Mathematical Modeling of Geological Processes (Dynamic Geological Systems)

Buryakovsky, Leonid A.; Chilingar, George V.; Aminzadeh, Fred

doi:10.1016/b978-088415342-9/50015-1

Cited by 2 publications

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“…Numerous additional petrophysical relationships were presented by Buryakovsky (1977Buryakovsky ( , 1985 and Buryakovsky et al (1982Buryakovsky et al ( , 1990). …”

Section: Tablementioning

confidence: 88%

“…Actual relationships attributed to oil and gas fields explored and produced in the South Caspian Basin are presented in Tables 2 to 5: Table 2-correlation between porosity and clay content, Table 3-correlation between porosity and carbonate content, Table 4-correlation between permeability and clay content, and Table 5-correlation between permeability and carbonate content (Buryakovsky, 1985;Buryakovsky et al, 1990Buryakovsky et al, , 2001.…”

Section: Modeling Of Core-to-core Relationshipsmentioning

confidence: 99%

“…In the actual cases, one may deal with a multitude of petrophysical parameters which may be processed only by statistical procedures. One of the most effective methodologies of the log-to-core simulation is statistical analysis of output of the correlation of variables (CorVar) and combination of variables (ComVar) procedures described in details by Buryakovsky et al (1990Buryakovsky et al ( , 2001.…”

Section: Modeling Of Log-to-core Relationshipsmentioning

confidence: 99%

“…Methodology of computation of relationships among petrophysical and other geological parameters is described in many publications (e.g., Miller and Kahn, 1962;Krumbein and Graybill, 1965;Griffiths, 1967;Harbaugh and Bonham-Carter, 1970;Khanin, 1976;Kotyakhov, 1977;Buryakovsky, 1985Buryakovsky, , 1992Buryakovsky et al, 1981Buryakovsky et al, , 1982Buryakovsky et al, , 1990Buryakovsky et al, , 2001. ries section differs from that of the red-bed series by more sorted and coarse sediments.…”

mentioning

confidence: 96%

“…Classification of the Middle Pliocene rocks within the South Caspian Basin is summarized in Table 1. Detailed information on geologic setting, stratigraphy and lithology of the area under study is provided in the following publications: Buryakovsky (1974Buryakovsky ( , 1985Buryakovsky ( , 1993 and Buryakovsky et al (1982Buryakovsky et al ( , 1990Buryakovsky et al ( , 2001. …”

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confidence: 99%

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Petrophysical Simulation in Petroleum Geology and Reservoir Engineering

Buryakovsky¹,

Chilingar²

2005

Energy Sources

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The simulation of multivariate petrophysical relationships between core and well-log derived parameters on the example of the South Caspian Basin is discussed. For developing the petrophysical relationships, a number of deterministic and stochastic calculating procedures are used by the authors. These relationships are widely used in petroleum geology and reservoir engineering for hydrocarbon reserves estimation, reservoir description and simulation, field development planning, and reservoir production management.Petrophysical relationships or petrophysical models are used to describe actual correlation existing among various core-derived parameters (core-to-core correlation) and to improve reliability and accuracy of wireline log analysis and interpretation on the basis of correlation between log and core petrophysical parameters (log-to-core correlation). Petrophysical relationships are widely used in petroleum geology and reservoir engineering for hydrocarbon reserves estimation, reservoir description and simulation, field development planning, and reservoir production management.Because of the multitude of variables involved in petrophysical relations, the most effective methodology of petrophysical simulation is statistical analysis. Very important is the description of regional distribution of such pertophysical properties as porosity and permeability, and their changes with depth of burial. The standard practice used to study regional distribution of any core-derived parameter is a construction of maps with contour lines or zonal maps, which are defined as two-dimensional images or models of a given reservoir feature. To study the distribution of petrophysical parameters over the entire field, the 3D statistical analysis is implemented.Methodology of computation of relationships among petrophysical and other geological parameters is described in many publications (e.

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“…Numerous additional petrophysical relationships were presented by Buryakovsky (1977Buryakovsky ( , 1985 and Buryakovsky et al (1982Buryakovsky et al ( , 1990). …”

Section: Tablementioning

confidence: 88%

Section: Modeling Of Core-to-core Relationshipsmentioning

confidence: 99%

Section: Modeling Of Log-to-core Relationshipsmentioning

confidence: 99%

mentioning

confidence: 96%

mentioning

confidence: 99%

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Petrophysical Simulation in Petroleum Geology and Reservoir Engineering

Buryakovsky¹,

Chilingar²

2005

Energy Sources

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Research by Hydrophysics Laboratory of NWPI KarRC RAS in 1991–2022

Тержевик,

Пальшин,

Ефремова

et al. 2023

Proceedings of KarRC of RAS

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Information is presented on the directions and results of research at the Hydrophysics Laboratory of the Northern Water Problems Institute of the Karelian Research Center of the Russian Academy of Sciences in 1991-2022. Laboratory staff study hydrophysical processes and phenomena in various lakes of Karelia, in the largest lakes of Eurasia -Onego and Ladoga, in Lake Baikal, in the White Sea, as well as in small lakes of the Arctic zone of Russia. Brief information is provided on the applied developments and basic scientific results produced while implementing state-ordered assignments, international and domestic research projects, including those carried out jointly with Russian and foreign scientific and educational organizations. The main results include: identification of patterns in the formation of the thermal, hydrodynamic, ice, radiation and oxygen regimes of lakes through the annual cycle (with more focus on the ice-covered period); development of a thermal model of Lake Onego; development and implementation of FLake lake model in collaboration with colleagues from the Institute of Limnology of the Russian Academy of Sciences, German Weather Service, and the Institute for Freshwater Ecology and Inland Fisheries (IGB, Germany); development of a 3D model of Lake Vendyurskoe; investigation of energy and greenhouse gas transport in high-latitude lake ecosystems in collaboration with colleagues from the University of Helsinki; assessment of the adaptive properties of Arctic aquatic ecosystems (lakes of the Yamal Peninsula, deltas of the Lena River, Kola Peninsula) in a changing climate in collaboration with colleagues from the St. Petersburg State University; study of turbulence parameters in ice-covered lakes during the period of spring underice convection, numerical modeling (Implicit LES) of radiation-generated convection in collaboration with colleagues from the Physical-Mechanical Institute of the Peter the Great St. Petersburg Polytechnic University; study of hydrophysical processes and phenomena in bays and inlets of the White Sea in collaboration with colleagues from the Water Problems Institute RAS (Moscow) and the Russian State Humanitarian University (St. Petersburg); study of turbulent transport, which determines the conditions for ice build-up and melting in the subglacial boundary layer of Lake Baikal in collaboration with colleagues from the

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Mathematical Modeling of Geological Processes (Dynamic Geological Systems)

Cited by 2 publications

References 0 publications

Petrophysical Simulation in Petroleum Geology and Reservoir Engineering

Petrophysical Simulation in Petroleum Geology and Reservoir Engineering

Research by Hydrophysics Laboratory of NWPI KarRC RAS in 1991–2022

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