Experimental analysis of centrifugal downhole separators in boosting artificial lift performance

Olubode, Michael; Iradukunda, P.; Karami, Hamidreza; Podio, Tony; McCoy, J.N.

doi:10.1016/j.jngse.2022.104408

Cited by 8 publications

(4 citation statements)

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“…This study utilizes data from experiments conducted to test the separation efficiency of both centrifugal and gravity-based downhole gas separators [1,2,11,12,18]. Refs.…”

Section: Data Collectionmentioning

confidence: 99%

“…Refs. [1,2,12] conducted experiments on the centrifugal gas separator, while Ref. [11] conducted experiments on the gravity-based downhole gas separator.…”

Section: Data Collectionmentioning

confidence: 99%

“…Ref. [12] conducted a comparative analysis between centrifugal and gravitational downhole separators, evaluating the performance of each through over 150 tests for centrifugal separators and 55 tests for gravitational separators. Their findings indicated that centrifugal separators outperformed gravitational separators in terms of separation efficiency.…”

Section: Introductionmentioning

confidence: 99%

“…Their findings indicated that centrifugal separators outperformed gravitational separators in terms of separation efficiency. The research aimed to assess the impact of flow rates on the performance and stability of a newly developed packer-type centrifugal separator, contrasting it with that of a gravitational separator [12]. Ref.…”

Section: Introductionmentioning

confidence: 99%

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Predicting Gas Separation Efficiency of a Downhole Separator Using Machine Learning

Sharma,

Osorio Ojeda,

Yuan

et al. 2024

Energies

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Artificial lift systems, such as electrical submersible pumps and sucker rod pumps, frequently encounter operational challenges due to high gas–oil ratios, leading to premature tool failure and increased downtime. Effective upstream gas separation is critical to maintain continuous operation. This study aims to predict the efficiency of downhole gas separator using machine learning models trained on data from a centrifugal separator and tested on data from a gravity separator (blind test). A comprehensive experimental setup included a multiphase flow system with horizontal (31 ft. (9.4 m)) and vertical (27 ft. (8.2 m)) sections to facilitate the tests. Seven regression models—multilinear regression, random forest, support vector machine, ridge, lasso, k-nearest neighbor, and XGBoost—were evaluated using performance metrics like RMSE, MAPE, and R-squared. In-depth exploratory data analysis and data preprocessing identified inlet liquid and gas volume flows as key predictors for gas volume flow per minute at the outlet (GVFO). Among the models, random forest was most effective, exhibiting an R-squared of 96% and an RMSE of 112. This model, followed by KNN, showed great promise in accurately predicting gas separation efficiency, aided by rigorous hyperparameter tuning and cross-validation to prevent overfitting. This research offers a robust machine learning workflow for predicting gas separation efficiency across different types of downhole gas separators, providing valuable insights for optimizing the performance of artificial lift systems.

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“…This study utilizes data from experiments conducted to test the separation efficiency of both centrifugal and gravity-based downhole gas separators [1,2,11,12,18]. Refs.…”

Section: Data Collectionmentioning

confidence: 99%

“…Refs. [1,2,12] conducted experiments on the centrifugal gas separator, while Ref. [11] conducted experiments on the gravity-based downhole gas separator.…”

Section: Data Collectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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