Home – Home – Jornals – Russian Geology and Geophysics 2018 number Неопубликованное

There is the Tira Horizon, represented by the Chistyakov, Omoro, and Oskoba Formations in different facies zones, widespread in the central and southwestern parts of the Siberian Platform. Deposits of the Tira Horizon in most sections of the Southwestern Siberian Platform are differ significantly in lithology from the underlying Nepa and overlying Danilov Horizons. In this paper considers the most complete section in the eastern part of the Angara Folded Zone, which serves as a key section. There are three main transgressive-regressive cycles, separated by sequence boundaries, are noted in the Tira interval. Three members correspond to these cycles. The first member records an abrupt change in facies from coastal to deep-sea, containing organic-walled microfossils of the first assemblage with Vanavarataenia insolita. The second member, formed in a coastal marine environment, exhibits an increase in carbonate and sulfate content, the appearance of chert lenses and nodules with microfossils, and the second assemblage with Tinnajaphyton amplum (lower submember). The third member is characterized by upper littoral, sabkha, and continental-deltaic deposits. The identified associations of microfossils, along with the features of the lithological composition and isotopic characteristics, allow them to be used as benchmarks in regional constructions on the Siberian platform, and possibly also beyond its borders. A carbon isotope anomaly of up to -11.3‰ (EPDB) was detected in dolomites of the Oskoba Formation at the Kamo Arch of the Baikit Anteclise. This may indicate a restructuring of the ecosystem in the paleobasin, accompanied by the appearance of certain microbiota groups and points to global climate change.

The problem of forecasting the filtration-capacitance properties (FCP) of target horizons (porosity, effective thickness, oil saturation) using methods capable of learning on large volumes of heterogeneous data is considered. 3D seismic data, both full-stack (seismic attributes extracted from a seismic cube) and partial-stacks (synchronous inversion), are used, limited to the target horizons interval, obtained at one of the offshore fields in the north of Sakhalin. The large data volume and the strong interdependence of the extracted features required dimensionality reduction of the seismic parameters. The novelty of the developed approach lies in the application of factor analysis prior to performing FCP forecasting. As a result, 92% of the data variations associated with reservoir properties were represented by five main factors. Subsequent application of neural networks to the forecasting problem demonstrated that the identified factors are significant predictors of porosity, oil saturation, and effective thickness. Cross-validation identified zones with low accuracy in FCP estimation – resulting from the strong influence of gas on seismic data – as well as zones with high accuracy in FCP estimation, which were subsequently used to determine optimal weights and the final forecasts. Based on the results of FCP forecasting using factor analysis and neural networks, correlation coefficients at test well locations ranged from 0.92 to 0.93, and forecast accuracy – verified following the drilling of two production wells – fell within the 85–93% range, thereby demonstrating the effectiveness of the proposed technology.