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Proceedings Paper

Embedded digital oilfield model
Author(s): Iakov S. Korovin; Anton S. Boldyreff
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Paper Abstract

In modern hard conditions for the whole worldwide oil production industry the problem of increasing volumes of produced oil has recently become vital. This problem concerns the existing oilfields cause due to low crude oil prices the possibilities to drill new ones has almost disappeared. In this paper, we describe a novel approach of oil production enhancement, based on online procedures of all oil field data processing. The essence is that we have developed a dynamic oilfield model that allows to simultaneously handle the information, stored in tNavigator, Schlumberger ECLIPSE 100/300 and other ‘popular’ formats in parallel. The model is developed on the basis of convolutional neural networks. An example of successful industrial experiment is depicted.

Paper Details

Date Published: 5 October 2017
PDF: 6 pages
Proc. SPIE 10430, High-Performance Computing in Geoscience and Remote Sensing VII, 1043007 (5 October 2017); doi: 10.1117/12.2299293
Show Author Affiliations
Iakov S. Korovin, Southern Federal Univ. (Russian Federation)
Anton S. Boldyreff, Southern Federal Univ. (Russian Federation)


Published in SPIE Proceedings Vol. 10430:
High-Performance Computing in Geoscience and Remote Sensing VII
Bormin Huang; Sebastián López; Zhensen Wu, Editor(s)

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