Matches in SemOpenAlex for { <https://semopenalex.org/work/W3207047754> ?p ?o ?g. }
Showing items 1 to 100 of
100
with 100 items per page.
- W3207047754 abstract "Abstract Detailed geological description of fractured reservoirs is typically characterized by the discrete-fracture model (DFM), in which the rock matrix and fractures are explicitly represented in the form of unstructured grids. Its high computation cost makes it infeasible for field-scale applications. Traditional flow-based and static-based methods used to upscale detailed geological DFM to reservoir simulation model suffer from, to some extent, high computation cost and low accuracy, respectively. In this paper, we present a novel deep learning-based upscaling method as an alternative to traditional methods. This work aims to build an image-to-value model based on convolutional neural network to model the nonlinear mapping between the high-resolution image of detailed DFM as input and the upscaled reservoir simulation model as output. The reservoir simulation model (herein refers to the dual-porosity model) includes the predicted fracture-fracture transmissibility linking two adjacent grid blocks and fracture-matrix transmissibility within each coarse block. The proposed upscaling workflow comprises the train-validation samples generation, convolutional neural network training-validating process, and model evaluation. We apply a two-point flux approximation (TPFA) scheme based on embedded discrete-fracture model (EDFM) to generate the datasets. We perform trial-error analysis on the coupling training-validating process to update the ratio of train-validation samples, optimize the learning rate and the network architecture. This process is applied until the trained model obtains an accuracy above 90 % for both train-validation samples. We then demonstrate its performance with the two-phase reference solutions obtained from the fine model in terms of water saturation profile and oil recovery versus PVI. Results show that the DL-based approach provides a good match with the reference solutions for both water saturation distribution and oil recovery curve. This work manifests the value of the DL-based method for the upscaling of detailed DFM to the dual-porosity model and can be extended to construct generalized dual-porosity, dual-permeability models or include more complex physics, such as capillary and gravity effects." @default.
- W3207047754 created "2021-10-25" @default.
- W3207047754 creator A5009325358 @default.
- W3207047754 creator A5059613649 @default.
- W3207047754 creator A5067974945 @default.
- W3207047754 creator A5072179993 @default.
- W3207047754 creator A5084996089 @default.
- W3207047754 date "2021-10-19" @default.
- W3207047754 modified "2023-09-24" @default.
- W3207047754 title "Constructing Dual-Porosity Models from High-Resolution Discrete-Fracture Models Using Deep Neural Networks" @default.
- W3207047754 cites W1604238058 @default.
- W3207047754 cites W1980972426 @default.
- W3207047754 cites W1997174216 @default.
- W3207047754 cites W2017057050 @default.
- W3207047754 cites W2021011596 @default.
- W3207047754 cites W2046435379 @default.
- W3207047754 cites W2048784891 @default.
- W3207047754 cites W2057807154 @default.
- W3207047754 cites W2062684873 @default.
- W3207047754 cites W2069062027 @default.
- W3207047754 cites W2083501502 @default.
- W3207047754 cites W2131343962 @default.
- W3207047754 cites W2133059825 @default.
- W3207047754 cites W2153401515 @default.
- W3207047754 cites W2471902419 @default.
- W3207047754 cites W2500202796 @default.
- W3207047754 cites W2888436193 @default.
- W3207047754 cites W2921355212 @default.
- W3207047754 cites W2963157298 @default.
- W3207047754 cites W2973481132 @default.
- W3207047754 cites W2986037633 @default.
- W3207047754 cites W3000194204 @default.
- W3207047754 doi "https://doi.org/10.2118/203901-ms" @default.
- W3207047754 hasPublicationYear "2021" @default.
- W3207047754 type Work @default.
- W3207047754 sameAs 3207047754 @default.
- W3207047754 citedByCount "7" @default.
- W3207047754 countsByYear W32070477542022 @default.
- W3207047754 countsByYear W32070477542023 @default.
- W3207047754 crossrefType "proceedings-article" @default.
- W3207047754 hasAuthorship W3207047754A5009325358 @default.
- W3207047754 hasAuthorship W3207047754A5059613649 @default.
- W3207047754 hasAuthorship W3207047754A5067974945 @default.
- W3207047754 hasAuthorship W3207047754A5072179993 @default.
- W3207047754 hasAuthorship W3207047754A5084996089 @default.
- W3207047754 hasConcept C108583219 @default.
- W3207047754 hasConcept C11413529 @default.
- W3207047754 hasConcept C127313418 @default.
- W3207047754 hasConcept C127413603 @default.
- W3207047754 hasConcept C154945302 @default.
- W3207047754 hasConcept C187320778 @default.
- W3207047754 hasConcept C2524010 @default.
- W3207047754 hasConcept C2777210771 @default.
- W3207047754 hasConcept C2778668878 @default.
- W3207047754 hasConcept C33923547 @default.
- W3207047754 hasConcept C41008148 @default.
- W3207047754 hasConcept C43369102 @default.
- W3207047754 hasConcept C45374587 @default.
- W3207047754 hasConcept C459310 @default.
- W3207047754 hasConcept C50644808 @default.
- W3207047754 hasConcept C62064638 @default.
- W3207047754 hasConcept C78519656 @default.
- W3207047754 hasConcept C78762247 @default.
- W3207047754 hasConcept C81363708 @default.
- W3207047754 hasConceptScore W3207047754C108583219 @default.
- W3207047754 hasConceptScore W3207047754C11413529 @default.
- W3207047754 hasConceptScore W3207047754C127313418 @default.
- W3207047754 hasConceptScore W3207047754C127413603 @default.
- W3207047754 hasConceptScore W3207047754C154945302 @default.
- W3207047754 hasConceptScore W3207047754C187320778 @default.
- W3207047754 hasConceptScore W3207047754C2524010 @default.
- W3207047754 hasConceptScore W3207047754C2777210771 @default.
- W3207047754 hasConceptScore W3207047754C2778668878 @default.
- W3207047754 hasConceptScore W3207047754C33923547 @default.
- W3207047754 hasConceptScore W3207047754C41008148 @default.
- W3207047754 hasConceptScore W3207047754C43369102 @default.
- W3207047754 hasConceptScore W3207047754C45374587 @default.
- W3207047754 hasConceptScore W3207047754C459310 @default.
- W3207047754 hasConceptScore W3207047754C50644808 @default.
- W3207047754 hasConceptScore W3207047754C62064638 @default.
- W3207047754 hasConceptScore W3207047754C78519656 @default.
- W3207047754 hasConceptScore W3207047754C78762247 @default.
- W3207047754 hasConceptScore W3207047754C81363708 @default.
- W3207047754 hasLocation W32070477541 @default.
- W3207047754 hasOpenAccess W3207047754 @default.
- W3207047754 hasPrimaryLocation W32070477541 @default.
- W3207047754 hasRelatedWork W2731899572 @default.
- W3207047754 hasRelatedWork W2763109982 @default.
- W3207047754 hasRelatedWork W2999805992 @default.
- W3207047754 hasRelatedWork W3116150086 @default.
- W3207047754 hasRelatedWork W3133861977 @default.
- W3207047754 hasRelatedWork W3166467183 @default.
- W3207047754 hasRelatedWork W3192840557 @default.
- W3207047754 hasRelatedWork W4200173597 @default.
- W3207047754 hasRelatedWork W4220996320 @default.
- W3207047754 hasRelatedWork W4312417841 @default.
- W3207047754 isParatext "false" @default.
- W3207047754 isRetracted "false" @default.
- W3207047754 magId "3207047754" @default.
- W3207047754 workType "article" @default.