Determination of the Hurst exponent by use of wavelet transform. Phys Rev E 58:2779–2787
Turcotte DL (2007) Fractals and chaos in geology and geophysics, 2nd edn. Cambridge Uni. Press, New York, p 398