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Recent advances and challenges of cement bond evaluation based on ultrasonic measurements in cased holes

Year: 2025

Type: article

Abstract: Cement bond quality evaluations are essential for assessing zonal isolation between formation strata, providing crucial information for ensuring environmental and ecological safety in oil and gas exploitation, geothermal energy injection and geological carbon dioxide sequestration. In the past decade, the ultrasonic pulse-echo and pitch-catch logging techniques have emerged as effective and non-destructive methods for quantitatively evaluating bond quality at both the casing-cement and cement-formation interfaces. This review presents a comprehensive overview of recent advancements in cement bond quality assessment based on ultrasonic measurements. Key developments include automatic waveform quality assessment, inversion techniques for mud and cement impedance, tool trajectory corrections, separation of flexural and extensional mode waves, machine learning-based extraction and enhancement of TIE waveforms, and imaging of the cement-formation interface using the reverse time migration approach. The review thoroughly explores the methodological principles and applications of these techniques, supported by synthetic datasets, full-scale physical well experiments, and field well data. Considering the recent progress in machine learning and the growing availability of advanced computational resources, we highlight the most significant achievements and ongoing challenges in data processing, while discussing the potential advancements these techniques could offer in the near future. • An overview of recent advancements in ultrasonic cement bond quality evaluation. • A unified framework for ultrasonic enhanced signal processing techniques in cased wells. • Solutions for tool eccentricity, lightweight cement discrimination and weak signal extraction. • Demonstrations from synthetic models to experimental well and field well deployments. • Implications for ML generalization and real-time diagnostics for well integrity assessment.

Source: Artificial Intelligence in Geosciences

DOI: 10.1016/j.aiig.2025.100170