Methodology for prediction of initial oil-saturated thicknesses for low-permeability reservoirs of AB1 (“Ruff”) group

Background. The high geological heterogeneity of complexly built reservoir rocks with textural clayey rocks necessitates the creation of special geological modeling techniques that allow us to account as accurately as possible for all the risks associated with the development of productive reservoirs of this type. It is especially urgent for 2D constructions, applied for conceptual modeling, because during calculation of oil-saturated thicknesses maps under-saturated areas remain in the volume of reservoir and may contribute to both overestimation of reserves and incorrect assessment of prospective areas during drilling planning.

The article proposes a new approach that allows quantitative assessment of undersaturated thicknesses of low-permeability reservoirs within deposits with “fritillary” texture, an essential part of which is creation of an addressed petrophysical model.

Aim. The purpose of this technique was to develop certain criteria for correct prediction of reservoir development zones with “ripple” texture in well sections and prediction of their saturation character. Materials and methods. As part of the work, core material, GIS and seismic data was used.

Results. An integrated approach has been implemented to select criteria for identifying undersaturated thicknesses and take them into account in a 2D geological model.

Conclusion. Building a petrophysical model of AB₁² formation is a non-trivial task and is associated with a number of difficulties, such as the presence of banded saturation, low permeability properties, high vertical heterogeneity, the effect of electric current shunting by thin interlayers of clays and carbonate-clay cement on the readings of electrometry methods. The main criteria for distinguishing these objects in the well are low values of effective thicknesses, as well as their location above the water-oil contact. Boundary values for these criteria were determined on the basis of neural network analysis, based on the results of capillarimetric studies by semipermeable membrane method and relative phase permeabilities on the core.

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