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<p>Heavy oil exploitation technology is currently at a critical stage of transformation and upgrading. Traditional experience-driven methods for steam injection parameter design can no longer meet the needs of efficient modern oilfield development. With the rapid development of big data and artificial intelligence technologies, the petroleum industry needs to deeply integrate these advanced technologies with conventional extraction processes to achieve a shift from experience-based to data-driven decision-making. To address this issue, this study combines the theoretical basis of cyclic steam stimulation and steam injection parameters, introduces the characteristics analysis and processing of production big data, and establishes an intelligent optimization model. Using a specific oilfield as an example, we conduct intelligent optimization research on steam injection parameters for heavy oil cyclic steam stimulation based on production big data. This work lays the foundation for further development in China’s petroleum industry. The results show that the proposed method can effectively improve the scientific and individualized nature of parameter schemes. After optimization, the cumulative oil production of test wells increased by about 13%, and thermal energy utilization improved by nearly 10%, significantly enhancing economic benefits while ensuring safety. This approach has strong potential for wider application.</p>

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Research on Intelligent Optimization of Steam Injection Parameters for Heavy Oil Cyclic Steam Stimulation Based on Production Big Data

  • 期刊论文

  • 发表日期：

    2025年11月1日

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  • Lu Jia,
  • Guowei Shi,
  • Xing Lu,
  • Sixu Li,
  • Mingju Lan,
  • Yawen Jiang

摘要

Heavy oil exploitation technology is currently at a critical stage of transformation and upgrading. Traditional experience-driven methods for steam injection parameter design can no longer meet the needs of efficient modern oilfield development. With the rapid development of big data and artificial intelligence technologies, the petroleum industry needs to deeply integrate these advanced technologies with conventional extraction processes to achieve a shift from experience-based to data-driven decision-making. To address this issue, this study combines the theoretical basis of cyclic steam stimulation and steam injection parameters, introduces the characteristics analysis and processing of production big data, and establishes an intelligent optimization model. Using a specific oilfield as an example, we conduct intelligent optimization research on steam injection parameters for heavy oil cyclic steam stimulation based on production big data. This work lays the foundation for further development in China’s petroleum industry. The results show that the proposed method can effectively improve the scientific and individualized nature of parameter schemes. After optimization, the cumulative oil production of test wells increased by about 13%, and thermal energy utilization improved by nearly 10%, significantly enhancing economic benefits while ensuring safety. This approach has strong potential for wider application.