A New Correlation for Calculating Wellhead Production Considering Influences of Temperature, GOR, and Water-Cut for Artificially Lifted Wells

Ghareeb, Mohamed; Shedid, S.A.

doi:10.2523/iptc-11101-ms

Cited by 13 publications

(1 citation statement)

References 2 publications

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“…The model estimates both critical and subcritical condition flow behaviors, their work was an extension of the work of Ros (1961b). Ghareeb and Shedid (2007) came up with a correlation for artificially lifted wells to estimate wellhead production rate. Osman and Dokla (1998) developed four empirical correlations describing gas condensate behavior through chokes.…”

Section: Literature Reviewmentioning

confidence: 99%

Application of Neural Network for Two-Phase Flow through Chokes

Al-Khalifa

Al-Marhoun

2013

All Days

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‫التخصص‬In recent years, neural network have gained popularity in the oil industry. Many authors discussed the applications of neural network in petroleum engineering applications; never the less, none of the researcher studied the application of neural networks for two phase flow through chokes. This study shows the utilization of Artificial Neural Network (ANN) as a simple engineering tool for estimating flow rate and the choke size. Two models were developed based on 4031 data points and a ratio of 80%, 10%, 10% for training, validation and testing respectively.In this study the existing choke correlations available in the literature were reviewed, evaluated and compared with the new ANN models using statistical and graphical analysis technique. The new models were found to outperform all the existing correlations and have provided the lowest error with average absolute percent error of 3.7% for the choke size prediction and 6.7% for the flow rate estimation.

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Section: Literature Reviewmentioning

confidence: 99%

Application of Neural Network for Two-Phase Flow through Chokes

Al-Khalifa

Al-Marhoun

2013

All Days

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Application of Artificial Intelligence to Estimate Oil Flow Rate in Gas-Lift Wells

Khan¹,

Tariq

Abdulraheem

2020

Nat Resour Res

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Utilization of machine learning for the estimation of production rates in wells operated by electrical submersible pumps

Agwu,

Alkouh,

Alatefi

et al. 2024

J Petrol Explor Prod Technol

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In this study, a neural network model is developed for the prediction of oil flow rates in wells lifted by electrical submersible pumps (ESPs). Three attributes of the model in this work make this study unique. First, the knowledge on the computational cost of models has been presented, a rarity in most neural network models on this subject; second, the models have been explicitly presented, a feature uncommon in published ANN predictive modelling studies; and third, it includes a sensitivity analysis of input variables. The dataset utilized for the model development comprises 275 data points collected from ESP-lifted wells in the Middle East. Statistical evaluation of the model’s performance using the metrics such as mean square error, root mean square error and coefficient of determination demonstrates high predictive accuracy with respective values of 0.0000201861, 0.00449 and 0.999. In order to ascertain the parametric importance of the inputs, Garson’s algorithm was utilized. In this regard, choke size and upstream pressure had the highest influence (19% and 16%, respectively), while casing head pressure had the least effect (4.8%) on oil flow rate. In terms of memory requirements and processing speed for software applications, the model had a memory footprint of 888 bytes and required 191 multiply and accumulate operations to give an output. By utilizing the proposed models, the time-consuming separator tests measurements of flow rate would no longer be necessary and real-time results could be provided in the field. This work would be useful to production engineers who seek a quick and accurate means of estimating oil flow rate from ESP wells in real time.

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A New Correlation for Calculating Wellhead Production Considering Influences of Temperature, GOR, and Water-Cut for Artificially Lifted Wells

Abstract: TX 75083-3836, U.S.A., fax 01-972-952-9435. Q = 0.0002 T3.0955 R2 = 0.8152 Production rate, bpd Production rate, bpd Production rate, bpd Production rate, bpd

Cited by 13 publications

References 2 publications

Application of Neural Network for Two-Phase Flow through Chokes

Application of Neural Network for Two-Phase Flow through Chokes

Application of Artificial Intelligence to Estimate Oil Flow Rate in Gas-Lift Wells

Utilization of machine learning for the estimation of production rates in wells operated by electrical submersible pumps

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