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Boolean Spectral Analysis in Categorical Reservoir Modeling. / Ismagilov, Niyaz; Borovitskiy, Viacheslav; Lifshits, Mikhail; Platonova, Mariia.

In: Mathematical Geosciences, Vol. 53, No. 3, 04.2021, p. 305-324.

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Harvard

Ismagilov, N, Borovitskiy, V, Lifshits, M & Platonova, M 2021, 'Boolean Spectral Analysis in Categorical Reservoir Modeling', Mathematical Geosciences, vol. 53, no. 3, pp. 305-324. https://doi.org/10.1007/s11004-021-09919-z

APA

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Author

Ismagilov, Niyaz ; Borovitskiy, Viacheslav ; Lifshits, Mikhail ; Platonova, Mariia. / Boolean Spectral Analysis in Categorical Reservoir Modeling. In: Mathematical Geosciences. 2021 ; Vol. 53, No. 3. pp. 305-324.

BibTeX

@article{72ebe8b4629044078af866ab755a217f,
title = "Boolean Spectral Analysis in Categorical Reservoir Modeling",
abstract = "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.",
keywords = "Boolean functions, Categorical simulation, Reservoir modeling, Spectral analysis",
author = "Niyaz Ismagilov and Viacheslav Borovitskiy and Mikhail Lifshits and Mariia Platonova",
note = "Funding Information: Research was partially supported by Russian Science Foundation Grant 19-71-30002. Publisher Copyright: {\textcopyright} 2021, International Association for Mathematical Geosciences. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.",
year = "2021",
month = apr,
doi = "10.1007/s11004-021-09919-z",
language = "English",
volume = "53",
pages = "305--324",
journal = "Mathematical Geosciences",
issn = "1874-8961",
publisher = "Springer Nature",
number = "3",

}

RIS

TY - JOUR

T1 - Boolean Spectral Analysis in Categorical Reservoir Modeling

AU - Ismagilov, Niyaz

AU - Borovitskiy, Viacheslav

AU - Lifshits, Mikhail

AU - Platonova, Mariia

N1 - Funding Information: Research was partially supported by Russian Science Foundation Grant 19-71-30002. Publisher Copyright: © 2021, International Association for Mathematical Geosciences. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2021/4

Y1 - 2021/4

N2 - 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.

AB - 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.

KW - Boolean functions

KW - Categorical simulation

KW - Reservoir modeling

KW - Spectral analysis

UR - http://www.scopus.com/inward/record.url?scp=85100073867&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/1e967251-6da3-31c8-b5d0-30620816e51f/

U2 - 10.1007/s11004-021-09919-z

DO - 10.1007/s11004-021-09919-z

M3 - Article

AN - SCOPUS:85100073867

VL - 53

SP - 305

EP - 324

JO - Mathematical Geosciences

JF - Mathematical Geosciences

SN - 1874-8961

IS - 3

ER -

ID: 75053013