Neural Network-Based Beam Pumper Model Optimization.

Area of Science:

• Mechanical Engineering
• Petroleum Engineering
• Artificial Intelligence

Background:

• Beam pumpers are essential rod pumpers in oil extraction, driven by surface dynamic transmission devices.
• Current beam pumper design can be improved for diversity, standardization, and energy efficiency.
• Modern industrial model design theories offer opportunities for beam pumper optimization.

Purpose of the Study:

• To optimize the model of beam pumpers using neural network technology.
• To enhance the system efficiency, diversity, serialization, and standardization of beam pumpers.
• To establish a mapping between model parameters and system efficiency for predictive optimization.

Main Methods:

• Decomposition of beam pumper system efficiency into surface and downhole components.
• Analysis of working efficiencies for both surface and downhole operations.
• Development and training of a radial basis function (RBF) neural network using sample data.
• Construction of a mapping between beam pumper model parameters and system efficiency.

Main Results:

• A neural network model was successfully established and trained for beam pumper optimization.
• The mapping between model parameters and system efficiency was effectively constructed.
• The study demonstrated the effectiveness of the proposed neural network model for beam pumper optimization.
• Predicted parameters for model optimization were identified, showcasing the model's utility.

Conclusions:

• The neural network approach provides an effective method for optimizing beam pumper models.
• Optimized beam pumper designs can lead to increased efficiency and reduced energy consumption.
• This research contributes to the advancement of standardized and serialized beam pumper designs.