Data mining/analysis

This paper introduces in-pipe inspection technologies enabling high-resolution digital measurements of tubular internal diameter and wall thickness for critical downhole applications.
In this study, the authors propose the use of a deep-learning reduced-order surrogate model that can lower computational costs significantly while still maintaining high accuracy for data assimilation or history-matching problems.
This paper describes an approach to creating a digital, interconnected workspace that aligns sensor data with operational context to place the completions engineer back into a central role.

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