Prediction of Minimum Horizontal Stress Using Machine Learning for Unconventional Reservoir Applications.
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| Title: | Prediction of Minimum Horizontal Stress Using Machine Learning for Unconventional Reservoir Applications. |
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| Authors: | Odekanle, Ebenezer Leke1 (AUTHOR), Olowookere, AbdulQoyum Adegoke1 (AUTHOR), Ajediti, Omolara Busayo1 (AUTHOR), Adeyanju, Bambo Ayo2 (AUTHOR), Ajagbe, Sunday Adeola3 (AUTHOR) saajagbe@pgschool.lautech.edu.ng, Adigun, Matthew O.4 (AUTHOR), Biswas, Arnab (AUTHOR) arnbiswas@wiley.com |
| Source: | International Journal of Chemical Engineering (1687806X). 5/18/2026, Vol. 2026, p1-15. 15p. |
| Subjects: | Machine learning, Strains & stresses (Mechanics), Ensemble learning, Shale, Shale gas reservoirs, Random forest algorithms, Hydraulic fracturing, Boosting algorithms |
| Geographic Terms: | Marcellus Shale |
| Abstract: | This study presents a leakage‐aware machine learning framework for predicting minimum horizontal stress (σhmin) using structured geomechanical and fracture‐related parameters. A dataset comprising 21,499 records from approximately 200 horizontal wells in the Marcellus Shale was preprocessed using a strictly leakage‐controlled pipeline, including feature refinement, outlier capping, and training‐fold–based transformations. Three ensemble models—random forest (RF), gradient boosting regressor (GBR), and extreme gradient boosting (XGBoost)—along with a stacking regressor and artificial neural network (ANN), were evaluated using root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The RF model achieved the best baseline performance (RMSE = 6.5846, R2 = 0.9988), while gradient boosting showed improved performance after tuning (RMSE ≈ 6.59). The stacking model delivered competitive results (RMSE = 7.4963), whereas the ANN showed lower performance (RMSE = 55.5008), indicating limited suitability for structured tabular data. A scenario‐based evaluation using Nigerian reservoir data demonstrated reasonable pr3332edictive consistency but was not treated as external validation due to data limitations. The results confirm that leakage‐aware ensemble learning provides a robust and physically consistent approach for predicting minimum horizontal stress, with potential application in reservoir characterization and hydraulic fracturing design. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Chemical Engineering (1687806X) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Abstract: | This study presents a leakage‐aware machine learning framework for predicting minimum horizontal stress (σhmin) using structured geomechanical and fracture‐related parameters. A dataset comprising 21,499 records from approximately 200 horizontal wells in the Marcellus Shale was preprocessed using a strictly leakage‐controlled pipeline, including feature refinement, outlier capping, and training‐fold–based transformations. Three ensemble models—random forest (RF), gradient boosting regressor (GBR), and extreme gradient boosting (XGBoost)—along with a stacking regressor and artificial neural network (ANN), were evaluated using root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The RF model achieved the best baseline performance (RMSE = 6.5846, R2 = 0.9988), while gradient boosting showed improved performance after tuning (RMSE ≈ 6.59). The stacking model delivered competitive results (RMSE = 7.4963), whereas the ANN showed lower performance (RMSE = 55.5008), indicating limited suitability for structured tabular data. A scenario‐based evaluation using Nigerian reservoir data demonstrated reasonable pr3332edictive consistency but was not treated as external validation due to data limitations. The results confirm that leakage‐aware ensemble learning provides a robust and physically consistent approach for predicting minimum horizontal stress, with potential application in reservoir characterization and hydraulic fracturing design. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 1687806X |
| DOI: | 10.1155/ijce/5409400 |
