FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4213309214> ?p ?o ?g. }

• W4213309214 endingPage "1830" @default.
• W4213309214 startingPage "1815" @default.
• W4213309214 abstract "Summary While deep learning has achieved great success in solving partial differential equations (PDEs) that accurately describe engineering systems, it remains a big challenge to obtain efficient and accurate solutions for complex problems instead of traditional numerical simulation. In the field of reservoir engineering, the current mainstream machine learning methods have been successfully applied. However, these popular methods cannot directly solve the problem of 2D two-phase oil/water PDEs well, which is the core of reservoir numerical simulation. Fourier neural operator (FNO) is a recently proposed high-efficiency PDE solution architecture that overcomes the shortcomings of the above popular methods, which can handle this type of PDE problem well in our work. In this paper, a deep-learning-based model is developed to solve three categories of problems controlled by the subsurface 2D oil/water two-phase flow PDE based on the FNO. For this complex engineering equation, we consider many factors, select characteristic variables, increase the dimension channel, expand the network structure, and realize the solution of the engineering problem. The first category is to predict the distribution of saturation and pressure fields by PDE parameters. The second category is the prediction of time series. The third category is for the inverse problem. It has achieved good results on both forward and inverse problems. The network uses fast Fourier transform (FFT) to extract PDE information in Fourier space to approximate differential operators, making the network faster and with greater physics significance. The model is mesh-independent and has good generalization, which also shows superresolution. Compared to the original FNO, we improve the network structure, add physical constraints to deal with boundary conditions (BCs), and use a shape matrix to control irregular boundaries. Also, we have improved the FFT module to make the transformation smoother. Compared with advanced deep learning-based solvers at different resolutions, the results show that this model overcomes some shortcomings of popular algorithms such as physics-informed neural networks (PINNs) and fully convolutional network (FCN) and has stronger accuracy and applicability. Our work has great potential in the replacement of traditional numerical methods with neural networks for reservoir numerical simulation." @default.
• W4213309214 created "2022-02-24" @default.
• W4213309214 creator A5002358967 @default.
• W4213309214 creator A5011159421 @default.
• W4213309214 creator A5021460583 @default.
• W4213309214 creator A5029779691 @default.
• W4213309214 creator A5035227128 @default.
• W4213309214 creator A5040469723 @default.
• W4213309214 creator A5057350479 @default.
• W4213309214 creator A5079367831 @default.
• W4213309214 creator A5080662057 @default.
• W4213309214 date "2022-02-17" @default.
• W4213309214 modified "2023-10-18" @default.
• W4213309214 title "Fourier Neural Operator for Solving Subsurface Oil/Water Two-Phase Flow Partial Differential Equation" @default.
• W4213309214 cites W2004124997 @default.
• W4213309214 cites W2009104157 @default.
• W4213309214 cites W2152657433 @default.
• W4213309214 cites W2766701559 @default.
• W4213309214 cites W2784733489 @default.
• W4213309214 cites W2899283552 @default.
• W4213309214 cites W2908541468 @default.
• W4213309214 cites W2912649832 @default.
• W4213309214 cites W2919115771 @default.
• W4213309214 cites W3006689658 @default.
• W4213309214 cites W3021801979 @default.
• W4213309214 cites W3045327624 @default.
• W4213309214 cites W3047245470 @default.
• W4213309214 cites W3081716925 @default.
• W4213309214 cites W3087889215 @default.
• W4213309214 cites W3092231855 @default.
• W4213309214 cites W3096261179 @default.
• W4213309214 cites W3098175809 @default.
• W4213309214 cites W3100968477 @default.
• W4213309214 cites W3114249691 @default.
• W4213309214 cites W3123691999 @default.
• W4213309214 cites W3123883114 @default.
• W4213309214 cites W3132621033 @default.
• W4213309214 cites W3157939602 @default.
• W4213309214 cites W3183684070 @default.
• W4213309214 cites W4252917338 @default.
• W4213309214 cites W4301455501 @default.
• W4213309214 doi "https://doi.org/10.2118/209223-pa" @default.
• W4213309214 hasPublicationYear "2022" @default.
• W4213309214 type Work @default.
• W4213309214 citedByCount "10" @default.
• W4213309214 countsByYear W42133092142022 @default.
• W4213309214 countsByYear W42133092142023 @default.
• W4213309214 crossrefType "journal-article" @default.
• W4213309214 hasAuthorship W4213309214A5002358967 @default.
• W4213309214 hasAuthorship W4213309214A5011159421 @default.
• W4213309214 hasAuthorship W4213309214A5021460583 @default.
• W4213309214 hasAuthorship W4213309214A5029779691 @default.
• W4213309214 hasAuthorship W4213309214A5035227128 @default.
• W4213309214 hasAuthorship W4213309214A5040469723 @default.
• W4213309214 hasAuthorship W4213309214A5057350479 @default.
• W4213309214 hasAuthorship W4213309214A5079367831 @default.
• W4213309214 hasAuthorship W4213309214A5080662057 @default.
• W4213309214 hasConcept C102519508 @default.
• W4213309214 hasConcept C104317684 @default.
• W4213309214 hasConcept C11413529 @default.
• W4213309214 hasConcept C126255220 @default.
• W4213309214 hasConcept C127413603 @default.
• W4213309214 hasConcept C134306372 @default.
• W4213309214 hasConcept C135252773 @default.
• W4213309214 hasConcept C154945302 @default.
• W4213309214 hasConcept C158448853 @default.
• W4213309214 hasConcept C17020691 @default.
• W4213309214 hasConcept C185592680 @default.
• W4213309214 hasConcept C207864730 @default.
• W4213309214 hasConcept C2778668878 @default.
• W4213309214 hasConcept C28826006 @default.
• W4213309214 hasConcept C33923547 @default.
• W4213309214 hasConcept C41008148 @default.
• W4213309214 hasConcept C50644808 @default.
• W4213309214 hasConcept C55493867 @default.
• W4213309214 hasConcept C78762247 @default.
• W4213309214 hasConcept C86339819 @default.
• W4213309214 hasConcept C93779851 @default.
• W4213309214 hasConceptScore W4213309214C102519508 @default.
• W4213309214 hasConceptScore W4213309214C104317684 @default.
• W4213309214 hasConceptScore W4213309214C11413529 @default.
• W4213309214 hasConceptScore W4213309214C126255220 @default.
• W4213309214 hasConceptScore W4213309214C127413603 @default.
• W4213309214 hasConceptScore W4213309214C134306372 @default.
• W4213309214 hasConceptScore W4213309214C135252773 @default.
• W4213309214 hasConceptScore W4213309214C154945302 @default.
• W4213309214 hasConceptScore W4213309214C158448853 @default.
• W4213309214 hasConceptScore W4213309214C17020691 @default.
• W4213309214 hasConceptScore W4213309214C185592680 @default.
• W4213309214 hasConceptScore W4213309214C207864730 @default.
• W4213309214 hasConceptScore W4213309214C2778668878 @default.
• W4213309214 hasConceptScore W4213309214C28826006 @default.
• W4213309214 hasConceptScore W4213309214C33923547 @default.
• W4213309214 hasConceptScore W4213309214C41008148 @default.
• W4213309214 hasConceptScore W4213309214C50644808 @default.
• W4213309214 hasConceptScore W4213309214C55493867 @default.
• W4213309214 hasConceptScore W4213309214C78762247 @default.
• W4213309214 hasConceptScore W4213309214C86339819 @default.

• next