Calculation Method for Inflow Performance Relationship in Sucker Rod Pump Wells Based on Real-Time Monitoring Dynamometer Card

Zhang, Ruichao; Yin, Yuqiong; Xiao, Liangfei; Chen, Dechun

doi:10.1155/2020/8884988

Cited by 6 publications

(3 citation statements)

References 18 publications

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“…is the discharge pressure of the pumping unit, is the average velocity during the upstroke, m/s. The volumetric coefficient of the mixed liquid [13] is given by: (7) Where:…”

Section: Figure 2 Convex Hull Of the Pump Cardmentioning

confidence: 99%

A study on yield calculation method using suspension power chart in beam pumping systems

Jiang,

Li,

Chen

et al. 2024

J. Phys.: Conf. Ser.

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Accurate production prediction plays a crucial role in well fault diagnosis and parameter optimization, significantly contributing to the advancement of oilfield digital construction. This paper focuses on the dynamic analysis of beam pumping machines to establish a calculation model for the power diagram of rod pumping systems. The solution involves employing the finite difference method and Fourier series method, facilitating the transition from the suspension point work diagram to the pump work diagram. The effective stroke of the plunger is determined by analyzing the opening and closing points of the Ver using the maximum curvature analysis method. Subsequently, the leakage and volume coefficients are derived by integrating on-site production data, culminating in the realization of an oil well production calculation method based on the suspension work diagram. The compilation of the pump power chart and oil well production calculation program was executed using QT 6.2.3. Comparative analysis with measured production data revealed an average accuracy of 90.05% using the finite difference method, surpassing the performance of the Fourier series algorithm. The established model not only offers a reliable means of predicting production but also provides valuable technical support for the digital and intelligent applications within the oilfield.

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“…is the discharge pressure of the pumping unit, is the average velocity during the upstroke, m/s. The volumetric coefficient of the mixed liquid [13] is given by: (7) Where:…”

Section: Figure 2 Convex Hull Of the Pump Cardmentioning

confidence: 99%

A study on yield calculation method using suspension power chart in beam pumping systems

Jiang,

Li,

Chen

et al. 2024

J. Phys.: Conf. Ser.

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show abstract

“…This indicates the pump's increased effort to lift the viscous fluid. Additionally, the dynamometer card might display irregularities in its load pattern, hinting at potential valve operation issues stemming from the fluid's viscosity [31]. In Figure 5, a high viscosity fluid dynamometer card is presented.…”

Section: Gas Interferencementioning

confidence: 99%

Unveiling Deep Learning Insights: A Specialized Analysis of Sucker Rod Pump Dynamographs, Emphasizing Visualizations and Human Insight

Martinović,

Bijanić,

Danilović

et al. 2023

Mathematics

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This study delves into the heightened efficiency and accuracy of 11 deep learning models classifying 11 dynamograph classes in the oil production sector. Introducing a novel framework with the Grad–CAM method, we address the “black box” issue, providing transparency in the models’ decision-making processes. Our analysis includes a comparative study with human experts, revealing a comprehensive understanding of both machine and human interpretive strategies. Results highlight the notable speed and precision of machine learning models, marking a significant advancement in rapid, reliable dynamograph classification for oil production decision-making. Additionally, nuanced findings in the model’s diagnostic accuracy reveal limitations in situations featuring the simultaneous occurrence of multiple pump issues. This underscores the need for additional features and domain-specific logic to enhance discernment and diagnostic precision in complex scenarios. The exploration of qualitative aspects distinguishes interpretive approaches, highlighting strengths and limitations. Machines, driven by algorithmic patterns and data processing, excel in rapid identification, albeit with occasional misclassifications. In contrast, human experts leverage experience and domain-specific knowledge for nuanced interpretation, providing a comprehensive understanding of both quantitative metrics and qualitative nuances. In conclusion, this study not only demonstrates the accelerated and enhanced accuracy of dynamograph classification by machine learning models compared to junior and medior domain experts, but also provides valuable insights into specific features and patterns guiding the decision-making process. This understanding allows continuous refinement, combining machine speed with human understanding for improved results in oil production. The potential for further studies and improvements in this domain is substantial.

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“…In stress-sensitive reservoirs, the absolute permeability of rocks can change with the variation of the pore pressure and stress state, which has a significant impact on reservoir productivity. Understanding the characteristics of the IPR (Huang et al, 1995;Jahanbani and Shadizadeh, 2009;Qasem et al, 2014;Changalvaie et al, 2015;Youssef, 2019;Zhang et al, 2020) is vital for evaluating the performance of a well and designing an optimal stimulation. The primary objective of this study was to provide a rapid and cost-efficient methodology to evaluate reservoir productivity in stress-sensitive reservoirs.…”

Section: Introductionmentioning

confidence: 99%

Single-phase inflow performance relationship in stress-sensitive reservoirs

Wang¹,

Gong²,

Huang³

et al. 2021

Adv. Geo-Energy Res.

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For stress-sensitive reservoirs, understanding the characteristics of the inflow performance relationship is vital for evaluating the performance of a well and designing an optimal stimulation. In this study, a reservoir simulator was used to establish the inflow performance relationship of a well for a wide variety of reservoirs and wellbore conditions. First, a base case was simulated using typical reservoir, wellbore, and fluid parameters. Subsequently, variations from the base case were investigated. The results of the simulation indicate that the dimensionless inflow performance relationship in the stress-sensitive reservoir is similar to the Vogel-type inflow performance relationship, which is used for evaluating the productivity of a vertical well in a solution-gas-drive reservoir. Unlike the two-phase flow in a solution-gas-drive reservoir, the nonlinear characteristic of the inflow performance relationship in stress-sensitive reservoirs is caused by stress-dependent permeability. Furthermore, the stress sensitivity level is the only parameter that affects the nonlinearity coefficient of the dimensionless inflow performance relationship equation. The nonlinearity coefficient was plotted against the stress sensitivity index, and the nonlinearity coefficient was found to be linearly proportional to the stress sensitivity index. This study provides a realistic and less expensive methodology to evaluate the reservoir productivity of stresssensitive reservoirs when the reservoir stress sensitivity level is known and to predict the reservoir stress sensitivity level when the inflow performance relationship of the stresssensitive reservoirs is known.

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Calculation Method for Inflow Performance Relationship in Sucker Rod Pump Wells Based on Real-Time Monitoring Dynamometer Card

Cited by 6 publications

References 18 publications

A study on yield calculation method using suspension power chart in beam pumping systems

A study on yield calculation method using suspension power chart in beam pumping systems

Unveiling Deep Learning Insights: A Specialized Analysis of Sucker Rod Pump Dynamographs, Emphasizing Visualizations and Human Insight

Single-phase inflow performance relationship in stress-sensitive reservoirs

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