Experimental Comparison of Two Downhole Separators in Boosting Artificial Lift Performance

Olubode, Michael; Osorio, Laura; Karami, Hamidreza; McCoy, J.N.; Podio, Tony

doi:10.2118/209723-ms

Cited by 4 publications

(4 citation statements)

References 12 publications

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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%

“…[1,2,12] conducted experiments on the centrifugal gas separator, while Ref. [11] conducted experiments on the gravity-based downhole gas separator. In total, 260 tests were performed to assess the separation efficiency of the centrifugal separator, and 55 tests were conducted for the gravity-based separator using multiphase flow facility as shown in next section.…”

Section: Data Collectionmentioning

confidence: 99%

“…The research found the separator consistently achieved over 90% liquid separation efficiency across various conditions, although efficiency slightly dropped at higher liquid rates. This highlights the separator's potential to significantly reduce gas-related issues in pumping systems, marking a notable advancement in artificial lift technologies [11]. Ref.…”

Section: Introductionmentioning

confidence: 97%

“…Ref. [11] conducted multiple tests to evaluate a newly designed centrifugal packer-type downhole separator for artificial lift systems in oil and gas wells. The research found the separator consistently achieved over 90% liquid separation efficiency across various conditions, although efficiency slightly dropped at higher liquid rates.…”

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%

Section: Data Collectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Predicting Gas Separation Efficiency of a Downhole Separator Using Machine Learning

Sharma,

Osorio Ojeda,

Yuan

et al. 2024

Energies

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Application of Machine Learning to Evaluate the Performances of Various Downhole Centrifugal Separator Types in Oil and Gas Production Systems

Ojeda

Olubode

Karami

et al. 2023

SPE Oklahoma City Oil and Gas Symposium

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Pumping artificial lift techniques, such as rod pumps and ESPs, are applied for gassy wells more than ever before. This has made the downhole separators a critical part of most such installations. There are multiple categories of downhole separators, with various techniques developed to assess and improve their performances, but no general guidelines are established for their application. This paper aims to classify the separator types and review their performances in the open literature. In addition, various data sets are collected and put together to evaluate and rank downhole centrifugal separators using data analysis and machine learning (ML) techniques. A comprehensive literature review is conducted to collect the available downhole separator performance data. Experiments and Computational Fluid Dynamic (CFD) simulations are the techniques used by the researchers. This information is collected to identify the optimum conditions for each separator type, considering the effects of liquid and gas rates and other flow parameters. The data collected from various research projects over the last 20 years are combined to make a comprehensive downhole separation databank. These data are analyzed using various machine learning algorithms to rank the performances of downhole separators at various operating conditions. Various downhole separators have been tested in the open literature, including poor-boy separators, two-stage separators, packer-type separators, rotary and spiral separators with different designs, etc. A critical factor that adds to the uncertainty is the separator's control system, which significantly affects its efficiency. The available data show that most separators provide separation efficiencies higher than 80% if the downstream casing valve is adequately controlled. The separation efficiencies decline as the liquid and gas rates increase past an upper limit. The collected data from multiple previous studies form a broad dataset. Data analysis is used to compare the performances of different downhole separator classes, and machine learning is applied to identify a robust prediction model. This paper gathers, interconnects, and examines several available research works through data analytics. The results provide a fundamental source and a valuable guideline for downhole liquid-gas separation, particularly in artificial lift applications.

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Application of Machine Learning and Dimensional Analysis to Evaluate the Performances of Various Downhole Centrifugal Separator Types

Ojeda

2023

SPE Annual Technical Conference and Exhibition

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Pumping artificial lift techniques, such as rod pumps and ESPs, are applied for gassy wells more than ever before. Gas largely affects pump performance, causing cavitation, gas locking, low production, and run-life shortening. These phenomena have made downhole separators a critical part of such installations. There are multiple categories of downhole separators, with various techniques developed to assess and improve their performances, but no general guidelines are established for their application. Through dimensional analysis and machine learning techniques, this paper aims to establish key parameters in the performance characterization of this equipment, and prediction models are attempted. A comprehensive literature review is conducted to understand factors (retention time, turbulence, and head) influencing the efficiency of this equipment and collect the available downhole separator performance data. This information is collected to identify the optimum conditions for each separator type, considering the effects of liquid and gas rates and other flow parameters. The data collected from various research projects over the last 20 years are combined to make a comprehensive centrifugal downhole separation databank. The available data show that most separators provide separation efficiencies higher than 79% if the downstream pressure-controlling systems and liquid levels are adequately monitored. The separation efficiencies decline as the liquid and gas rates increase past an upper limit of around 800 BPD. Furthermore, the separator's control system is a critical factor that adds to the uncertainty, significantly affecting the separation efficiency calculation. Data analysis of the Buckingham Pi Theorem for dimensionless numbers is used to compare the performances of different downhole separator classes, and machine learning is applied to identify a robust prediction model. Two dimensionless numbers, the Weber number, and the gas Reynolds number, were found to largely dominate the behavior of the separators’ performance. Results provide a fundamental source and a valuable guideline for downhole liquid-gas separation, particularly in artificial lift applications.

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Experimental Comparison of Two Downhole Separators in Boosting Artificial Lift Performance

Cited by 4 publications

References 12 publications

Predicting Gas Separation Efficiency of a Downhole Separator Using Machine Learning

Predicting Gas Separation Efficiency of a Downhole Separator Using Machine Learning

Application of Machine Learning to Evaluate the Performances of Various Downhole Centrifugal Separator Types in Oil and Gas Production Systems

Application of Machine Learning and Dimensional Analysis to Evaluate the Performances of Various Downhole Centrifugal Separator Types

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