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This work is devoted to an assessment of the application of machine learning
algorithms in the prediction of a fracture's aspect ratio caused by the hydraulic fracturing. By
the aspect ratio in this work is assumed the ratio of the larger half-axis of the fracture to the
smaller one. The study shows the prospects of applying data-driven surrogate model methods
(deep neural networks learning from data simulated by means of traditional solvers) to
particle dynamics modelling of hydraulic fracturing. The solution obtained allows to predict
the aspect ratio value quickly, and thus, to evaluate the volume of the hydraulic fracturing
fluid injection necessary to achieve the required fracture length.
Keywords: neural networks, surrogate model, hydraulic fracturing, crack |
full paper (pdf, 944 Kb)