FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4281251367> ?p ?o ?g. }

• W4281251367 endingPage "102439" @default.
• W4281251367 startingPage "102439" @default.
• W4281251367 abstract "Accurate prediction of geothermal reservoir responses to alternative energy production scenarios is critical for optimizing the development of the underlying resources. While the conventional physics-based models offer a comprehensive prediction tool, data-driven models provide an efficient alternative to build fit-for-purpose predictive models by extracting and using the statistical patterns in the collected data to make predictions. The recurrent neural network (RNN) is a data-driven model that is commonly applied to predict time series sequences. This paper presents a variant of RNN that also utilizes the efficiency of convolutional neural networks (CNN) for the prediction of energy production from geothermal reservoirs. Specifically, a CNN–RNN architecture is developed that takes historical well controls as input (features) and their corresponding production response data as output (labels) to learn an input-output mapping that can predict the future well production responses/performance for any given future well control inputs. The model is paired with a labeling scheme to handle real field disturbances that create data gaps. In addition to the model structure, we introduce a thorough workflow for applying the model, which includes data pre-processing, feature selection, as well as different training strategies for short-term and long-term prediction. The performance and accuracy of the model are evaluated by applying it to multiple datasets, including a field reservoir model." @default.
• W4281251367 created "2022-05-23" @default.
• W4281251367 creator A5003736943 @default.
• W4281251367 creator A5005251842 @default.
• W4281251367 creator A5059919900 @default.
• W4281251367 creator A5068668053 @default.
• W4281251367 creator A5070014324 @default.
• W4281251367 date "2022-09-01" @default.
• W4281251367 modified "2023-10-17" @default.
• W4281251367 title "Recurrent neural networks for short-term and long-term prediction of geothermal reservoirs" @default.
• W4281251367 cites W1980283418 @default.
• W4281251367 cites W1982029046 @default.
• W4281251367 cites W1992548144 @default.
• W4281251367 cites W2010093225 @default.
• W4281251367 cites W2034936124 @default.
• W4281251367 cites W2056897883 @default.
• W4281251367 cites W2062118960 @default.
• W4281251367 cites W2064675550 @default.
• W4281251367 cites W2075943016 @default.
• W4281251367 cites W2095454627 @default.
• W4281251367 cites W2107878631 @default.
• W4281251367 cites W2111172465 @default.
• W4281251367 cites W2134379449 @default.
• W4281251367 cites W2145781284 @default.
• W4281251367 cites W2168166553 @default.
• W4281251367 cites W2516310076 @default.
• W4281251367 cites W2604847698 @default.
• W4281251367 cites W2724452338 @default.
• W4281251367 cites W2747599906 @default.
• W4281251367 cites W2762902720 @default.
• W4281251367 cites W2777052342 @default.
• W4281251367 cites W2991100041 @default.
• W4281251367 cites W2991137082 @default.
• W4281251367 cites W3015568951 @default.
• W4281251367 cites W3016208458 @default.
• W4281251367 cites W3022787740 @default.
• W4281251367 cites W3036139134 @default.
• W4281251367 cites W3102778529 @default.
• W4281251367 cites W3123529861 @default.
• W4281251367 cites W3124685445 @default.
• W4281251367 cites W3160821982 @default.
• W4281251367 cites W3207102426 @default.
• W4281251367 cites W4205164920 @default.
• W4281251367 doi "https://doi.org/10.1016/j.geothermics.2022.102439" @default.
• W4281251367 hasPublicationYear "2022" @default.
• W4281251367 type Work @default.
• W4281251367 citedByCount "8" @default.
• W4281251367 countsByYear W42812513672022 @default.
• W4281251367 countsByYear W42812513672023 @default.
• W4281251367 crossrefType "journal-article" @default.
• W4281251367 hasAuthorship W4281251367A5003736943 @default.
• W4281251367 hasAuthorship W4281251367A5005251842 @default.
• W4281251367 hasAuthorship W4281251367A5059919900 @default.
• W4281251367 hasAuthorship W4281251367A5068668053 @default.
• W4281251367 hasAuthorship W4281251367A5070014324 @default.
• W4281251367 hasConcept C111766609 @default.
• W4281251367 hasConcept C119857082 @default.
• W4281251367 hasConcept C121332964 @default.
• W4281251367 hasConcept C124101348 @default.
• W4281251367 hasConcept C127313418 @default.
• W4281251367 hasConcept C139719470 @default.
• W4281251367 hasConcept C147168706 @default.
• W4281251367 hasConcept C154945302 @default.
• W4281251367 hasConcept C162324750 @default.
• W4281251367 hasConcept C172205157 @default.
• W4281251367 hasConcept C177212765 @default.
• W4281251367 hasConcept C202444582 @default.
• W4281251367 hasConcept C2775924081 @default.
• W4281251367 hasConcept C2778348673 @default.
• W4281251367 hasConcept C33923547 @default.
• W4281251367 hasConcept C41008148 @default.
• W4281251367 hasConcept C45804977 @default.
• W4281251367 hasConcept C50644808 @default.
• W4281251367 hasConcept C61797465 @default.
• W4281251367 hasConcept C62520636 @default.
• W4281251367 hasConcept C77088390 @default.
• W4281251367 hasConcept C8058405 @default.
• W4281251367 hasConcept C81363708 @default.
• W4281251367 hasConcept C9652623 @default.
• W4281251367 hasConceptScore W4281251367C111766609 @default.
• W4281251367 hasConceptScore W4281251367C119857082 @default.
• W4281251367 hasConceptScore W4281251367C121332964 @default.
• W4281251367 hasConceptScore W4281251367C124101348 @default.
• W4281251367 hasConceptScore W4281251367C127313418 @default.
• W4281251367 hasConceptScore W4281251367C139719470 @default.
• W4281251367 hasConceptScore W4281251367C147168706 @default.
• W4281251367 hasConceptScore W4281251367C154945302 @default.
• W4281251367 hasConceptScore W4281251367C162324750 @default.
• W4281251367 hasConceptScore W4281251367C172205157 @default.
• W4281251367 hasConceptScore W4281251367C177212765 @default.
• W4281251367 hasConceptScore W4281251367C202444582 @default.
• W4281251367 hasConceptScore W4281251367C2775924081 @default.
• W4281251367 hasConceptScore W4281251367C2778348673 @default.
• W4281251367 hasConceptScore W4281251367C33923547 @default.
• W4281251367 hasConceptScore W4281251367C41008148 @default.
• W4281251367 hasConceptScore W4281251367C45804977 @default.
• W4281251367 hasConceptScore W4281251367C50644808 @default.
• W4281251367 hasConceptScore W4281251367C61797465 @default.

• next