FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3104804895> ?p ?o ?g. }

• W3104804895 endingPage "432" @default.
• W3104804895 startingPage "411" @default.
• W3104804895 abstract "Data-space inversion (DSI) and related procedures represent a family of methods applicable for data assimilation in subsurface flow settings. These methods differ from usual model-based techniques in that they provide only posterior predictions for quantities (time series) of interest, not posterior models with calibrated parameters. DSI methods require a large number (O(500–1000)) of flow simulations to first be performed on prior geological realizations. Given observed data, posterior predictions for time series of interest, such as well injection or production rates, can then be generated directly. DSI operates in a Bayesian setting and provides posterior samples of the data vector. In this work, we develop and evaluate a new approach for data parameterization in DSI. Parameterization is useful in DSI as it reduces the number of variables to determine in the inversion, and it maintains the physical character of the data variables. The new parameterization uses a recurrent autoencoder (RAE) for dimension reduction, and a long short-term memory (LSTM) recurrent neural network architecture to represent flow-rate time series. The RAE-based parameterization is combined with an ensemble smoother with multiple data assimilation (ESMDA) for posterior data sample generation. Results are presented for two- and three-phase flows in a 2D channelized system and a 3D multi-Gaussian model. The new DSI RAE procedure, along with several existing DSI treatments, is assessed through detailed comparison to reference rejection sampling (RS) results. The new DSI methodology is shown to consistently outperform existing approaches, in terms of statistical (P10–P90 interval and Mahalanobis distance) agreement with RS results. The method is also shown to accurately capture derived quantities which are computed from variables considered directly in DSI. This requires correlation and covariance between variables to be properly captured, and accuracy in these relationships is demonstrated. The RAE-based parameterization developed here is clearly useful in DSI, and it may also find application in other subsurface flow problems." @default.
• W3104804895 created "2020-11-23" @default.
• W3104804895 creator A5002057296 @default.
• W3104804895 creator A5013882172 @default.
• W3104804895 date "2020-11-18" @default.
• W3104804895 modified "2023-09-25" @default.
• W3104804895 title "Data-space inversion using a recurrent autoencoder for time-series parameterization" @default.
• W3104804895 cites W1778389879 @default.
• W3104804895 cites W1989411673 @default.
• W3104804895 cites W2004124997 @default.
• W3104804895 cites W2018353504 @default.
• W3104804895 cites W2024330397 @default.
• W3104804895 cites W2031172850 @default.
• W3104804895 cites W2040658407 @default.
• W3104804895 cites W2058051865 @default.
• W3104804895 cites W2064675550 @default.
• W3104804895 cites W2100495367 @default.
• W3104804895 cites W2115613106 @default.
• W3104804895 cites W2122538988 @default.
• W3104804895 cites W2148411410 @default.
• W3104804895 cites W2418993857 @default.
• W3104804895 cites W2436686040 @default.
• W3104804895 cites W2519996514 @default.
• W3104804895 cites W2574572704 @default.
• W3104804895 cites W2581526618 @default.
• W3104804895 cites W2581984441 @default.
• W3104804895 cites W2600422184 @default.
• W3104804895 cites W2606759614 @default.
• W3104804895 cites W2750454216 @default.
• W3104804895 cites W2762902720 @default.
• W3104804895 cites W2806863484 @default.
• W3104804895 cites W2899187130 @default.
• W3104804895 cites W2906141267 @default.
• W3104804895 cites W2916289860 @default.
• W3104804895 cites W2943227802 @default.
• W3104804895 cites W2950337752 @default.
• W3104804895 cites W2958042318 @default.
• W3104804895 cites W2963162215 @default.
• W3104804895 cites W2995015263 @default.
• W3104804895 cites W2999802832 @default.
• W3104804895 cites W3010021872 @default.
• W3104804895 cites W3098024297 @default.
• W3104804895 cites W3123551284 @default.
• W3104804895 doi "https://doi.org/10.1007/s10596-020-10014-1" @default.
• W3104804895 hasPublicationYear "2020" @default.
• W3104804895 type Work @default.
• W3104804895 sameAs 3104804895 @default.
• W3104804895 citedByCount "11" @default.
• W3104804895 countsByYear W31048048952021 @default.
• W3104804895 countsByYear W31048048952022 @default.
• W3104804895 countsByYear W31048048952023 @default.
• W3104804895 crossrefType "journal-article" @default.
• W3104804895 hasAuthorship W3104804895A5002057296 @default.
• W3104804895 hasAuthorship W3104804895A5013882172 @default.
• W3104804895 hasBestOaLocation W31048048952 @default.
• W3104804895 hasConcept C101738243 @default.
• W3104804895 hasConcept C107673813 @default.
• W3104804895 hasConcept C11413529 @default.
• W3104804895 hasConcept C121332964 @default.
• W3104804895 hasConcept C124101348 @default.
• W3104804895 hasConcept C153180895 @default.
• W3104804895 hasConcept C153294291 @default.
• W3104804895 hasConcept C154945302 @default.
• W3104804895 hasConcept C1921717 @default.
• W3104804895 hasConcept C24552861 @default.
• W3104804895 hasConcept C41008148 @default.
• W3104804895 hasConcept C50644808 @default.
• W3104804895 hasConcept C57830394 @default.
• W3104804895 hasConceptScore W3104804895C101738243 @default.
• W3104804895 hasConceptScore W3104804895C107673813 @default.
• W3104804895 hasConceptScore W3104804895C11413529 @default.
• W3104804895 hasConceptScore W3104804895C121332964 @default.
• W3104804895 hasConceptScore W3104804895C124101348 @default.
• W3104804895 hasConceptScore W3104804895C153180895 @default.
• W3104804895 hasConceptScore W3104804895C153294291 @default.
• W3104804895 hasConceptScore W3104804895C154945302 @default.
• W3104804895 hasConceptScore W3104804895C1921717 @default.
• W3104804895 hasConceptScore W3104804895C24552861 @default.
• W3104804895 hasConceptScore W3104804895C41008148 @default.
• W3104804895 hasConceptScore W3104804895C50644808 @default.
• W3104804895 hasConceptScore W3104804895C57830394 @default.
• W3104804895 hasIssue "1" @default.
• W3104804895 hasLocation W31048048951 @default.
• W3104804895 hasLocation W31048048952 @default.
• W3104804895 hasOpenAccess W3104804895 @default.
• W3104804895 hasPrimaryLocation W31048048951 @default.
• W3104804895 hasRelatedWork W1992870039 @default.
• W3104804895 hasRelatedWork W2587510517 @default.
• W3104804895 hasRelatedWork W2597678298 @default.
• W3104804895 hasRelatedWork W2776466379 @default.
• W3104804895 hasRelatedWork W2902494752 @default.
• W3104804895 hasRelatedWork W2946739205 @default.
• W3104804895 hasRelatedWork W3095330331 @default.
• W3104804895 hasRelatedWork W4220825524 @default.
• W3104804895 hasRelatedWork W4287650792 @default.
• W3104804895 hasRelatedWork W4378417406 @default.
• W3104804895 hasVolume "25" @default.
• W3104804895 isParatext "false" @default.

• next