ArticleScientific reports2026
A physics-informed hybrid ML framework for pore pressure and fracture gradient prediction in carbonate reservoirs.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Accurate prediction of formation pore pressure and fracture gradient is essential for safe mud-weight selection and wellbore stability, especially in heterogeneous offshore carbonate reservoirs. Classical empirical methods (Eaton, Miller, and Zhang) often degrade in such settings, while purely data-driven models can be hard to justify when calibration data are sparse. This study presents a physics-informed hybrid framework that integrates classical model outputs with a new Adaptive Calibration Layer (ACL) and an uncertainty quantification module (UQM) within a gradient-boosted learning architecture. The ACL learns depth-dependent corrections from sparse MDT/XPT points while enforcing physical smoothness. The framework was evaluated on six wells from an Iranian offshore carbonate gas field. After calibration, classical models achieved R
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