Optimization Zonal Isolation: AI Featured Engineering Tool Predicted the Successful Results for Enhanced 7-In Liner Cement Job Before Execution

Achieving effective zonal isolation and long-term well integrity is a critical requirement in oil and gas wells. This paper presents a 7-inch liner cementing job where an artificial intelligence (AI)-driven modeling, supported by industry best practices for cementing operation, was integrated to optimize cementing execution and enhance cement bond quality. The objective was to validate the predicted theoretical top of cement and bonding quality through a cement bond long (CBL), demonstrating the reliability and accuracy of the AI-assisted cementing applications. The cementing job was executed in accordance with industry's best practices, incorporating a customized slurry design, optimized fluid displacement, and real-time monitoring to manage any operational deviations. A nearest-neighbor machine learning model was employed to analyze over 100,000 historical cementing jobs, scoring them based on similarity to the current job design. This approach identified the most relevant prior jobs—those with similar well parameters, fluid properties, job types, and operator objectives—allowing engineers to focus on proven best practices. The model also provided a performance benchmark, helping improve the score of the current job relative to historical outcomes and visualizing its position within a histogram of past job performance. Additionally, the AI was used to estimate cement coverage, placement efficiency, and predicted CBL results, enabling optimization of key parameters such as fluid compatibility, pumping rates, and pre-job planning. A post-job evaluation was conducted 72 hours later using CBL logging to validate zonal isolation and compare actual results against AI predictions, confirming the value of AI-assisted cementing optimization. The CBL response confirmed a high-quality cement bond, with no free-pipe indication across the section, validating the predicated AI cement placement up to the top of liner. The results minimized channeling risks and ensured reliable zonal isolation across the reservoir. The strong alignment between predicted and actual results demonstrated the applicability of AI modeling in cementing operation, reducing operational uncertainties and enhancing zonal isolation. This paper showcases the innovative integration of AI-driven predictive analytics with field-proven cementing practices to enhance job execution efficiency and cement bond reliability. By delivering accurate pre-job predictions and post-job validation, the AI model sets a new benchmark for optimizing cementing operations. The scalability of this approach allows for broader application across various well conditions, enabling operators to systematically enhance cementing performance while significantly reducing operational risks. The implementation of this AI-assisted methodology resulted in the best CBL integrity response the operator had observed in the field, demonstrating its potential to drive higher reliability and consistency in wellbore isolation. This marks a major advancement in the digital transformation of well construction, reinforcing the industry's shift toward data-driven, scalable cementing solutions.