Field Evaluation of MOV Adjustable Steam Chokes

Latif, Faisal; Griston-Castrup, Suzanne; Kalbani, Ali Al

doi:10.2118/153726-ms

Cited by 5 publications

(1 citation statement)

References 3 publications

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“…Analytical and empirical approaches are considered the main two approaches to predict the multiphase fluid flow through wellhead chokes [8][9][10][11][12][13]. There are numerous analytical and empirical correlations for the prediction of choke performance relationships [14][15][16][17][18].…”

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confidence: 99%

An Investigation into Sub-Critical Choke Flow Performance in High Rate Gas Condensate Wells

et al. 2019

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There have been some correlations in the literature to predict the gas and liquid flow rate through wellhead chokes under subcritical flow conditions. The majority of these empirical correlations have been developed based on limited production data sets that were collected from a small number of fields. Therefore, these correlations are valid within the parameter variation ranges of those fields. If such correlations are used elsewhere for the prediction of the subcritical choke flow performance of the other fields, significant errors will occur. Additionally, there are only a few empirical correlations for sub-critical choke flow performance in high rate gas condensate wells. These led the authors to develop a new empirical correlation based on a wider production data set from different gas condensate fields in the world; 234 production data points were collected from a large number of production wells in twenty different gas condensate fields with diverse reservoir conditions and different production histories. A non-linear regression analysis method was applied to their production. The new correlation was validated with a new set of data points from some other production wells to confirm the accuracy of the established correlation. The results show that the new correlation had minimal errors and predicted the gas flow rate more accurately than the other three existing models over a wider range of parameter variation ranges.

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confidence: 99%

An Investigation into Sub-Critical Choke Flow Performance in High Rate Gas Condensate Wells

et al. 2019

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Prediction performance advantages of deep machine learning algorithms for two-phase flow rates through wellhead chokes

Barjouei

Ghorbani

Mohamadian

et al. 2021

J Petrol Explor Prod Technol

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Two-phase flow rate estimation of liquid and gas flow through wellhead chokes is essential for determining and monitoring production performance from oil and gas reservoirs at specific well locations. Liquid flow rate (QL) tends to be nonlinearly related to these influencing variables, making empirical correlations unreliable for predictions applied to different reservoir conditions and favoring machine learning (ML) algorithms for that purpose. Recent advances in deep learning (DL) algorithms make them useful for predicting wellhead choke flow rates for large field datasets and suitable for wider application once trained. DL has not previously been applied to predict QL from a large oil field. In this study, 7245 multi-well data records from Sorush oil field are used to compare the QL prediction performance of traditional empirical, ML and DL algorithms based on four influencing variables: choke size (D64), wellhead pressure (Pwh), oil specific gravity (γo) and gas–liquid ratio (GLR). The prevailing flow regime for the wells evaluated is critical flow. The DL algorithm substantially outperforms the other algorithms considered in terms of QL prediction accuracy. The DL algorithm predicts QL for the testing subset with a root-mean-squared error (RMSE) of 196 STB/day and coefficient of determination (R2) of 0.9969 for Sorush dataset. The QL prediction accuracy of the models evaluated for this dataset can be arranged in the descending order: DL > DT > RF > ANN > SVR > Pilehvari > Baxendell > Ros > Glbert > Achong. Analysis reveals that input variable GLR has the greatest, whereas input variable D64 has the least relative influence on dependent variable QL.

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Proposing a New Model for Estimation of Oil Rate Passing Through Wellhead Chokes in an Iranian Heavy Oil Field

Hazbeh

Ghorbani

Molaei

et al. 2022

2022 IEEE 22nd International Symposium on Computational Intelligence and Informatics and 8th IEEE International Conference on R

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Field Evaluation of MOV Adjustable Steam Chokes

Cited by 5 publications

References 3 publications

An Investigation into Sub-Critical Choke Flow Performance in High Rate Gas Condensate Wells

An Investigation into Sub-Critical Choke Flow Performance in High Rate Gas Condensate Wells

Prediction performance advantages of deep machine learning algorithms for two-phase flow rates through wellhead chokes

Proposing a New Model for Estimation of Oil Rate Passing Through Wellhead Chokes in an Iranian Heavy Oil Field

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