Flow properties for a variety of porous media can be predicted using pore-scale modelling with geologically realistic networks. A state of the art pore-scale network model is developed for this purpose. It combines topologically disordered networks that represent real systems with detailed displacement mechanisms for any sequence of water and oil flooding, and any wettability. The model is verified by successfully predicting experimental relative permeability from a well described water-wet Berea sandstone. More complex experimental data are subsequently investigated. By developing a methodology combining realistic network topology with network properties tuned to easily obtainable experimental data such as mercury injection capillary pressures, the flow properties for several porous media can successfully be predicted. This is initially applied to a water-wet sand pack. The aim of this work is not simply to match experiments, but to use easily acquired data to predict difficult-to-measure properties. Furthermore, the variation of these properties in the field, due to wettability trends and different pore structures, can now be predicted reliably.
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Flow properties for a variety of porous media can be predicted using pore-scale modelling with geologically realistic networks. A state of the art pore-scale network model is developed for this purpose. It combines topologically disordered networks that represent real systems with detailed displacement mechanisms for any sequence of water and oil flooding, and any wettability. The model is verified by successfully predicting experimental relative permeability from a well described water-wet Berea sandstone. More complex experimental data are subsequently investigated. By developing a methodology combining realistic network topology with network properties tuned to easily obtainable experimental data such as mercury injection capillary pressures, the flow properties for several porous media can successfully be predicted. This is initially applied to a water-wet sand pack. The aim of this work is not simply to match experiments, but to use easily acquired data to predict difficult-to-measure properties. Furthermore, the variation of these properties in the field, due to wettability trends and different pore structures, can now be predicted reliably.
Per Valvatne has authored and co-authored a number of articles within the fields of pore-scale modelling and reservoir simulation. He has a doctorate from Imperial College London as well as an MS degree from Stanford University. He is currently working for Shell International, developing reservoir simulation tools for the oil industry.
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Kartoniert / Broschiert. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Valvatne PerPer Valvatne has authored and co-authored a number of articlesnwithin the fields of pore-scale modelling and reservoirnsimulation. He has a doctorate from Imperial College London asnwell as an MS degree from Stanford Univ. N° de réf. du vendeur 4953043
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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Flow properties for a variety of porous media canbe predicted using pore-scale modelling withgeologically realistic networks. A state of the artpore-scale network model is developed for thispurpose. It combines topologically disorderednetworks that represent real systems with detaileddisplacement mechanisms for any sequence of water andoil flooding, and any wettability. The model isverified by successfully predicting experimentalrelative permeability from a well described water-wetBerea sandstone. More complex experimental data aresubsequently investigated. By developing amethodology combining realistic network topology withnetwork properties tuned to easily obtainableexperimental data such as mercury injection capillarypressures, the flow properties for several porousmedia can successfully be predicted. This isinitially applied to a water-wet sand pack. The aimof this work is not simply to match experiments, butto use easily acquired data to predictdifficult-to-measure properties. Furthermore, thevariation of these properties in the field, due towettability trends and different pore structures, cannow be predicted reliably. N° de réf. du vendeur 9783639053289
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