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Proportional-Derivative (PD) control is a widely used control method in various engineering systems to enhance stability and performance. In a system with only proportional control, common issues include high maximum overshoot and oscillation, observed in both the error signal and its rate of change. This behavior can be divided into three distinct phases: initial overshoot, subsequent undershoot, and gradual stabilization.
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In automotive engineering, car suspension systems often employ Proportional Derivative (PD) controllers to enhance performance. PD controllers are utilized to adjust the damping force in response to road conditions. A controller, acting as an amplifier with a constant gain, demonstrates proportional control, with output directly mirroring input.
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Controller configurations are crucial in a car's cruise control system because they manage speed over time to maintain a consistent pace regardless of road conditions, thereby meeting design goals. In traditional control systems, fixed-configuration design involves predetermined controller placement. System performance modifications are known as compensation.
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Proportional-Integral-Derivative (PID) controllers are widely used in various control systems to enhance stability and performance. In a thermostat, it adjusts heating or cooling based on the temperature difference between the actual and desired levels. They are often used in automotive speed systems, effectively managing sudden speed changes while maintaining a constant speed under varying conditions. On the other hand, PI controllers, commonly employed in voltage regulation, enhance stability...
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Rolling resistance, also known as rolling friction, is the force that resists the motion of a rolling object, such as a wheel, tire, or ball, when it moves over a surface. It is caused by the deformation of the object and the surface in contact with each other, as well as other factors like internal friction, hysteresis, and energy losses within the materials. Rolling resistance opposes the object's motion, requiring additional energy to overcome it and maintain movement. In practical...
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Proportional Integral (PI) controllers are a fundamental component in modern control systems, widely used to enhance performance and mitigate steady-state errors. They are particularly effective in applications such as automatic brightness adjustment on smartphones, where they excel at mitigating steady-state errors for step-function inputs. Unlike PD controllers, which require time-varying errors to function optimally, PI controllers leverage their integral component to address residual...
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Related Experiment Video

Updated: Jul 24, 2025

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
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Optimization of Profile Control and Oil Displacement Scheme Parameters Based on Deep Deterministic Policy Gradient.

Chaodong Tan1,2, Chunqiu Wang2, Jinjie Tian3

  • 1Department of Automation, China University of Petroleum, Changping, Beijing 102249, China.

ACS Omega
|July 10, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a deep deterministic policy gradient (DDPG) model to optimize profile control and oil displacement (PCOD) parameters, significantly boosting oil production and recovery efficiency in oil fields.

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Area of Science:

  • Petroleum Engineering
  • Artificial Intelligence in Reservoir Management
  • Enhanced Oil Recovery (EOR)

Background:

  • Effective parameter design for profile control and oil displacement (PCOD) is crucial for enhancing waterflooding efficiency and maximizing oil field production and recovery.
  • Traditional optimization methods may not fully capture the complex dynamics of PCOD processes.
  • The need for advanced computational methods to optimize PCOD parameters for improved oil recovery is evident.

Purpose of the Study:

  • To develop and validate a parameter optimization model for PCOD schemes using deep deterministic policy gradient (DDPG).
  • To maximize the half-year increased oil production (Q) of injection well groups.
  • To optimize PCOD system type, concentration, injection volume, and injection rate under defined constraints.

Main Methods:

  • Constructed a parameter optimization model and solution method based on DDPG.
  • Utilized historical PCOD data and extreme gradient boosting (XGBoost) to create a proxy model of the PCOD process as the environment.
  • Defined the reward function based on the change rate of Q, with system type, concentration, injection volume, and rate as actions, and employed a Gaussian strategy with noise for exploration.

Main Results:

  • The DDPG-based model successfully optimized parameters for a compound slug PCOD process (pre-slug + main slug + protection slug) in an offshore oil field example.
  • Achieved higher oil production PCOD schemes for well groups with varying PCOD characteristics.
  • Demonstrated superior optimization and generalization capabilities compared to the particle swarm optimization (PSO) model.

Conclusions:

  • The DDPG-based parameter optimization model is effective for improving PCOD scheme performance.
  • The proposed method offers significant advantages in optimizing oil field production and recovery.
  • The DDPG approach shows strong potential for practical application in complex reservoir management scenarios.