本文全面展示了多物理场井下成像与机器学习（ML）技术的融合如何显著提升射孔侵蚀分析的水平。作者介绍了一种能够提高测量精度、一致性和缩短周转时间的新方法，并阐述了该方法如何应用于完井设计和水力压裂优化领域，从而提高非常规油井的产能并降低成本。
在应用机器学习模型之前，对于一口典型的油井，分析大约 500 个射孔通常需要 3 到 4 周时间。随着行业的发展和水平井长度的增加，每口井平均射孔数量通常约为 1000 个。如果不应用机器学习，则需要 6 到 7 周时间才能完成图像采集、射孔尺寸测量以及按射孔、射孔簇和射孔段计算侵蚀量并最终得出结果。
The complete paper demonstrates how the integration of multiphysics downhole imaging with machine-learning (ML) techniques represents a major advance in perforation erosion analysis. The authors describe a novel approach that improves measurement accuracy, consistency, and turnaround time, illustrating how this capability benefits the fields of completion design and optimization of hydraulic fracturing and enables gains in productivity and cost reduction in unconventional-wells development.
Before the application of ML models, for a typical well, the analysis process for approximately 500 perforations would often take 3–4 weeks. As the industry has developed and drilled longer lateral wells, it is common to have an average of approximately 1,000 perforations per well. Without the application of ML, it would require 6–7 weeks to fully capture images; dimension perforations; and compute erosion by perforation, cluster, and stage before reporting results.
