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Updated: Jul 4, 2025

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An uncertainty approach for Electric Submersible Pump modeling through Deep Neural Network.

Erbet Almeida Costa1,2, Carine de Menezes Rebello2, Vinicius Viena Santana2

  • 1Programa de pós-graduação em Mecatrônica, Universidade Federal da Bahia, Rua Prof. Aristides Novis, n 2., Salvador, 40210-630, Brazil.

Heliyon
|January 31, 2024
PubMed
Summary

This study introduces a new method for validating deep learning models in artificial oil lift systems, ensuring reliable AI dynamic models for digital twins and control applications.

Keywords:
Deep neural networksElectric submersible pumpMCMCUncertainty assessment

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Area of Science:

  • Petroleum Engineering
  • Artificial Intelligence
  • Machine Learning

Background:

  • Artificial oil lift systems using submersible electric pumps often require accurate predictive models.
  • Traditional deep learning models necessitate large datasets, posing a challenge for complex systems.
  • Evaluating the uncertainty of AI models is crucial for reliable system operation.

Purpose of the Study:

  • To propose a novel methodology for identifying and validating deep learning models for artificial oil lift systems.
  • To enable joint and systematic evaluation of model performance and prediction uncertainty.
  • To develop computationally lighter AI dynamic models suitable for digital twin construction, control, and optimization.

Main Methods:

  • Utilized a nonlinear model to generate synthetic training and validation data, mitigating the need for extensive real-world datasets.
  • Employed the Markov Chain Monte Carlo algorithm to systematically assess the epistemic uncertainty of neural networks.
  • Implemented the methodology in Python using Tensorflow and Keras for neural network construction and hyperparameter tuning.
  • Validated the developed deep learning models against experimental data, including uncertainty assessment.

Main Results:

  • The proposed methodology successfully generated deep learning models that accurately represent both the nonlinear model's dynamics and experimental data.
  • The models provided a most probable value close to experimental data, with prediction uncertainty comparable to the nonlinear model.
  • Uncertainty assessment confirmed the adequate validation of the developed AI dynamic models.
  • The resulting models are computationally efficient and reliable.

Conclusions:

  • The developed methodology offers a robust approach for creating and validating deep learning models for artificial oil lift systems.
  • The AI dynamic models are suitable for applications requiring high reliability and computational efficiency, such as digital twins.
  • This work advances the application of AI in optimizing oil and gas production systems.