FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4379280087> ?p ?o ?g. }

• W4379280087 abstract "Abstract The Fast Marching Method (FMM) is a highly efficient numerical algorithm frequently used to solve the Eikonal equation to obtain the travel time from the source point to spatial locations, which can generate a geometric description of monotonically advancing front in anisotropic and heterogeneous media. In modeling fluid flow in subsurface heterogeneous porous media, application of the FMM makes the characterization of pressure front propagation quite straightforward using the diffusive time of flight (DTOF) as the Eikonal solution from an asymptotic approximation to the diffusivity equation. For the infinite-acting flow that occurs in smoothly varying heterogeneous media, travel time of pressure front from the active production or injection well to the observation well can be directly estimated from the DTOF using the concept of radius of investigation (ROI). Based on the ROI definition, the travel time to a given location in space can be determined from the maximum magnitude of partial derivative of pressure to time. Treating travel time computed at the observation well as the objective function, we propose a FMM based deep learning (DL) framework, namely the Inversion Neural Network (INN), to inversely estimate heterogeneous reservoir permeability fields through training the deep neural network (DNN) with the travel time data directly generated from the FMM. A convolutional neural network (CNN) is adopted to establish the mapping between the heterogeneous permeability field and the sparse observational data. Because of the quasi-linear relationship between the travel time and reservoir properties, CNN inspired by FMM is able to provide a rapid inverse estimate of heterogeneous reservoir properties that show sufficient accuracy compared to the true reference model with a limited number of observation wells. Inverse modeling results of the permeability fields are validated by the asymptotic pressure approximation through history matching of the reservoir models with the multi-well pressure transient data." @default.
• W4379280087 created "2023-06-05" @default.
• W4379280087 creator A5017471172 @default.
• W4379280087 creator A5080116144 @default.
• W4379280087 creator A5081032657 @default.
• W4379280087 creator A5092083008 @default.
• W4379280087 date "2023-06-05" @default.
• W4379280087 modified "2023-09-25" @default.
• W4379280087 title "Rapid Inference of Reservoir Permeability From Inversion of Travel Time Data Under a Fast Marching Method Based Deep Learning Framework" @default.
• W4379280087 cites W1485354198 @default.
• W4379280087 cites W1512403558 @default.
• W4379280087 cites W1901129140 @default.
• W4379280087 cites W1967304259 @default.
• W4379280087 cites W1973998773 @default.
• W4379280087 cites W1975025790 @default.
• W4379280087 cites W1980251225 @default.
• W4379280087 cites W1990466879 @default.
• W4379280087 cites W1999244633 @default.
• W4379280087 cites W2013190964 @default.
• W4379280087 cites W2018353504 @default.
• W4379280087 cites W2021318987 @default.
• W4379280087 cites W2025550225 @default.
• W4379280087 cites W2041091843 @default.
• W4379280087 cites W2054113582 @default.
• W4379280087 cites W2055341194 @default.
• W4379280087 cites W2057652881 @default.
• W4379280087 cites W2058069835 @default.
• W4379280087 cites W2110766101 @default.
• W4379280087 cites W2157918148 @default.
• W4379280087 cites W2164197089 @default.
• W4379280087 cites W2168959606 @default.
• W4379280087 cites W2169528473 @default.
• W4379280087 cites W2341780487 @default.
• W4379280087 cites W2345411445 @default.
• W4379280087 cites W2508022387 @default.
• W4379280087 cites W2559345005 @default.
• W4379280087 cites W2587120479 @default.
• W4379280087 cites W2587257166 @default.
• W4379280087 cites W2625995436 @default.
• W4379280087 cites W2760908924 @default.
• W4379280087 cites W2803629276 @default.
• W4379280087 cites W2899283552 @default.
• W4379280087 cites W2919115771 @default.
• W4379280087 cites W2950337752 @default.
• W4379280087 cites W2961132713 @default.
• W4379280087 cites W2963162215 @default.
• W4379280087 cites W3006689658 @default.
• W4379280087 cites W3014175830 @default.
• W4379280087 cites W3014468003 @default.
• W4379280087 cites W3021668893 @default.
• W4379280087 cites W3021923779 @default.
• W4379280087 cites W3082717723 @default.
• W4379280087 cites W3104994177 @default.
• W4379280087 cites W3106811345 @default.
• W4379280087 cites W3123551284 @default.
• W4379280087 cites W3126055445 @default.
• W4379280087 cites W3137389037 @default.
• W4379280087 cites W3158866033 @default.
• W4379280087 cites W3166538042 @default.
• W4379280087 cites W3179010545 @default.
• W4379280087 cites W3207319801 @default.
• W4379280087 cites W4280497428 @default.
• W4379280087 cites W4312403480 @default.
• W4379280087 doi "https://doi.org/10.2118/214385-ms" @default.
• W4379280087 hasPublicationYear "2023" @default.
• W4379280087 type Work @default.
• W4379280087 citedByCount "0" @default.
• W4379280087 crossrefType "proceedings-article" @default.
• W4379280087 hasAuthorship W4379280087A5017471172 @default.
• W4379280087 hasAuthorship W4379280087A5080116144 @default.
• W4379280087 hasAuthorship W4379280087A5081032657 @default.
• W4379280087 hasAuthorship W4379280087A5092083008 @default.
• W4379280087 hasConcept C105569014 @default.
• W4379280087 hasConcept C108583219 @default.
• W4379280087 hasConcept C11413529 @default.
• W4379280087 hasConcept C126255220 @default.
• W4379280087 hasConcept C127313418 @default.
• W4379280087 hasConcept C134306372 @default.
• W4379280087 hasConcept C135252773 @default.
• W4379280087 hasConcept C14641988 @default.
• W4379280087 hasConcept C154945302 @default.
• W4379280087 hasConcept C160920958 @default.
• W4379280087 hasConcept C17866373 @default.
• W4379280087 hasConcept C187320778 @default.
• W4379280087 hasConcept C33923547 @default.
• W4379280087 hasConcept C40375134 @default.
• W4379280087 hasConcept C41008148 @default.
• W4379280087 hasConcept C50644808 @default.
• W4379280087 hasConcept C6648577 @default.
• W4379280087 hasConcept C81363708 @default.
• W4379280087 hasConceptScore W4379280087C105569014 @default.
• W4379280087 hasConceptScore W4379280087C108583219 @default.
• W4379280087 hasConceptScore W4379280087C11413529 @default.
• W4379280087 hasConceptScore W4379280087C126255220 @default.
• W4379280087 hasConceptScore W4379280087C127313418 @default.
• W4379280087 hasConceptScore W4379280087C134306372 @default.
• W4379280087 hasConceptScore W4379280087C135252773 @default.
• W4379280087 hasConceptScore W4379280087C14641988 @default.
• W4379280087 hasConceptScore W4379280087C154945302 @default.
• W4379280087 hasConceptScore W4379280087C160920958 @default.

• next