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Boolean spectral analysis in categorical reservoir modeling. (English) Zbl 1465.86013

Summary: This work introduces a new method for simulating facies distribution with two categories based on Fourier analysis of Boolean functions. According to this method, two categories of facies distributed along vertical wells are encoded as Boolean functions taking two values. The subsequent simulation process is divided into three consecutive steps. First, Boolean functions of the well data are decomposed into a binary version of a Fourier series. Decomposition coefficients are then simulated over the two-dimensional area as stationary random fields. Finally, synthetic data in the interwell space are reconstructed from simulated coefficients. The described method was implemented experimentally in software and tested on a case of a real oil field and on a case of a synthetic oil field model. Simulations on the synthetic model were used to test the performance of the method for two different bases in the Fourier expansion (Walsh functions and Haar wavelets). The simulation results were compared to those obtained on the same synthetic model via the classical sequential indicator simulation. It was shown that, for both bases, the new method reproduces statistical parameters of the well data better than sequential indicator simulation.

MSC:

86A32 Geostatistics
62M15 Inference from stochastic processes and spectral analysis

Software:

JBool
Full Text: DOI

References:

[1] Ahmed, N.; Rao, KR, Orthogonal transforms for digital signal processing (1975), New York: Springer, New York · Zbl 0335.94001 · doi:10.1007/978-3-642-45450-9
[2] Armstrong, M.; Galli, A.; Beucher, H.; Loc’h, G.; Renard, D.; Doligez, B.; Eschard, R.; Geffroy, F., Plurigaussian simulations in geosciences (2011), London: Springer, London · doi:10.1007/978-3-642-19607-2
[3] Baykov, VA; Bakirov, NK; Yakovlev, AA, New methods in the theory of geostatistical modeling, Vestnik UGATU, 14, 2-37, 209-215 (2010)
[4] Bonham-Carter, GF, Geographic information systems for geoscientists: modeling with GIS (2014), London: Elsevier, London
[5] Candès, EJ; Donoho, DL, Ridgelets: a key to higher-dimensional intermittency?, Philos Trans R Soc Lond Ser A Math Phys Eng Sci, 357, 1760, 2495-2509 (1999) · Zbl 1082.42503 · doi:10.1098/rsta.1999.0444
[6] Chilès J-P, Delfiner P (2012) Geostatistics: modeling spatial uncertainty. In: Wiley series in probability and statistics, 2nd edn, vol 713. Wiley, Hoboken · Zbl 1256.86007
[7] Crama, Y.; Hammer, PL, Boolean functions: theory, algorithms, and applications (2011), Cambridge: Cambridge University Press, Cambridge · Zbl 1237.06001 · doi:10.1017/CBO9780511852008
[8] Daubechies I (1992) Ten lectures on wavelets. Ser.: CBMS-NSF Regional Conference Series in Applied Mathematics, vol. 61. SIAM, New York · Zbl 0776.42018
[9] Demicco, RV; Klir, GJ, Fuzzy logic in geology (2003), London: Elsevier, London
[10] Dubrule, O., Indicator variogram models: Do we have much choice?, Math Geosci, 49, 4, 441-465 (2017) · Zbl 1369.86017 · doi:10.1007/s11004-017-9678-x
[11] Eidsvik, J.; Mukerji, T.; Switzer, P., Estimation of geological attributes from a well log: an application of hidden Markov chains, Math Geol, 36, 3, 379-397 (2004) · Zbl 1122.86316 · doi:10.1023/B:MATG.0000028443.75501.d9
[12] Fine, NJ, On the Walsh functions, Trans Am Math Soc, 65, 3, 372-414 (1949) · Zbl 0036.03604 · doi:10.1090/S0002-9947-1949-0032833-2
[13] Folland, GB, A course in abstract harmonic analysis (2016), London: CRC Press, London · Zbl 1342.43001 · doi:10.1201/b19172
[14] Grana, D.; Fjeldstad, T.; Omre, H., Bayesian Gaussian mixture linear inversion for geophysical inverse problems, Math Geosci, 49, 4, 493-515 (2017) · Zbl 1369.86012 · doi:10.1007/s11004-016-9671-9
[15] Haar, A., Zur Theorie der orthogonalen Funktionsysteme, Math Ann, 69, 331-371 (1910) · JFM 41.0469.03 · doi:10.1007/BF01456326
[16] Ismagilov N, Lifshits M (2018) Conditioning spectral simulation method by horizontal well data. In: Proceedings of the conference ECMOR XVI, Barcelona. Report P069. doi:10.3997/2214-4609.201802198
[17] Ismagilov N, Popova O, Trushin A (2019) Effectiveness study of the spectral approach to geostatistical simulation. In: Proceedings of the conference SPE annual technical conference and exhibition. Society of Petroleum Engineers, Calgary. doi:10.2118/196106-MS
[18] Ismagilov N, Azangulov I, Borovitskiy V, Lifshits M, Mostowsky P (2020a) Bayesian inference of covariance parameters in spectral approach to geostatistical simulation. Proc Conf ECMOR XVII. doi:10.3997/2214-4609.202035092
[19] Ismagilov NS, Lifshits MA, Yakovlev AA (2020b) A new type of conditioning of stationary fields and its application to the spectral simulation approach in geostatistics. Math Geosci. doi:10.1007/s11004-020-09872-3 · Zbl 1465.86014
[20] Kashin BS, Saakyan AA (1989) Orthogonal series. Ser.: Transl. Math. Monographs, vol. 75. AMS, Providence · Zbl 0668.42011
[21] Lindberg, DV; Grana, D., Petro-elastic log-facies classification using the expectation-maximization algorithm and hidden Markov models, Math Geosci, 47, 6, 719-752 (2020) · Zbl 1323.86049 · doi:10.1007/s11004-015-9604-z
[22] Mallat, S., A wavelet tour of signal processing (1999), London: Elsevier, London · Zbl 0998.94510
[23] Norberg, T.; Rosén, L.; Baran, A.; Baran, S., On modelling discrete geological structures as Markov random fields, Math Geol, 34, 1, 63-77 (2002) · Zbl 1033.86006 · doi:10.1023/A:1014079411253
[24] O’Donnell, R., Analysis of Boolean functions (2014), New York: Cambridge University Press, New York · Zbl 1336.94096 · doi:10.1017/CBO9781139814782
[25] Pardo-Iguzquiza, E.; Chica-Olmo, M., The Fourier integral method: an efficient spectral method for simulation of random fields, Math Geol, 25, 2, 177-217 (1993) · doi:10.1007/BF00893272
[26] Pyrcz MJ, Deutsch CV (2014) Geostatistical reservoir modeling, Oxford
[27] Rasmussen, CE; Williams, CK, Gaussian processes for machine learning (2006), Cambridge: MIT Press, Cambridge · Zbl 1177.68165
[28] Terras, A., Fourier analysis on finite groups and applications (1999), Cambridge: Cambridge University Press, Cambridge · Zbl 0928.43001 · doi:10.1017/CBO9780511626265
[29] Vretblad, A., Fourier analysis and its applications (2003), London: Springer, London · Zbl 1032.42001 · doi:10.1007/b97452
[30] Walnut, DF, An introduction to wavelet analysis (2013), New York: Springer, New York
[31] Walter, GG; Shen, X., Wavelets and other orthogonal systems (2018), London: CRC Press, London · doi:10.1201/9781315273716
[32] Wang, H.; Xiang, S., On the convergence rates of Legendre approximation, Math Comput, 81, 278, 861-877 (2012) · Zbl 1242.41016 · doi:10.1090/S0025-5718-2011-02549-4
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