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Data-Driven Machine Learning Approach in Reservoir Parameter Prediction
  • Vivian O Oguadinma,
  • Thankgod Ujowundu,
  • Chibuzo V Ahaneku
Vivian O Oguadinma
Dievoc Integrated
Thankgod Ujowundu
Chibuzo V Ahaneku
University of Malta


In the realm of reservoir engineering, the application of machine learning has emerged as a transformative force, offering unprecedented insights into reservoir parameter characterization. In this study, we present a comprehensive analysis of four distinct machine learning models, namely Bagging, Extra Tree Regressor, XGBoost, and Ridge, to elucidate their efficacy in predicting permeability, a critical parameter for reservoir characterization. Our findings reveal an understanding of each model's performance. The Bagging model, while demonstrating an impressive trained accuracy of 0.99, exhibits some uncertainty in high permeability predictions, casting slight shadows on its applicability for reservoir characterization. In contrast, the Extra Tree Regressor model outshines the Bagging model with a trained accuracy of 100% and a prediction accuracy of 99.8%. It boasts lower absolute and absolute percentage errors, reinforcing its ability in permeability prediction. However, the XGBoost model takes a unique approach by emphasizing the density-corrected log over gamma-ray and sonic logs. Despite achieving remarkable trained and predicted data accuracy exceeding 99%, its reliance on the corrected density log introduces a mean absolute percentage error above 10, warranting closer scrutiny. In contrast, the Ridge model struggles, evident from its high AIC reading, signifying its limited compatibility with permeability prediction. Joint plots and LMplot analyses further showcases model behaviors. The Extra Tree model exhibits a 99% confidence interval, underscoring its reliability with minimal underpredictions. Conversely, the Bagging and Ridge models show susceptibility to high uncertainties in permeability predictions, particularly at extreme values. Our study concludes that the Extra Tree Regressor model excels in permeability prediction, with potential applications in reservoir interval assessments. The XGBoost model, while competent in sandstone reservoir prediction, bears a higher uncertainty burden. The Bagging and Ridge models, due to their uncertainty challenges, are less suitable for non-reservoir and sandstone reservoir interval predictions. High permeability correlations with elevated porosity, reduced water saturation, and lower gamma ray readings highlight the reservoir intervals' distinct characteristics. These observations underscore the reliability of our models and their potential contributions to reservoir engineering practices.
03 Nov 2023Submitted to Data Science and Machine Learning
03 Nov 2023Published in Data Science and Machine Learning