FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2022658703> ?p ?o ?g. }

• W2022658703 endingPage "740" @default.
• W2022658703 startingPage "723" @default.
• W2022658703 abstract "Evaluation of steam-assisted gravity drainage (SAGD) performance that involves detailed compositional simulations is usually deterministic, cumbersome, expensive (manpower and time consuming), and not quite suitable for practical decision making and forecasting, particularly when dealing with high-dimensional data space consisting of large number of operational and geological parameters. Data-driven modeling techniques, which entail comprehensive data analysis and implementation of machine learning methods for system forecast, provide an attractive alternative. In this paper, artificial neural network (ANN) is employed to predict SAGD production in heterogeneous reservoirs, an important application that is lacking in existing literature. Numerical flow simulations are performed to construct a training data set consists of various attributes describing characteristics associated with reservoir heterogeneities and other relevant operating parameters. Empirical Arps decline parameters are tested successfully for parameterization of cumulative production profile and considered as outputs of the ANN models. Sensitivity studies on network configurations are also investigated. Principal components analysis (PCA) is performed to reduce the dimensionality of the input vector, improve prediction quality, and limit over-fitting. In a case study, reservoirs with distinct heterogeneity distributions are fed to the model. It is shown that robustness and accuracy of the prediction capability are greatly enhanced when cluster analysis are performed to identify internal data structures and groupings prior to ANN modeling. Both deterministic and fuzzy-based clustering techniques are compared, and separate ANN model is constructed for each cluster. The model is then tested using a validation data set (cases that have not been used during the training stage). The proposed approach can be integrated directly into most existing reservoir management routines. In addition, incorporating techniques for dimensionality reduction and clustering with ANN demonstrates the viability of this approach for analyzing large field data set. Given that quantitative ranking of operating areas, robust forecasting, and optimization of heavy oil recovery processes are major challenges faced by the industry, the proposed research highlights the significant potential of applying effective data-driven modeling approaches in analyzing other solvent-additive steam injection projects." @default.
• W2022658703 created "2016-06-24" @default.
• W2022658703 creator A5004951074 @default.
• W2022658703 creator A5018957484 @default.
• W2022658703 creator A5079537222 @default.
• W2022658703 creator A5090525883 @default.
• W2022658703 date "2015-02-01" @default.
• W2022658703 modified "2023-10-01" @default.
• W2022658703 title "Integrated cluster analysis and artificial neural network modeling for steam-assisted gravity drainage performance prediction in heterogeneous reservoirs" @default.
• W2022658703 cites W1976509667 @default.
• W2022658703 cites W1986880669 @default.
• W2022658703 cites W1987971958 @default.
• W2022658703 cites W1990368529 @default.
• W2022658703 cites W1990476081 @default.
• W2022658703 cites W1990826942 @default.
• W2022658703 cites W1990893622 @default.
• W2022658703 cites W1992147426 @default.
• W2022658703 cites W1994181952 @default.
• W2022658703 cites W1994656180 @default.
• W2022658703 cites W1997235858 @default.
• W2022658703 cites W1998349408 @default.
• W2022658703 cites W2004601679 @default.
• W2022658703 cites W2005726493 @default.
• W2022658703 cites W2006232606 @default.
• W2022658703 cites W2008561762 @default.
• W2022658703 cites W2009950164 @default.
• W2022658703 cites W2012173597 @default.
• W2022658703 cites W2012427834 @default.
• W2022658703 cites W2014682740 @default.
• W2022658703 cites W2016451901 @default.
• W2022658703 cites W2016951851 @default.
• W2022658703 cites W2023840662 @default.
• W2022658703 cites W2024392312 @default.
• W2022658703 cites W2025924882 @default.
• W2022658703 cites W2026532985 @default.
• W2022658703 cites W2026593176 @default.
• W2022658703 cites W2027067114 @default.
• W2022658703 cites W2027693022 @default.
• W2022658703 cites W2033672252 @default.
• W2022658703 cites W2033904036 @default.
• W2022658703 cites W2037325453 @default.
• W2022658703 cites W2038550955 @default.
• W2022658703 cites W2038648432 @default.
• W2022658703 cites W2050570866 @default.
• W2022658703 cites W2051787883 @default.
• W2022658703 cites W2053779593 @default.
• W2022658703 cites W2054132868 @default.
• W2022658703 cites W2057285821 @default.
• W2022658703 cites W2058441237 @default.
• W2022658703 cites W2059846335 @default.
• W2022658703 cites W2060598872 @default.
• W2022658703 cites W2065724166 @default.
• W2022658703 cites W2068992358 @default.
• W2022658703 cites W2069084807 @default.
• W2022658703 cites W2073397831 @default.
• W2022658703 cites W2073819036 @default.
• W2022658703 cites W2074786424 @default.
• W2022658703 cites W2076579537 @default.
• W2022658703 cites W2078146557 @default.
• W2022658703 cites W2082280104 @default.
• W2022658703 cites W2083241398 @default.
• W2022658703 cites W2088238688 @default.
• W2022658703 cites W2092159336 @default.
• W2022658703 cites W2093206231 @default.
• W2022658703 cites W2096227959 @default.
• W2022658703 cites W2098379698 @default.
• W2022658703 cites W2102342419 @default.
• W2022658703 cites W2116280911 @default.
• W2022658703 cites W2161619569 @default.
• W2022658703 cites W4211007335 @default.
• W2022658703 doi "https://doi.org/10.1016/j.eswa.2014.08.034" @default.
• W2022658703 hasPublicationYear "2015" @default.
• W2022658703 type Work @default.
• W2022658703 sameAs 2022658703 @default.
• W2022658703 citedByCount "61" @default.
• W2022658703 countsByYear W20226587032015 @default.
• W2022658703 countsByYear W20226587032016 @default.
• W2022658703 countsByYear W20226587032017 @default.
• W2022658703 countsByYear W20226587032018 @default.
• W2022658703 countsByYear W20226587032019 @default.
• W2022658703 countsByYear W20226587032020 @default.
• W2022658703 countsByYear W20226587032021 @default.
• W2022658703 countsByYear W20226587032022 @default.
• W2022658703 countsByYear W20226587032023 @default.
• W2022658703 crossrefType "journal-article" @default.
• W2022658703 hasAuthorship W2022658703A5004951074 @default.
• W2022658703 hasAuthorship W2022658703A5018957484 @default.
• W2022658703 hasAuthorship W2022658703A5079537222 @default.
• W2022658703 hasAuthorship W2022658703A5090525883 @default.
• W2022658703 hasConcept C104317684 @default.
• W2022658703 hasConcept C111030470 @default.
• W2022658703 hasConcept C119857082 @default.
• W2022658703 hasConcept C124101348 @default.
• W2022658703 hasConcept C127413603 @default.
• W2022658703 hasConcept C154945302 @default.
• W2022658703 hasConcept C185592680 @default.
• W2022658703 hasConcept C21200559 @default.
• W2022658703 hasConcept C24326235 @default.

• next