Embedded digital oilfield model

Iakov S. Korovin; Anton S. Boldyreff

doi:10.1117/12.2299293

5 October 2017 Embedded digital oilfield model

Iakov S. Korovin, Anton S. Boldyreff

Proceedings Volume 10430, High-Performance Computing in Geoscience and Remote Sensing VII; 1043007 (2017) https://doi.org/10.1117/12.2299293
Event: SPIE Remote Sensing, 2017, Warsaw, Poland

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.

Citation Download Citation

Iakov S. Korovin and Anton S. Boldyreff "Embedded digital oilfield model", Proc. SPIE 10430, High-Performance Computing in Geoscience and Remote Sensing VII, 1043007 (5 October 2017); https://doi.org/10.1117/12.2299293

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