2023/01/09 by Dmitry Ivlev, Ivlev, Dmitry
Engineering · #Drilling and Well Engineering #FOS: Computer and information sciences #FOS: Physical sciences #Geophysics (physics.geo-ph) #Hydraulic Fracturing and Reservoir Analysis #Machine Learning (cs.LG) #Reservoir Engineering and Simulation Methods
paper · pdf · doi:10.48550/arxiv.2301.03216
openalex publication_date 2023/01/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.