«Oilfield engineering»

Well fracture design using machine learning algorithms based on hydrodynamic modeling. Part 2

UDC: 622.276.66
DOI: -

Authors:

¹ Northern (Arctic) Federal University named after M.V. Lomonosov, Arkhangelsk, Russia
² National University of Oil and Gas "Gubkin University", Moscow, Russia

Keywords: hydraulic fracturing of a formation, modeling, optimization, fracture azimuth, oil production rate, reservoir heterogeneity

Annotation:

The authors of the article present the results of designing hydraulic fracturing in a well using machine learning algorithms based on hydrodynamic modeling. The current part of the article is devoted directly to hydraulic fracturing modeling, taking into account the fracture half-length and proppant quantity predicted in the first part of the article to determine the most optimal fracture azimuth and calculate oil production. The variations of the hydrodynamic model calculations in heterogeneous, anisotropic and low-permeable formations prove the fact that such approach provides an increase of low-content water production time.

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