Influence of facies heterogeneity of the U11 formation on the reservoir properties distribution by the example of a part of the Nizhnеvartovsk arch

UDK: 553.98.061.4
DOI: 10.24887/0028-2448-2019-10-24-27
Key words: petrophysical relations, facies model, facies logging, data integration
Authors: O.E. Kurmanov (Gazpromneft NTC LLC, RF, Saint-Petersburg), V.G. Miroshkin (Gazpromneft NTC LLC, RF, Saint-Petersburg), A.S. Khaydarov (Gazpromneft NTC LLC, RF, Saint-Petersburg), A.A. Shtyrlyaeva (Gazpromneft NTC LLC, RF, Saint-Petersburg), L.A. Guryevskikh (Gazpromneft NTC LLC, RF, Saint-Petersburg), I.I. Zayrullin (Gazpromneft NTC LLC, RF, Saint-Petersburg)

The article describes data analysis and integration method for geological and petrophysical modeling. The input data includes the results of the interpretation of facies from the core and spontaneous polarization diagrams, analysis of petrophysical relations and processing of 3D seismic for the U11 formation in one of the fields of the Nizhnevartovsk arch. The analysis of these data showed a significant influence of the formation conditions on the distribution characteristics of petrophysical parameters in the reservoir. It became possible to see that the sandstones composing the meandering distribution channels of the delta plain and the sandstones of marine bars significantly differ in the nature of the core-core relations (porosity / permeability). The meandering distribution channels of the delta plain are clearly visible on the maps of the spectral decomposition and have a special form of spontaneous polarization diagrams in the wells. These sand bodies formed at the regression stage under the conditions of fluvial processes are more highly permeable reservoirs than the sand bodies of sea bars. The sufficient convergence of these wells and seismic data allows us to create different approaches for calculating permeability for sandstones of distribution channels and sandstones of sea bars. Facial logging was introduced into the petrophysical model to optimize calculations using two separate approaches. This allows you to automatically select an algorithm depending on the facies. A set of facies logs, which is confirmed by core analysis, spontaneous polarization diagrams, and seismic data, can serve as an example of an ideal training set for detecting facies by logging using machine learning methods.


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