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January 1, 2026International Journal of Oil Gas and Coal Technology

Predicting well cement integrity using open-hole logging and borehole survey data: a novel machine learning approach

Amal Nabillah Abdul Aziz Stefan Gödeke Mohamad Azwan Yakup Pg Emeroylariffion Abas

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Key Points

To predict the integrity of well cement utilizing open-hole logging and borehole survey data through machine learning techniques.
Implemented a machine learning algorithm to analyze data from open-hole logging and borehole surveys.
Collected and processed input features relevant to cement integrity.
Evaluated model performance against traditional prediction methods.
Achieved higher prediction accuracy compared to conventional methods.
Identified key variables impacting well cement integrity.
Demonstrated the feasibility of using machine learning for real-time assessments.

Implication

Demonstrates a novel machine learning approach to predict well cement integrity, indicating its potential in enhancing borehole assessments.

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Cite This Study

Aziz et al. (2026) studied this question.

synapsesocial.com/papers/69d5f05d74eaea4b11a79bf9 https://doi.org/10.1504/ijogct.2026.10077489

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