国内外智能压裂技术现状及发展趋势

Development Status and Future Trends of Intelligent Fracturing Technologies

蒋廷学 周珺 廖璐璐

JIANG Tingxue ZHOU Jun LIAO Lulu

页岩油气富集机理与有效开发国家重点实验室, 北京102206 中国石化石油工程技术研究院, 北京102206

State Key Laboratory of Shale Oil and Gas Enrichment Mechanisms and Effective Development, Beijing, 102206, China Sinopec Research Institute of Petroleum Engineering, Beijing, 102206, China

随着人工智能技术的快速发展及其在油气领域的广泛应用，智能压裂技术取得较大的进展，包括裂缝参数及压裂参数智能优化、智能压裂流体及材料、智能压裂设备及工具、压裂风险智能预警系统、压裂参数实时优化智能控制和压裂裂缝智能监测技术等方面，但其智能化程度不一，没有形成一个完整的智能压裂技术体系。在分析智能压裂技术现状的基础上，指出了智能压裂的发展趋势，包括开展小数据样本的深度挖掘、建立基于三维甜点分布的地质工程一体化压裂智能决策平台、研制智能响应性压裂流体及材料、研制裂缝扩展四维智能监控模型并实现可视化、研制无人值守压裂设备及智能工具等，这对于形成完整统一的智能压裂技术体系，实现新一轮水力压裂技术的革新具有重要意义。

With the rapid development of artificial intelligence technology and its wide implementation in the oil and gas industry, impressive progress in intelligent fracturing technologies has been achieved. These are technologies that involve the intelligent optimization of fracture and fracturing parameters, fluids and materials, equipment and tools, early warning system for fracturing risks, control of real-time fracturing parameter optimization, and fracture monitoring, etc. Despite that, a complete intelligent fracturing technology system remains to be developed due to unbalanced developed degree among those aspects.After the development status of intelligent fracturing technologies was analyzed, the development trends of intelligent fracturing were identified, including in-depth data mining of small data samples, building an intelligent decision-making platform for geological-engineering integrated fracturing based on 3D sweet spot distribution, developing intelligent responsive fracturing fluids and materials, creating a 4D intelligent monitoring model for fracture propagation and achieving visualization, and building unattended fracturing equipment and intelligent tools. All of these are crucial for developing a complete and uniform intelligent fracturing technology system and accomplishing another round of hydraulic fracturing technology innovations.

中国石化科技前瞻项目“基于深度学习的压裂智能优化设计方法研究”和“基于AI的加拿大Wapiti致密储层工程参数优化” （编号：P21031-1)联合资助

https://doi.org/10.11911/syztjs.2022065