Efficiency of salinity-responsive ZnO/PEG nanocomposite on the immiscible fluid-fluid displacement and interface behavior in different formations

Journal of Molecular Liquids, Journal Year: 2025, Volume and Issue: 428, P. 127462 - 127462

Published: April 10, 2025

Language: Английский

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Machine learning models for estimating the overall oil recovery of waterflooding operations in heterogenous reservoirs

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

Published: April 26, 2025

Abstract Waterflooding is the most widely used improved oil recovery technique. Predicting overall resulting from waterflooding in reservoirs crucial for effective reservoir management and appropriate decision-making. Machine learning (ML) techniques present resourceful fast-track tools, aiding predicting recovery, which time-consuming costly to accomplish by simulation studies. In this paper, four machine models: artificial neural network (ANN), Random Forest (RF), K-Nearest Neighbor (K-NN), Support Vector (SVM) are applied estimate (R) of water flooding. Initially, statistical methods were employed analyze input data before applying techniques. These models take into consideration mobility ratio (M), permeability variation (V), water-oil production (WOR), initial saturation (S Wi ). 1054 datasets utilized develop machine-learning models. ANN-based correlation was developed waterflooding. The ANN proposed model achieves a high coefficient determination (R 2 ) 0.999 low root-mean-square error (RMSE) 0.0063 on validation dataset. On other hand, like RF, K-NN, SVM achieve accurate estimation (R), where coefficients values 0.97, 0.95, 0.80 RMSE scores 0.0282, 0.0405, 0.0629 dataset, respectively. innovative application such ML demonstrates significant improvements prediction accuracy reliability, offering robust solution optimizing processes. provide industry research with efficient economical tools accurately estimating operations within heterogeneous reservoirs.

Language: Английский

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Enhanced oil recovery using chemical and nanoparticles for heavy oil sandstone reservoirs: Chemical vs nanofluid flooding

Journal of Molecular Liquids, Journal Year: 2025, Volume and Issue: unknown, P. 127707 - 127707

Published: May 1, 2025

Language: Английский

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