Challenges and Solutions for Advanced Through-Tubing Cement Evaluation

Well integrity is a fundamental requirement for safe and efficient oil and gas operations. Cement evaluation, especially the quality of the cement bond between casing and formation, is a critical component of ensuring well stability, preventing fluid migration, and avoiding environmental contamination. Traditional cement evaluation methods rely on tools such as cement bond logs (CBLs) and ultrasonic techniques, but these require direct access to the casing surface, necessitating the removal of production tubing. This process is time consuming, costly, and logistically challenging. Through-tubing cement evaluation (TTCE) techniques address these issues by enabling cement bond assessment without pulling the tubing. However, TTCE faces two major challenges: significant signal attenuation due to multi-interface reflections and the adverse impact of tubing eccentricity (ECC), which distorts the signal. This publication presents a comprehensive solution integrating selective non-harmonic resonance (SNHR) and electromechanical impedance (EMI) technologies with a machine-learning-based, real-time ECC correction algorithm. SNHR enables continuous harmonic excitation to analyze resonance power loss, providing insights into cement bond quality. EMI uses piezoelectric transducers to measure admittance, offering high sensitivity to changes in the casing-cement bond. A machine-learning algorithm is then applied to correct for ECC effects, significantly improving the reliability of the bond index (BI). Finite element simulations and laboratory experiments validate the proposed techniques. Results demonstrate strong alignment between simulated and measured data, with clear differentiation between cemented and free pipe conditions. The combination of SNHR, EMI, and machine learning provides a robust, cost-effective solution for TTCE, ensuring accurate well integrity assessment in rigless operations.