油气产量预测方法与技术研究进展

doi:10.3969/j.issn.1672-7703.2025.05.010

摘要/Abstract

摘要： 油气产量预测是优化油气田开发策略、提升采收率的关键技术手段。本文系统梳理了油气产量递减规律的理论体系，对比分析了传统经验模型与解析方法的理论框架、适用条件及局限性，并重点探讨了机器学习在复杂储层产量预测中的创新应用。分析认为：（1）传统方法在常规储层中仍具稳健性，但其在非常规油气田中受限于强非均质性和多相渗流非线性等复杂条件；（2）数据驱动模型通过自动特征提取与时空关联建模，在非常规储层预测中展现出显著优势；（3）物理约束混合模型有效融合数据驱动能力与物理机理，在复杂条件及长期预测中表现出更可靠的预测性能。研究结果表明，人工智能技术显著提升了油气产量预测的精度与可靠性，其中机器学习和深度学习方法为复杂储层开发提供了创新技术支撑。然而，该技术在实时计算和模型可解释性等工程应用层面仍存在挑战，需进一步深化人工智能与油气领域的交叉融合研究，以促进油气行业向智能化、高质量方向发展。

关键词: 产量预测, 递减模型, 机器学习, 深度学习, 应用分析

Abstract: Oil and gas production forecast is a critical technical approach for optimizing development strategy and enhancing recovery factor of oil and gas fields. The theoretical system of oil and gas production decline has systematically been reviewed, and a comparative evaluation between conventional empirical models and analytical methods has been conducted in terms of their theoretical foundations, applicability, and limitations. In addition, innovative application of machine learning in production forecast of complex reservoirs has been discussed in detail. The analysis results show that: (1) Traditional methods show robustness for conventional reservoirs but exhibit constrained application performance in unconventional oil and gas reservoirs due to strong heterogeneity and nonlinear multiphase flow; (2) The data-driven models demonstrate superior prediction performance for unconventional reservoirs through automated feature extraction and spatiotemporal correlation modeling; (3) The physical-informed hybrid models effectively integrate data-driven advantages with physical mechanisms, delivering enhanced reliability in complex conditions and long-term production forecast. The study concludes that artificial intelligence significantly improves prediction accuracy and reliability in oil and gas production forecast, with machine learning and deep learning offering novel technical support for complex reservoir development. However, challenges persist in engineering applications, particularly in real-time computation and model interpretability, where further interdisciplinary research is needed on artificial intelligence and oil and gas domain to promote intelligent and high-quality development of the oil and gas industry.

Key words: production forecast, decline model, machine learning, deep learning, application analysis

中图分类号:

TE313

刘保磊, 张心怡. 油气产量预测方法与技术研究进展[J]. 中国石油勘探, 2025, 30(5): 128-144.

Liu Baolei, Zhang Xinyi. Research progress in oil and gas production forecast method and technology[J]. China Petroleum Exploration, 2025, 30(5): 128-144.

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