Modern Methods for the Selection of Effective Demulgators for Oil Dehydration in Eastern Siberia

Shakirova, E. V.; Semykin, M. V.; Aleksandrov, A. A.; Bryzhevatykh, N.V.

doi:10.17122/ntj-oil-2021-3-36-44

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…В зависимости от типа используемых скважинных датчиков можно отсле-дить следующие параметры: давление и температуру на приеме насоса, температуру обмотки погружного электродвигателя, температуру моторного масла и величину осевых вибраций [5]. Помимо этого, данная система в режиме реального времени позволит проводить мониторинг следующих параметров на устье скважины: поверхностный расход, давление в трубопроводе, давление в обсадной колонне, частоту, удельное сопротивление кабеля и фазное напряжение [6,7].…”

Section: Introductionunclassified

Submersible pumpset failure prediction using artificial intelligence methods

Shakirova

Semykin

2023

jour

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It is well-known that large amounts of data are collected and processed during the operation of electric submersible pumps. To optimize the work of mining control center operators, it is recommended to use an automated emergency prevention system. In this way, operators will be able to receive timely information about possible equipment failures, which in its turn will increase the service life of the equipment and reduce operating costs for repairs. The purpose of the present research is to develop a model predicting submersible pumping equipment failures using the method of artificial intelligence. To identify the most accurate model, the paper compares the following forecasting methods: the nearest neighbour method and the linear classifier building method. The presented correlation was created on the basis of 30 parameters obtained from 272 wells of the Eastern Siberia field. Being used, it enabled error-free prediction of failures and complications in pumping equipment operation depending on the gas factor and frequency. Thus, the developed model can be used by oil and gas enterprises to predict failures and accidents in the operation of submersible pumping equipment. The conducted study shows that the prediction accuracy of the required parameter in the developed artificial intelligence model exceeds the results of conventional statistical methods. The model also can be useful for future optimization of processes when field planning and developing. Artificial intelligence is the best prediction method of submersible pumping equipment failures, due to its high speed and accuracy, cognitive technologies are widely used in big data processing.

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Section: Introductionunclassified

Submersible pumpset failure prediction using artificial intelligence methods

Shakirova

Semykin

2023

jour

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Development of a new empirical correlation for predicting formation volume factor of reservoir oil using artificial intelligence

Shakirova

Aleksandrov

Semykin

2021

jour

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It is known that oil in reservoir conditions is characterized by the content of a certain amount of dissolved gas. As reservoir pressure decreases this gas is released from oil significantly changing its physical properties, primarily its density and viscosity. In addition, the oil volume also reduces, sometimes by 50–60 %. In this regard, when calculating reserves, it is necessary to justify the reduction amount of the reservoir oil volume when oil is extracted to the surface. For this purpose, the concept of formation volume factor of reservoir oil has been introduced. The formation volume factor of oil is considered one of the main characterizing parameters of crude oil. It is also required for modeling and predicting the characteristics of an oil reservoir. The purpose of the present work is to develop a new empirical correlation for predicting the formation volume factor of reservoir oil using artificial intelligence methods based on MATLAB software, such as: an artificial neural network, an adaptive neuro-fuzzy inference system, and a support vector machine. The article presents a new empirical correlation extracted from the artificial neural network based on 503 experimental data points for oils from the Eastern Siberia field, which was able to predict the formation volume factor of oil with the correlation coefficient of 0.969 and average absolute error of less than 1 %. The conducted study shows that the prediction accuracy of the desired parameter in the developed artificial intelligence model exceeds the accuracy of study results obtained by conventional statistical methods. Moreover, the model can be useful in the prospect of process optimization in field planning and development.

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Modern Methods for the Selection of Effective Demulgators for Oil Dehydration in Eastern Siberia

Cited by 2 publications

References 7 publications

Submersible pumpset failure prediction using artificial intelligence methods

Submersible pumpset failure prediction using artificial intelligence methods

Development of a new empirical correlation for predicting formation volume factor of reservoir oil using artificial intelligence

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