The prediction of wellhead pressure for multiphase flow of vertical wells using artificial neural networks

Gomaa, Ibrahim; Gowida, Ahmed; Elkatatny, Salaheldin; Abdulraheem, Abdulazeez

doi:10.1007/s12517-021-07099-y

Cited by 5 publications

(2 citation statements)

References 29 publications

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“…Today, new machine learning techniques are efficient tools for optimization and sophisticated computing that reduce operating costs and improve system performance. Extensive research has been conducted in recent years on the application of intelligent machine learning methods in various sectors of the upstream oil and gas industry, such as desalting system analysis [37], hydrocarbon phase behavior prediction [38][39][40][41][42], determination of oil and gas flow through orifice [43][44][45][46] and determination of flow rate through wellhead choke [18,[47][48][49][50][51][52][53]. Predicting multiphase flow rate from wellhead chokes is the subject of other studies on machine learning application in flow measurement concepts.…”

Section: Introductionmentioning

confidence: 99%

Robust hybrid machine learning algorithms for gas flow rates prediction through wellhead chokes in gas condensate fields

Abad

Ghorbani

Mohamadian

et al. 2022

Fuel

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Section: Introductionmentioning

confidence: 99%

Robust hybrid machine learning algorithms for gas flow rates prediction through wellhead chokes in gas condensate fields

Abad

Ghorbani

Mohamadian

et al. 2022

Fuel

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“…Furthermore, this technique has been implemented in engineering for wind energy prediction [29,30]. Additionally, a fundamental aspect of using neural networks is the allocation of 80% of the database for model training, 10% for testing, and 10% for validation [31].…”

Section: Introductionmentioning

confidence: 99%

Two-Phase Flow Pattern Identification in Vertical Pipes Using Transformer Neural Networks

Ruiz-Díaz,

Perilla-Plata,

González-Estrada

2024

Inventions

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The oil and gas industry consistently embraces innovative technologies due to the significant expenses associated with hydrocarbon transportation, pipeline corrosion issues, and the necessity to gain a deeper understanding of two-phase flow characteristics. These solutions involve the implementation of predictive models utilizing neural networks. In this research paper, a comprehensive database comprising 4864 data points, encompassing information pertaining to oil–water two-phase flow properties within vertical pipelines, was meticulously curated. Subsequently, an encoder-only type transformer neural network (TNN) was employed to identify two-phase flow patterns. Various configurations for the TNN model were proposed, involving parameter adjustments such as the number of attention heads, activation function, dropout rate, and learning rate, with the aim of selecting the configuration yielding optimal outcomes. Following the training of the network, predictions were generated using a reserved dataset, thus facilitating the creation of flow maps depicting the patterns anticipated by the model. The developed TNN model successfully predicted 9 out of the 10 flow patterns present in the database, achieving a peak accuracy of 53.07%. Furthermore, the various predicted flow patterns exhibited an average precision of 63.21% and an average accuracy of 86.51%.

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Comprehensive investigation and evaluation for the different multiphase flow correlations in gas wells

El-Moniem

Sudan

El-Banbi

2022

Egyptian Journal of Petroleum

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The prediction of wellhead pressure for multiphase flow of vertical wells using artificial neural networks

Cited by 5 publications

References 29 publications

Robust hybrid machine learning algorithms for gas flow rates prediction through wellhead chokes in gas condensate fields

Robust hybrid machine learning algorithms for gas flow rates prediction through wellhead chokes in gas condensate fields

Two-Phase Flow Pattern Identification in Vertical Pipes Using Transformer Neural Networks

Comprehensive investigation and evaluation for the different multiphase flow correlations in gas wells

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