Lithofacial analysis and possibilities for prediction of properties on geophysical research and seismic exploration data by methods of machine learning

The success of a development strategy for any field depends on the degree of knowledge of the geological structure of its main reservoirs. As the area is drilled out, the concept of the structure of the hydrocarbon accumulation is refined, but in the case of a complex structure of the void space of the reservoirs and the lithological heterogeneity of the section over the area, geological uncertainties and risks during the subsequent placement of wells remain high. For these reasons, one of the main problems in hydrocarbon production is predicting rock types and the distribution of fluids throughout the reservoir away from wells, since the determination of rock properties is a major source of uncertainty in reservoir modeling studies [1, 2]. The proposed project will demonstrate algorithms based on machine learning methods that allow predicting the distribution of lithology and the uncertainty of lithofacies variability in the section.

classification, machine learning methods, lithotyping, facies forecast, cluster model, property prediction, specification of reservoir properties