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.
Application of ML to Downhole Camera Images
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.
