Application of proxy models for oil reservoirs performance prediction


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Abstract

The evolution of oil and gas reservoirs development parameters prediction has received new opportunities due to the development of digital technologies and computing power. The idea and first experiments in the use of artificial neural networks for various kinds of applied problems as classification of workover actions, automatic interpretation of geophysical well logging and core analyses results can be considered as an important milestone for the oil industry. The application of machine learning for reservoir development parameters prediction is currently a pressing and unresolved issue. Disputes arising in attempts to industrialize this technology are associated with so-called “black box” – a situation when the constructed model cannot explain physical laws and it is almost impossible to track intermediate results in the process of calculating non-linear dependencies. Given the problems described above, the current best practice is to combine machine learning models and physically meaningful analytical models as described in this paper.

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Zh. T. Zhetruov

KMG Engineering LLP

Email: zh.zhetruov@niikmg.kz
руководитель службы по аналитике Nur-Sultan

K. N. Shayakhmet

KMG Engineering LLP

Email: k.shayakhmet@niikmg.kz
ведущий инженер службы по аналитике Nur-Sultan

Kuat K. Karsybayev

KMG Engineering LLP

Email: k.karsybayev@niikmg.kz
эксперт службы по аналитике Nur-Sultan

Azamat M. Bulakbay

KMG Engineering LLP

Email: a.bulakbay@niikmg.kz
ведущий инженер службы по аналитике Nur-Sultan

Sara B. Kulzhanova

KMG Engineering LLP

Email: s.kulzhanova@niikmg.kz
старший инженер службы по аналитике Nur-Sultan

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Copyright (c) 2022 Zhetruov Z.T., Shayakhmet K.N., Karsybayev K.K., Bulakbay A.M., Kulzhanova S.B.

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