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This is a Literature Review study.

August 16, 2025

Prediction of Sanding Potential in Unconsolidated Sandstones Using Selected Machine-Learning Techniques

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Authors

CNChiamaka R. NwaruezeUniversity of Ibadan SIS. O. IsehunwaUniversity of Ibadan

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Implication

Machine learning models demonstrate high accuracy in predicting sand production rates in unconsolidated sandstones, suggesting improvements for oil and gas extraction practices.

Key Points

Lasso Regression successfully predicts sand production with an R2 score of 0.977 and RMSE of 133.45, indicating strong accuracy.
Advanced machine learning techniques offer a proactive alternative to empirical models for predicting sand production in the oil industry.
The study utilized a dataset derived from Triassic sandstones, focusing on key parameters like compressional wave transit time and pore pressure.
Adopting data-driven methodologies can enhance sand control strategies and operational efficiency in oil and gas extraction.

Cite This Study

Nwarueze et al. (2025) studied this question.

synapsesocial.com/papers/68a368780a429f797332d3ea https://doi.org/10.2118/228688-ms

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Implication

Machine learning models demonstrate high accuracy in predicting sand production rates in unconsolidated sandstones, suggesting improvements for oil and gas extraction practices.

Prediction of Sanding Potential in Unconsolidated Sandstones Using Selected Machine-Learning Techniques

Discussion

Member takes

Implication

Key Points

Cite This Study

Prediction of Sanding Potential in Unconsolidated Sandstones Using Selected Machine-Learning Techniques

Discussion

Member takes

Implication

Key Points

Cite This Study

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