Drilling and Well Engineering
Abstract
Abstract
Abstract Reliable evaluation of cement sheath integrity is essential for well integrity management, yet conventional interpretation of acoustic cement logs is time-consuming and subject to variability among specialists. This paper presents a machine learning–based framework de-signed to support and accelerate cement bond evaluation. The system was developed using a database with data from over 100 wells and approximately 120 km of interpreted acoustic logs. The system automatically classifies cement bonding conditions and generates interpretation results with a vertical resolution of 1 m in minutes. Compared with traditional manual workflows, which typically assess intervals of 5 m or greater, the proposed approach increases interpretative granularity while significantly reducing analysis time. ML-assisted interpretation allows clear intervals to be evaluated rapidly, enabling specialists to focus on uncertain sections and reducing variability and interpretative bias. The platform supports multiple well life cycle stages, including plug and abandonment. Faster turnaround, improved planning efficiency, and increased confidence in barrier verification contribute to reduced operational costs and lower environmental risk.
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@article{Camerini2026Revolutionizing,
title = {Revolutionizing Cement Layer Integrity Assessment with AI},
author = {I. G. Camerini and L. P. B. de Souza and G. R. B. Ferreira and M. A. M. Amendoeira and A. S. Rodrigues and L. D. P. Agostinho},
journal = {SPE Latin American and Caribbean Petroleum Engineering Conference},
year = {2026},
doi = {10.2118/231662-ms},
url = {https://doi.org/10.2118/231662-ms}
}FAQ
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