International Journal of Biological Macromolecules, Journal Year: 2024, Volume and Issue: unknown, P. 138974 - 138974
Published: Dec. 1, 2024
Language: Английский
International Journal of Biological Macromolecules, Journal Year: 2024, Volume and Issue: unknown, P. 138974 - 138974
Published: Dec. 1, 2024
Language: Английский
Published: April 21, 2025
Abstract Effective management of water-driven, Naturally Fractured Reservoirs (NFR) requires minimizing water production. This study compares two predictive approaches—statistical methods, specifically Response Surface Methodology (RSM), and artificial intelligence (AI)—to determine their effectiveness in predicting early breakthrough time (tbt) NFRs. A dataset comprising 261 simulation cases was generated based on design experiments approach, using dual-porosity dual-permeability models, varying key reservoir fracture parameters, including permeability, matrix spacing, storativity ratio, mobility production rate. log transformation applied to normalize the dataset, improving accuracy. The employed RSM for statistical modeling various AI-based approaches, Neural Networks, Optimizable Ensemble Trees, Gaussian Process Regression, trained 80% data tested remaining 20% evaluate performance. log-transformed significantly improved prediction accuracy, with best performing model being Trees a validation Root Mean Square Error (RMSE) 0.51631 Coefficient Determination (R²) 0.86, while its test RMSE 0.50674 an R² 0.90. In comparison, approach produced 0.9392, 0.9261, adjusted 0.8960, predicted 0.8175, Adequate Precision 26.9, indicating strong signal. While provided valuable interpretability parameter sensitivity insights, it struggled complex interactions exhibited higher variability predictions. AI models demonstrated superior capability capturing non-linear relationships between properties rates, making them more reliable practical applications. concludes that given sufficient data, offer robust scalable solution times. integration machine learning (ML) offers enhanced scalability, decision-making potential optimizing strategies fractured reservoirs.
Language: Английский
Citations
0Journal of Thermal Analysis and Calorimetry, Journal Year: 2024, Volume and Issue: 149(12), P. 5843 - 5869
Published: June 1, 2024
Language: Английский
Citations
3Food and Bioprocess Technology, Journal Year: 2024, Volume and Issue: unknown
Published: June 7, 2024
Language: Английский
Citations
3Food Bioscience, Journal Year: 2024, Volume and Issue: 61, P. 104790 - 104790
Published: July 23, 2024
Language: Английский
Citations
2Borneo Journal of Pharmacy, Journal Year: 2024, Volume and Issue: 7(1), P. 1 - 13
Published: Feb. 29, 2024
Candida albicans can cause two infections in humans: superficial and systemic. The ability of C. to infect the host is influenced by virulence factors character changes so that it fool immune system. From change factor, form a biofilm. This study aims determine good concentration inhibiting antifungal antibiofilm activity nanoemulsion mouthwash formulation bajakah tampala (Spatholobus littoralis Hassk) skin extract against albicans. research was conducted with an experimental method. used spontaneous magnetic stirrer technique make preparations. Antifungal tests were carried out dilution method using 96-well plate microplate reader wavelength 620 nm percentage inhibition MIC50 MBIC50. results showed S. inhibited planktonic biofilm 1% expressed as Therefore, could inhibit growth oral cavity.
Language: Английский
Citations
1Journal of Molecular Liquids, Journal Year: 2024, Volume and Issue: 412, P. 125875 - 125875
Published: Aug. 27, 2024
Language: Английский
Citations
1Journal of Controlled Release, Journal Year: 2024, Volume and Issue: 375, P. 552 - 573
Published: Sept. 21, 2024
Language: Английский
Citations
1Journal of Inorganic and Organometallic Polymers and Materials, Journal Year: 2024, Volume and Issue: unknown
Published: Nov. 15, 2024
Language: Английский
Citations
1Applied Physics A, Journal Year: 2024, Volume and Issue: 130(3)
Published: Feb. 24, 2024
Language: Английский
Citations
0Structural Chemistry, Journal Year: 2024, Volume and Issue: 35(6), P. 1925 - 1935
Published: June 28, 2024
Language: Английский
Citations
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