特种油气藏

特种油气藏 >

2025 , Vol. 32 >Issue 4: 14 - 24

DOI: https://doi.org/10.3969/j.issn.1006-6535.2025.04.002

综述

机器学习方法预测油气产量技术发展现状及前景展望

  • 谢坤 ,
  • 田轩硕 ,
  • 刘长龙 ,
  • 邵明 ,
  • 刘延春 ,
  • 高铭宣 ,
  • 袁世亮 ,
  • 张宝岩
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  • 1.东北石油大学提高油气采收率教育部重点实验室,黑龙江 大庆 163318;
    2.中海石油(中国)有限公司天津分公司渤海石油研究院,天津 300459;
    3.中国石油大庆油田有限责任公司采油工程研究院,黑龙江 大庆 163453
谢坤(1991—),男,副教授,2013年毕业于东北石油大学石油工程专业,2019年毕业于该校石油与天然气工程专业,获博士学位,现从事提高采收率理论和技术研究工作。

收稿日期: 2024-08-02

  修回日期: 2025-05-06

  网络出版日期: 2025-09-03

基金资助

国家自然科学基金青年基金“高矿化度油藏智能响应微凝胶构建及时变液流转向机制研究”(52204037);中央支持地方高校改革发展资金优秀青年项目“智能响应复合相微凝胶构建及时变液流转向效果研究”

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Status and prospects of machine learning methods for predicting hydrocarbon production

  • XIE Kun ,
  • TIAN Xuanshuo ,
  • LIU Changlong ,
  • SHAO Ming ,
  • LIU Yanchun ,
  • GAO Mingxuan ,
  • YUAN Shiliang ,
  • ZHANG Baoyan
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  • 1. Key Laboratory for Improving Oil and Gas Recovery (Northeast Petroleum University), Ministry of Education, Daqing, Heilongjiang 163318, China;
    2. CNOOC (China) Tianjin Company, Tianjin Branch, Bohai Oil Research Institute, Tianjin 300459, China;
    3. PetroChina Daqing Oilfield Co., Ltd., Oil Production Engineering Research Institute, Daqing, Heilongjiang 163453, China

Received date: 2024-08-02

  Revised date: 2025-05-06

  Online published: 2025-09-03

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摘要

受油气开发过程中储层物性、流体性质和工艺措施的复杂多变性影响,生产数据的分析利用程度多取决于石油科技工作者的专业经验,计算成本和时间成本高,难以满足油气藏高效开发需求,亟待发现更加高效的油气产量预测方法。近年来,以深度神经网络、随机森林算法和迁移学习为代表的机器学习方法凭借处理高维数据、捕捉时序数据长期依赖关系和提取开发数据特征等方面的独特优势,在油气产量预测中取得了显著应用效果。该文通过对常用油气产量预测机器学习方法的原理及其优缺点进行分析,阐述了机器学习方法在油气产量预测领域的应用现状,总结了应用过程中潜在的问题,同时对油气产量预测方法的发展前景进行展望。未来,一方面应加强对物理约束融入机器学习模型的研究,提高模型的可解释性,避免过于理想化的预测结果;另一方面要开发适合小样本情况下的算法和迁移学习技术,充分利用历史生产数据,为油气产量预测提供更好的数据分析技术支持。该研究对油气产量的智能预测技术完善具有理论指导意义。

关键词： 油气田开发; 产量预测; 机器学习; 神经网络; 迁移学习

本文引用格式

谢坤 , 田轩硕 , 刘长龙 , 邵明 , 刘延春 , 高铭宣 , 袁世亮 , 张宝岩 . 机器学习方法预测油气产量技术发展现状及前景展望[J]. 特种油气藏, 2025 , 32(4) : 14 -24 . DOI: 10.3969/j.issn.1006-6535.2025.04.002

Abstract

Affected by the complex variability of reservoir physical properties, fluid characteristics, and technological measures during hydrocarbon development, the utilization of production data largely depends on the professional experience of petroleum scientists and engineers, resulting in high computational and time costs. This makes it difficult to meet the demands of efficient modern hydrocarbon reservoir development, necessitating the discovery of more efficient methods for hydrocarbon production prediction. In recent years, machine learning methods represented by deep neural networks, random forest algorithms, and transfer learning have achieved significant application results in hydrocarbon production prediction due to their unique advantages in handling high-dimensional data, capturing long-term dependencies in time-series data, and extracting development data features. This paper analyzes the principles, advantages, and disadvantages of commonly used machine learning methods for hydrocarbon production prediction, elaborates on the current application status of these methods in the field of hydrocarbon production prediction, summarizes potential issues during application, and prospects the development trends of hydrocarbon production prediction methods. In the future, on one hand, research on integrating physical constraints into machine learning models should be strengthened to enhance model interpretability and avoid overly idealized prediction results; on the other hand, algorithms and transfer learning techniques suitable for small-sample scenarios should be developed to fully utilize historical production data, providing better data analysis and technical support for hydrocarbon production prediction. This research has theoretical significance for improving intelligent hydrocarbon production prediction technology.

Key words： oil and gas field development; production prediction; machine learning; neural network; transfer learning

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