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Scientific and technical journal

«Geology, geophysics and development of oil and gas fields»

ISSN 2413-5011

Determination of ranges of mutual statistical consistency of properties of complex productive oil reservoirs for hydrodynamic modeling

UDC: 550.8.072:622.276.031
DOI: -

Authors:

KOZYREV N.D.1,
KRIVOSHCHEKOV S.N.1,
KOCHNEV A.A.1,
OZHGIBESOV E.S.1

1 Perm National Research Polytechnic University, Perm, Russia

Keywords: geological-hydrodynamic modeling, complex reservoir, mutual statistical consistency, core, petrophysical dependence, permeability, porosity, residual water saturation, residual oil saturation, fractured and cavernous reservoir typification

Annotation:

Geological-hydrodynamic reservoir model today is one of the main tools for forecasting technological indicators of oil and gas fields development. Formation model building and tuning is a rather labor-intensive process, covering competencies in the field of development, geology, petrophysics, reservoir physics, geophysical, well hydrodynamic studies, PVT modeling. The predictive ability of the model depends on the correctness of the tuning methods. This paper considers the problem of correctness of reservoir properties selection when tuning the model according to the actual field information. Often at local selection of reservoir properties the mutual consistency is not observed, i. e. in the model volume there are cells with rather high permeabilities, which is not characteristic for low porosity values, also there is inconsistency of selection of end points of relative phase permeabilities, namely residual water and oil saturation. As part of the work, the core material was statistically analyzed for mutual statistical consistency of permeability, porosity, residual water and oil saturation. The obtained dependencies are a constraint for selection of reservoir properties, which can be further applied in automated multivariate model tuning. Application of the obtained dependencies will reduce the uncertainty of properties and provide physicality of multiple variants of tuned models, thereby increasing the correctness of risk assessment in forecasting the production levels of reservoir fluids.

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