Studies from Sahand University of Technology Yield New Data on Machine Learning (Low salinity water flooding: estimating relative permeability and capillary pre ssure using coupling of particle swarm optimization and machine learning techniq ue)
Studies from Sahand University of Technology Yield New Data on Machine Learning (Low salinity water flooding: estimating relative permeability and capillary pre ssure using coupling of particle swarm optimization and machine learning techniq ue)
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on artificial intelligenc e is the subject of a new report. According to news reporting originating from S ahand University of Technology by NewsRx correspondents, research stated, "The r eservoir's properties are required for proper reservoir simulation, which also i nvolves uncertainties. Experimental methods to estimate the relative permeabilit y and capillary pressure data are expensive and time-consuming." Our news reporters obtained a quote from the research from Sahand University of Technology: "This study aims to determine the relative permeability and capillar y pressure functions of a sandstone core in the presence and absence of clay dur ing low-salinity water floods. The data were provided by automatic history match ing the results from previously lab-reported studies through coupling a simulato r with the particle swarm optimization algorithm. Correlations were proposed usi ng multiple-linear regression for relative permeability and capillary pressure p arameters at low-salinity conditions. They were validated against experimental r esults of no clay and clayey formation with regression of 95% and 97%. To assign one curve of relative permeability and capillary pre ssure to the grid cells of the simulator, averaging techniques were implemented. The effect of salinity and clay content on the obtained curves was investigated . Changing salinity from 42000 to 4000 ppm, the reduction in water relative perm eability appeared to be higher than the oil relative permeability increment."
Key words
Sahand University of Technology/Cyborgs/Emerging Technologies/Machine Learning/Particle Swarm Optimization
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