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The aim of this work was to predict the probability of the spread of rockformations with hydrocarbon-collecting properties in the studied coastal areausing a stack of machine learning algorithms and data augmentation andmodification methods. This research develops the direction of machine learningwhere training is conducted on well data and spatial attributes. Methods forovercoming the limitations of this direction are shown, two methods -augmentation and modification of the well data sample: Spindle andRevers-Calibration. Considering the difficulties for seismic datainterpretation in coastal area conditions, the proposed approach is a toolwhich is able to work with the whole totality of geological and geophysicaldata, extract the knowledge from 159-dimensional space spatial attributes andmake facies spreading prediction with acceptable quality - F1 measure forreservoir class 0.798 on average for evaluation of "drilling" results ofdifferent geological conditions. It was shown that consistent application ofthe proposed augmentation methods in the implemented technology stack improvesthe quality of reservoir prediction by a factor of 1.56 relative to theoriginal dataset.

Reservoir Prediction by Machine Learning Methods on The Well Data and Seismic Attributes for Complex Coastal Conditions