@article{Alsubaih2026, 
author = {Ahmed Alsubaih and Evan Wetmore and Watheq J. Al-Mudhafar and Kamy Sepehrnoori and Alberto L. Manriquez and Mojdeh Delshad},
title = {Physics-informed machine learning for sustained casing pressure diagnostics and root cause analysis for well integrity},
year = {2026},
journal = {Petroleum Science},
volume = {23},
number = {9},
pages = {5636-5647},
keywords = {Sustained casing pressure (SCP), Well integrity, Physics-informed informed machine learning (PIML), Annular pressure, SCADA monitoring},
url = {https://www.sciopen.com/article/10.1016/j.petsci.2026.03.049},
doi = {10.1016/j.petsci.2026.03.049},
abstract = {Sustained casing pressure (SCP) is a primary indicator of well integrity degradation, arising from compromised barriers such as cement, casing, tubing/packer components, or wellhead seals. Traditional SCP diagnostics—based on manual interpretation of annular pressure trends, bleed-off tests, and operational records—are often time-consuming, analyst-dependent, and difficult to scale across large well populations. This study presents a physics-informed machine learning (PIML) framework that integrates engineering-based physical principles, including fluid compressibility, thermal expansion, and leak-path mechanics, into a machine learning workflow for automated and scalable SCP classification. The framework is applied to field monitoring data from 26 wells, with detailed multi-annulus case studies for wells A5, A8, and A12. The method classifies cycle-level SCP behavior into six diagnostic types—no pressure, thermal pressure, trapped pressure, recharge pressure, constant pressure, and high-rate recharge—and maintains physically plausible and interpretable outputs under noisy or complex pressure signatures. Operationally, the framework supports real-time, SCADA-integrated surveillance to enable early detection of integrity threats, prioritization of higher-risk wells, and proactive intervention planning. The novelty of this work lies in embedding physical constraints directly into the classification logic, producing a robust and interpretable diagnostic capability that advances SCP analysis from a reactive, manual task toward automated well-integrity surveillance applicable to offshore, HPHT, CO2 sequestration, and hydrogen storage operations.}
}