Reservoir Prediction by Machine Learning Methods on The Well Data and Seismic Attributes for Complex Coastal Conditions
This work addresses reservoir prediction for hydrocarbon exploration in challenging coastal environments, representing an incremental advancement in applying machine learning to geological data.
The study tackled reservoir prediction in complex coastal conditions by using machine learning on well data and seismic attributes, achieving an average F1 score of 0.798 for reservoir class and improving prediction quality by a factor of 1.56 with data augmentation methods.
The aim of this work was to predict the probability of the spread of rock formations with hydrocarbon-collecting properties in the studied coastal area using a stack of machine learning algorithms and data augmentation and modification methods. This research develops the direction of machine learning where training is conducted on well data and spatial attributes. Methods for overcoming the limitations of this direction are shown, two methods - augmentation and modification of the well data sample: Spindle and Revers-Calibration. Considering the difficulties for seismic data interpretation in coastal area conditions, the proposed approach is a tool which is able to work with the whole totality of geological and geophysical data, extract the knowledge from 159-dimensional space spatial attributes and make facies spreading prediction with acceptable quality - F1 measure for reservoir class 0.798 on average for evaluation of "drilling" results of different geological conditions. It was shown that consistent application of the proposed augmentation methods in the implemented technology stack improves the quality of reservoir prediction by a factor of 1.56 relative to the original dataset.