Proposing a Method for Performance Evaluation of a Designed Two-Phase Vertical Separator and a Piston Pump Using Computational Fluid Dynamics

Dastyar, Zahra; Rabieh, Milad M.; Hajidavalloo, Ebrahim

doi:10.2118/215822-pa

Cited by 2 publications

(2 citation statements)

References 21 publications

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“…Ref. [13] developed and validated a numerical model to evaluate a hybrid separator combined with a piston pump, utilizing two-phase turbulent CFD analysis. The study examines the impact of piston speed and gas/liquid ratio on the separator's performance, highlighting that higher piston speeds decrease efficiency due to greater pressure drops.…”

Section: Introductionmentioning

confidence: 99%

“…The study examines the impact of piston speed and gas/liquid ratio on the separator's performance, highlighting that higher piston speeds decrease efficiency due to greater pressure drops. It also introduces a correlation for predicting separator efficiency, providing key insights for optimizing downhole separator design and operation [13]. 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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting Gas Separation Efficiency of a Downhole Separator Using Machine Learning

Sharma,

Osorio Ojeda,

Yuan

et al. 2024

Energies

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Leveraging Machine Learning for Predicting Gas Separation Efficiency of a Downhole Separator

Sharma,

Osorio,

Ashesh

et al. 2024

Day 1 Mon, April 22, 2024

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Artificial lift systems, specifically Electrical Submersible Pumps and Sucker Rod Pumps, often face operational challenges due to high Gas-Oil Ratio, leading to premature tool failure and increased non-productive time. These issues underscore the necessity for efficient gas separation at upstream of the artificial lift pump to ensure uninterrupted operations. Downhole gas separators, situated upstream of the artificial lift pump, are designed to segregate gas from the fluid, reducing downtime. This study aims to predict the gas separation efficiency of the downhole gas separator using experimental data from a centrifugal separator and testing the generated models on experimental data for a gravity separator. To predict the separation efficiency of the downhole separator, multiple machine learning models were evaluated, and a comparative analysis was performed. The experimental test data obtained for the centrifugal packer-type separator was used for model training and experimental test data for the gravity separator was used for model testing (blind test). To conduct experiments, multiphase flow setup (31ft. (9.4m) long horizontal and 27ft. (8.2m) tall vertical section of multiphase flow setup) was used. In this study, seven regression methods, including multilinear (MLR), random forest (RF), support vector machine (SVM), ridge, lasso, k-nearest neighbor (KNN) and XGBoost, were examined to assess their ability to effectively predict the gas separation efficiency. Several performance metrics, including Root mean square error (RMSE), Mean absolute percentage error (MAPE) and R-squared were evaluated to select the efficient model. In-depth exploratory data analysis and feature engineering were performed to identify significant input parameters for model training. The scatterplot and correlation plot indicated the response variable were mostly affected by inlet liquid and gas volume flow. Therefore, liquid and gas volume flow at inlet were selected as two independent parameters to predict gas volume flow per minute at the outlet (GVFO) as the response variable. After data scaling, a comparative analysis of the seven incorporated regression models revealed that, the Random Forest model to be the most efficient for predicting GVFO, closely followed by the KNN model. The Random Forest model exhibits R-squared values of 96% and RMSE value of 112. Hyperparameter tuning and tenfold cross-validation ensured optimal values for tuning parameters and prevented oveffitting, respectively. This developed machine learning workflow enables the prediction of the gas separation efficiency of different type of downhole gas separator by using experimental test data from a centrifugal downhole separator. It provides critical findings through a comparative analysis of seven regression models. This research offers valuable insights that can aid in optimizing the performance of artificial lift systems.

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Proposing a Method for Performance Evaluation of a Designed Two-Phase Vertical Separator and a Piston Pump Using Computational Fluid Dynamics

Cited by 2 publications

References 21 publications

Predicting Gas Separation Efficiency of a Downhole Separator Using Machine Learning

Predicting Gas Separation Efficiency of a Downhole Separator Using Machine Learning

Leveraging Machine Learning for Predicting Gas Separation Efficiency of a Downhole Separator

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