Explainable artificial intelligence models for estimating the heat capacity of deep eutectic solvents DOI
Saad Alatefi, Okorie E. Agwu,

Menad Nait Amar

et al.

Fuel, Journal Year: 2025, Volume and Issue: 394, P. 135073 - 135073

Published: March 18, 2025

Language: Английский

A novel hybrid finite-infinite diffusion model for determining CO2 diffusion coefficient in oil-saturated porous media: Applications for enhanced oil recovery and geological carbon storage DOI
Mingyang Yang, Shijun Huang,

Fenglan Zhao

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 134621 - 134621

Published: Jan. 1, 2025

Language: Английский

Citations

0
Compositional modeling of solution gas–oil ratio (Rs): a comparative study of tree-based models, neural networks, and equations of state DOI Creative Commons
Aydin Larestani,

Sara Sahebalzamani,

Abdolhossein Hemmati‐Sarapardeh

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 11, 2025

Accurate knowledge of crude oil pressure–volume–temperature (PVT) properties is essential for both industrial and academic applications. However, traditional experimental methods determining these properties, particularly the solution gas–oil ratio (Rs), are time-intensive costly. In this study, advanced compositional models were developed using a broad range machine learning (ML) techniques to predict Rs efficiently reliably. A comprehensive database 1,154 data points was utilized modeling. Among tested models, extra trees (ET) algorithm demonstrated superior performance, achieving an average absolute percent relative error (AAPRE) approximately 3%, indicating its high reliability prediction. Additionally, estimated seven different equations state (EoS). Systematic graphical statistical evaluations revealed that Schmidt-Wenzel (SW) EoS most accurate among conventional methods, with 11%. The robustness ET validated across various temperature ranges, detailed trend analysis confirming their ability accurately capture physical relationship between pressure. relevancy factor quantified influence each input parameter on model outputs, whereas Leverage technique identified outliers defined ranges optimal performance. While ML achieved predictive reliability, computational demands complexity may limit deployment in resource-constrained environments decision-critical Nevertheless, study represents significant advancement modeling, providing robust, scalable, cost-effective tools

Language: Английский

Citations

0
Calculation of hydrogen dispersion in cushion gases using machine learning DOI Creative Commons
Ali Akbari,

Mehdi Maleki,

Yousef Kazemzadeh

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 21, 2025

Hydrogen storage is a crucial technology for ensuring sustainable energy transition. Underground Storage (UHS) in depleted hydrocarbon reservoirs, aquifers, and salt caverns provides viable large-scale solution. However, hydrogen dispersion cushion gases such as nitrogen (N2), methane (CH4), carbon dioxide (CO2) lead to contamination, reduced purity, increased purification costs. Existing experimental numerical methods predicting coefficients (KL) are often limited by high costs, lengthy processing times, insufficient accuracy dynamic reservoir conditions. This study addresses these challenges integrating data with advanced machine learning (ML) techniques model dispersion. Various ML models-including Random Forest (RF), Least Squares Boosting (LSBoost), Bayesian Regression, Linear Regression (LR), Artificial Neural Networks (ANNs), Support Vector Machines (SVMs)-were employed quantify KL function of pressure (P) displacement velocity (Um). Among methods, RF outperformed the others, achieving an R2 0.9965 test 0.9999 training data, RMSE values 0.023 0.001, respectively. The findings highlight potential ML-driven approaches optimizing UHS operations enhancing predictive accuracy, reducing computational mitigating contamination risks.

Language: Английский

Citations

0
Enhancing carbonated water efficiency in the fluid and rock surface activity and swelling behavior of crude oil using an anionic CO2-philic surfactant DOI Creative Commons
Iman Nowrouzi, Amir H. Mohammadi, Abbas Khaksar Manshad

et al.

Fuel, Journal Year: 2025, Volume and Issue: 398, P. 135475 - 135475

Published: May 2, 2025

Language: Английский

Citations

0
Performance Assessment of Solvent-Assisted Low-Salinity Waterflooding in Cyclic Injection Mode for Alaska Heavy Oil Recovery DOI Creative Commons
Temitope Ogunkunle, Hyun Woong Jang, Asad Syed

et al.

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 103948 - 103948

Published: Jan. 1, 2025

Language: Английский

Citations

0
Modelling the flowing bottom hole pressure of oil and gas wells using multivariate adaptive regression splines DOI Creative Commons
Okorie E. Agwu, Saad Alatefi,

Ahmad Alkouh

et al.

Journal of Petroleum Exploration and Production Technology, Journal Year: 2025, Volume and Issue: 15(2)

Published: Feb. 1, 2025

Language: Английский

Citations

0
Explainable artificial intelligence models for estimating the heat capacity of deep eutectic solvents DOI
Saad Alatefi, Okorie E. Agwu,

Menad Nait Amar

et al.

Fuel, Journal Year: 2025, Volume and Issue: 394, P. 135073 - 135073

Published: March 18, 2025

Language: Английский

Citations

0