Robust ensemble learning frameworks for predicting minimum miscibility pressure in pure nitrogen and gas mixtures containing nitrogen–crude oil systems: Insights from explainable artificial intelligence DOI

Menad Nait Amar,

Noureddine Zeraibi,

Fahd Mohamad Alqahtani

et al.

The Canadian Journal of Chemical Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: May 4, 2025

Abstract Miscible gas injection techniques, such as nitrogen injection, are among the attractive enhanced oil recovery (EOR) techniques for improving factors in reservoirs. A key challenge implementing these is accurately determining minimum miscibility pressure (MMP). While laboratory experiments offer reliable results, they costly and time‐consuming, existing empirical correlations often have moderate accuracy, which limits their practical use. In this study, robust ensemble methods, namely light gradient boosting machine (LightGBM), extra trees (ET), categorical (CatBoost), were implemented modelling MMP pure mixtures containing nitrogen–crude systems. An extensive experimental database involving 164 data points was used to elaborate on predictive models. The findings revealed that proposed methods achieved outstanding accuracy training test datasets, with ET consistently outperforming other model provided most consistent predictions a total root mean square error (RMSE) of only 0.3197 MPa determination coefficient 0.9976. Additionally, exhibited very small RMSE values across broad range operational conditions. Furthermore, Shapley additive explanations (SHAP) method further validated interpretability model, allowing clear insights into impact input features. This study underlines significant potential learning enhance prediction systems, thereby aiding appropriate design kind EOR process supporting better decision‐making reservoir management.

Language: Английский

Predictive Modeling of Co2 and Methane Adsorption in Tight Reservoirs Using Machine Learning Techniques DOI

Mehdi Maleki,

Mohammad Rasool Dehghani,

Moein Kafi

et al.

Published: Jan. 1, 2025

Language: Английский

Citations

0
Robust ensemble learning frameworks for predicting minimum miscibility pressure in pure nitrogen and gas mixtures containing nitrogen–crude oil systems: Insights from explainable artificial intelligence DOI

Menad Nait Amar,

Noureddine Zeraibi,

Fahd Mohamad Alqahtani

et al.

The Canadian Journal of Chemical Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: May 4, 2025

Abstract Miscible gas injection techniques, such as nitrogen injection, are among the attractive enhanced oil recovery (EOR) techniques for improving factors in reservoirs. A key challenge implementing these is accurately determining minimum miscibility pressure (MMP). While laboratory experiments offer reliable results, they costly and time‐consuming, existing empirical correlations often have moderate accuracy, which limits their practical use. In this study, robust ensemble methods, namely light gradient boosting machine (LightGBM), extra trees (ET), categorical (CatBoost), were implemented modelling MMP pure mixtures containing nitrogen–crude systems. An extensive experimental database involving 164 data points was used to elaborate on predictive models. The findings revealed that proposed methods achieved outstanding accuracy training test datasets, with ET consistently outperforming other model provided most consistent predictions a total root mean square error (RMSE) of only 0.3197 MPa determination coefficient 0.9976. Additionally, exhibited very small RMSE values across broad range operational conditions. Furthermore, Shapley additive explanations (SHAP) method further validated interpretability model, allowing clear insights into impact input features. This study underlines significant potential learning enhance prediction systems, thereby aiding appropriate design kind EOR process supporting better decision‐making reservoir management.

Language: Английский

Citations

0