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This work proposes the implementation of advanced technologies in the oil and gas industry, focusing on nitrogen generation in situ via membranes and its use in oil field stimulation. Situated within Industry 4.0, the strategy leverages intelligent systems to enhance operational efficiency. Artificial intelligence (AI) methods, including neural networks and neuro-fuzzy networks, are used to develop models that predict process behavior based on input-output data. These models are incorporated into optimization routines to improve system performance. Bioinspired algorithms, such as ant colony, particle swarm, and evolutionary algorithms, are applied to solve optimization problems and determine optimal process variables. The effectiveness of the optimized process will be validated using industry-standard software to assess improvements in oil extraction performance.

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An Approach Based on Neural Networks and Bioinspired Systems for Oil Production Systems Optimization

  • 会议论文

  • 发表日期：

    2026年

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  • Bryand J. Garcia-Sigales,
  • Jose A. Ruz-Hernandez,
  • Jose-Luis Rullán-Lara,
  • Alma Y. Alanis,
  • Mario Antonio Ruz Canul,
  • Rubén Salazar-Mendoza

摘要

This work proposes the implementation of advanced technologies in the oil and gas industry, focusing on nitrogen generation in situ via membranes and its use in oil field stimulation. Situated within Industry 4.0, the strategy leverages intelligent systems to enhance operational efficiency. Artificial intelligence (AI) methods, including neural networks and neuro-fuzzy networks, are used to develop models that predict process behavior based on input-output data. These models are incorporated into optimization routines to improve system performance. Bioinspired algorithms, such as ant colony, particle swarm, and evolutionary algorithms, are applied to solve optimization problems and determine optimal process variables. The effectiveness of the optimized process will be validated using industry-standard software to assess improvements in oil extraction performance.