Status and Prospect of Artificial Intelligence Application in Fracturing Technology
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摘要:
随着人工智能理论和计算机技术的快速发展，智能化和数字化已成为推动储层压裂技术发展的重要力量。针对压裂技术智能化发展，阐述了人工智能技术在地质参数预测、压裂参数优化设计、压裂施工实时诊断与调控、压裂工具及材料研发等方面的研究进展与应用情况，分析了当前智能压裂技术发展存在的主要问题与今后的重点发展方向，认识到智能压裂技术仍处于探索试验阶段，国外在“甜点”智能识别、压裂参数优化、现场施工智能化控制等方面的研究已走在前列，并在北美地区多个区块的压裂服务中成功应用，国内仅在压裂大数据机器学习、智能化压裂材料等方面进行了早期探索，在智能压裂装备、工具、实时监测诊断和现场智能化调控等方面的研究与应用较少，与国外存在较大差距。分析认为，数据样本可靠性差、一体化智能压裂方法与装备欠缺和多领域交叉人才缺乏等是影响智能压裂技术快速发展的关键问题，并预测随着万物互联技术的发展，将形成智能化完井压裂系统，不需要人工干预即可完成储层评估、“甜点”识别、压裂参数优化设计、现场调控和压后评估等工作，真正实现一体化智能储层改造。
Abstract:With the rapid development of artificial intelligence (AI) theory and computer technology, intelligence and digitalization have become important forces to promote the fracturing technology of reservoirs. In terms of the intelligent development of the fracturing technology, the research progress and application of AI technology in the prediction of geological parameters, optimization design of fracturing parameters, real-time diagnosis and control of fracturing construction, and development of fracturing tools and materials were introduced. The main problems existing in the development of the intelligent fracturing technology as well as the key development direction were analyzed. It suggests that the intelligent fracturing technology was still in the stage of exploration and trial. Foreign countries have taken the lead in the intelligent identification of sweet spots, optimization of fracturing parameters, and intelligent control of field construction, and they have obtained successful applications in fracturing services in several fields in North America. China has only carried out early exploration in fracturing big data machine learning, intelligent fracturing materials, and there was no significant progress in intelligent fracturing equipment and tools, real-time monitoring and diagnosis, and intelligent field control. Therefore, there is a gap between China and other countries. The key problems affecting the development of intelligent fracturing technology were proposed, including the poor reliability of data samples, the lack of integrated intelligent fracturing methods and equipment, and the shortage of interdisciplinary talents. It was also predicted that with the development of the Internet of Everything technology, an intelligent completion fracturing system would be developed, which could complete reservoir evaluation, the sweet spot identification, optimization design of fracturing parameters, field control, post-fracture, etc. without human intervention. At that point, the integrated intelligent reservoir stimulation could be truly realized.
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图 1 J.Guevara等人建立的智能预测模型流程
Figure 1. Flow of intelligent prediction model established by J.Guevara et al.
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图 2 压裂优化预测代理模型的工作流程
Figure 2. Workflow of predictive surrogate model for fracturing optimization
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图 3 预测井口压力的循环机器学习流程
Figure 3. Flow of continuous machine learning for wellhead pressure prediction
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