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September 10, 2025

Predictive Analytics for Sand Production: a Machine Learning Study from the Niger Delta

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Machine learning methods develop predictive models for sand production in Niger Delta, indicating effective management strategies.

The predictive model achieves an R2 score of 0.9547, demonstrating high accuracy in forecasting sand production trends.
Key predictors include flow bottom-hole pressure, gas-oil ratio, and permeability, significantly affecting sand production.
Random Forest and XGBoost outperformed other models, effectively handling the nonlinear relationships in sand production data.
This framework enhances operational decision-making and minimizes risks in petroleum production by optimizing sand management strategies.

Omoare et al. (2025) studied this question.

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