Abstract:

To systematically review the research progress of intelligent fracturing technology in unconventional oil and gas reservoirs, by integrating machine learning algorithms (e.g., random forest and gradient boosting), the embedded discrete fracture model (EDFM), fiber-optic/microseismic monitoring technologies, and smart equipment, this study analyzes technological breakthroughs and application cases in key aspects such as reservoir parameter prediction, fracture propagation simulation, and real-time control. Results demonstrate that the random forest and gradient boosting models demonstrated optimal performance in permeability prediction (R²>0.92). The EDFM-AI workflow reduces fracture parameter calibration errors to 6.8%. Fiber-optic monitoring technology achieves sub-millimeter resolution in fracture detection. The intelligent early-warning system predicts sand plugging risks 30 seconds in advance (accuracy above 85%). Intelligent fracturing technology significantly enhances reservoir modification efficiency and production, but challenges such as small-sample generalization, multi-source data fusion, and equipment autonomy require further resolution. Establishing a closed-loop technical system encompassing “reservoir evaluation, optimized design, fracture monitoring, anomaly prediction, and equipment control”, and promote the development of intelligent and precise fracturing processes.

Key words: unconventional oil and gas reservoirs, intelligent fracturing, progress, prospects

摘要：

系统梳理了非常规油气藏智能压裂技术的研究进展，通过整合机器学习算法(如随机森林、梯度提升)、嵌入式离散裂缝模型(EDFM)、光纤/微地震监测技术及智能装备，分析了储层参数预测、裂缝扩展模拟、压裂实时调控等关键环节的技术突破与应用案例。结果表明：随机森林与梯度提升模型在渗透率预测中表现最优(R²>0.92)；EDFM-AI工作流将裂缝参数校准误差降至6.8%；光纤监测技术实现裂缝亚毫米级分辨率；智能预警系统可提前30秒预测砂堵风险(准确率>85%)。智能压裂技术显著提升了储层改造效率与产量，但需解决小样本数据泛化、多源数据融合及装备自主化等挑战。构建“储层评价-优化设计-裂缝监测-异常预警-装备调控”的闭环技术体系，将推动压裂工艺向智能化、精准化方向发展。

关键词: 非常规油气藏, 智能压裂, 进展, 展望

CLC Number: 

• TE377

• P618.13