Embedded digital oilfield model

Korovin, Iakov; Boldyreff, Anton S.

doi:10.1117/12.2299293

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Cited by 3 publications

(2 citation statements)

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“…The classical genetic algorithm operates on a binary number system, although recently there are often jobs in which the operators of genetic algorithms perform operations with a set of real numbers. This allows to significantly expand the possibilities of application of the described algorithms [33]- [36].…”

Section: Data Miningmentioning

confidence: 99%

Digital Twin of the Oil Well, Based on Data Mining Technologies

Kosenkov¹,

Turchaninov²,

Korovin³

et al. 2019

dtcse

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Section: Data Miningmentioning

confidence: 99%

Digital Twin of the Oil Well, Based on Data Mining Technologies

Kosenkov¹,

Turchaninov²,

Korovin³

et al. 2019

dtcse

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“…In this connection, modern approaches and methods of organizing information management systems are widely used [10][11][12][13][14][15]. At the same time, the oil industry has a certain specificity [16], therefore it is necessary to ensure:…”

Section: Introductionmentioning

confidence: 99%

Oilfield Equipment Control Distributed Automated System

Kosenkov¹,

Turchaninov²,

Korovin³

et al. 2019

dtcse

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The article proposes a neural network technique for the operational selection of optimal industrial activities. This technique is a sequential process, most of which is a separate algorithm (training of a neural network) or several algorithms (preliminary data processing). The difference between the methods described in this article is that, firstly, the use of hybrid neural network analysis technology, which uses in the simultaneous application, firstly, a multitude of neural trained neural networks with a threshold activation function in order to simultaneously offer several options solutions (this situation is typical only for the oilfield, when several types of problems arise simultaneously at the facility or there are two or more options for recommended measures for clustering, secondly, in the application of an untrained neural network for better selection. The developed methods are focused on processing oilfield data rather than any other data and, in fact, their implementation is dictated by the specifics of the information being analyzed, as well as the need for its speed processing.

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A Failure Prediction Method for Oil Field Complex Technical Objects

Korovin

Kalyaev

et al. 2018

2018 Joint 7th International Conference on Informatics, Electronics &Amp; Vision (ICIEV) and 2018 2nd International Conference

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Embedded digital oilfield model

Cited by 3 publications

References 4 publications

Digital Twin of the Oil Well, Based on Data Mining Technologies

Digital Twin of the Oil Well, Based on Data Mining Technologies

Oilfield Equipment Control Distributed Automated System

A Failure Prediction Method for Oil Field Complex Technical Objects

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