Lithofacies analysis of Devonian carbonate deposits based on geological and geophysical information analysis by using machine learning methods
E. Kolbikova, С. И. Гусев, O. Malinovskaya, Azat Garaev, R. Valiev
Abstract
Summary The main object of Kharyaginskoye oil field development is a Devonian age carbonate reservoir. The productive zones of the studied object are mainly confined to thin bed low-porosity reservoirs with a complex structure of void space. The high heterogeneity of deposits laterally and the presence of different levels of oil-water contact (OWC) in the marginal isolated zones necessitate a more accurate assessment of the oil-saturated effective thicknesses. The increase in the reliability of the interpretation was achieved by the joint analysis of borehole and seismic studies using Machine Learning methods. The effectiveness of the presented technologies was demonstrated by analyzing the properties of low-permeable carbonate reservoirs, where classical attributes and inversion demonstrate limitations in describing a heterogeneous saturation model. The use of neural network approaches allows to configure complex nonlinear dependencies that are not available to classical methods. The use of a small volume of multi-scale geological and geophysical information using Machine Learning algorithms in the field of field-geophysical and seismic interpretation makes it possible to increase the reliability of interpretation and clarify the location of prospective zones with improved reservoir properties on the studied area, as well as to minimize geological risks during subsequent well placement.