ArticleScientific reports2025
Application of supervised machine learning and unsupervised data compression models for pore pressure prediction employing drilling, petrophysical, and well log data.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Review
- Enhancing wellbore stability through machine learning for sustainable hydrocarbon exploitation.Scientific reports · 2025Article
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Authors and funding
6 authors.
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Abstract
Accurate determination of pore pressure is critical in the design of wells, determining a safe range of mud properties, and estimating the required mud weight to ensure wellbore stability. Conventional techniques for forecasting pore pressure, such as the Eaton, Bower, or compressibility methods, have certain constraints. These methods depend on empirical relationships and constants that can differ between basins. This study proposes an effective data-driven approach that utilizes machine learning algorithms to forecast reservoir pore pressure. A total of five machine learning algorithms, namely multivariable regression (MVR), polynomial regression (PR), random forest (RF), CatBoost regression, and multilayer perception (MLP), are applied in this research. Hybrid stacking modeling is employed for the first time to forecast pore pressure and to improve the accuracy and robustness of the results by combining different methodologies. Principal component analysis is also utilized (PCA) to extract features, hence expediting the entire process by reducing dimensionality. To accomplish the objectives, 1811 recordings are selected from the Volve Field, situated approximately 200 km west of Stavanger, Norway. These recordings encompass depth data; well logs, including NPHI, GR, DT, RD, RHOB, RS, and RT; drilling activities, specifically ROP; and petrophysical parameters, including BVW, K, PHIF, SW, and VCL. Pore pressure is used as the output level to generate data-driven models. 70% of the dataset is used for training the machine learning models, while the remaining 30% is reserved for testing the models to evaluate their performance and generalization capability. Data standardization is conducted to ensure that the utilized data is statistically well-distributed, devoid of measurement mistakes, and impervious to instrumental noise. Regression metrics, such as mean MAE, R
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