Improving Sucker Rod Pump Performance and Overall Production After Applying Continues Steam Injection in Heavy Oil Project-North Kuwait

AbdulHadi, Fahd; Al-Ajeel, Fatemah; Sierra, Tomas; Mohamed, Assem; Heshmat, Kareem

doi:10.2118/193800-ms

Cited by 2 publications

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A comprehensive review of sucker rod pumps’ components, diagnostics, mathematical models, and common failures and mitigations

Fakher

Khlaifat

Hossain

et al. 2021

J Petrol Explor Prod Technol

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In many oil reservoirs worldwide, the downhole pressure does not have the ability to lift the produced fluids to the surface. In order to produce these fluids, pumps are used to artificially lift the fluids; this method is referred to as artificial lift. More than seventy percent of all currently producing oil wells are being produced by artificial lift methods. One of the most applied artificial lift methods is sucker rod pump. Sucker rod pumps are considered a well-established technology in the oil and gas industry and thus are easy to apply, very common worldwide, and low in capital and operational costs. Many advancements in technology have been applied to improve sucker rod pumps performance, applicability range, and diagnostics. With these advancements, it is important to be able to constantly provide an updated review and guide to the utilization of the sucker rod pumps. This research provides an updated comprehensive review of sucker rod pumps components, diagnostics methods, mathematical models, and common failures experienced in the field and how to prevent and mitigate these failures. Based on the review conducted, a new classification of all the methods that can fall under the sucker rod pump technology based on newly introduced sucker rod pump methods in the industry has been introduced. Several field cases studies from wells worldwide are also discussed in this research to highlight some of the main features of sucker rod pumps. Finally, the advantages and limitations of sucker rod pumps are mentioned based on the updated review. The findings of this study can help increase the understanding of the different sucker rod pumps and provide a holistic view of the beam rod pump and its properties and modeling.

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A comprehensive review of sucker rod pumps’ components, diagnostics, mathematical models, and common failures and mitigations

Fakher

Khlaifat

Hossain

et al. 2021

J Petrol Explor Prod Technol

View full text Add to dashboard Cite

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Machine Learning Models to Predict Production Rate of Sucker Rod Pump Wells

Thabet,

Zidan,

Elhadidy

et al. 2024

SPE Western Regional Meeting

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The design, operation, and optimization of Sucker Rod Pumping (SRP) systems necessitate the utilization of production data. However, forecasting fluid flow rates at the surface of SRP artificially lifted wells usually poses a challenge, especially in instances where traditional separators and multiphase flowmeters are not universally available. Consequently, this study introduces nine machine learning (ML) models employing real data sourced from 598 wells with a production history exceeding three years. The dataset, comprising 8,372 data points, undergoes a random split allocating around 80% of the data (6,697 data points) for training, while around 20% (1,675 data points) are used for testing. The ML models encompass Gradient Boosting (GB), Adaptive Boosting (AdaBoost), Random Forest (RF), Support Vector Machines (SVMs), Decision Tree (DT), K-Nearest Neighbor (KNN), Linear Regression (LR), Artificial Neural Network (ANN), and Stochastic Gradient Descent (SGD). The chosen input features for the models are readily accessible during any SRP well-lifting process, and these inputs include various variables such as wellhead flowing pressure, casing pressure, predicted bottom hole fluid production rate, predicted bottom hole oil production rate, net liquid head above the pump, pump size, pump clearance, stroke length, pump speed, pump setting depth, the temperature at the pump depth, oil gravity, and water viscosity. Evaluation of the different ML models’ performance is carried out by two methodologies: K-fold cross-validation, and repeated random sampling. The findings reveal that the top-performing models are GB, AdaBoost, RF, LR, and SGD, exhibiting mean absolute percentage errors of 3.6%, 3.4%, 3.4%, 4.0%, and 4.4% respectively, and correlation coefficients (R2) of 0.937, 0.934, 0.935, 0.921, and 0.915, respectively. In practical field application, these models are deployed on a well within Egypt's Western Desert fields, demonstrating excellent agreement between actual fluid rates and model predictions. In conclusion, across diverse pumping scenarios and completion configurations, the ML models could effectively forecast production rates for different SRP wells. This capability facilitates continuous monitoring, optimization, and performance analysis of SRP wells, enabling swift responses to operational challenges since the proposed ML models offer an accessible, rapid, and cost-effective alternative to conventional separators and multiphase flowmeters.

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Improving Sucker Rod Pump Performance and Overall Production After Applying Continues Steam Injection in Heavy Oil Project-North Kuwait

Cited by 2 publications

References 0 publications

A comprehensive review of sucker rod pumps’ components, diagnostics, mathematical models, and common failures and mitigations

A comprehensive review of sucker rod pumps’ components, diagnostics, mathematical models, and common failures and mitigations

Machine Learning Models to Predict Production Rate of Sucker Rod Pump Wells

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