Matches in SemOpenAlex for { <https://semopenalex.org/work/W2982224914> ?p ?o ?g. }
Showing items 1 to 63 of
63
with 100 items per page.
- W2982224914 abstract "Abstract In this paper, we propose an alternative approach to the problem of oil-production forecast based on the most straightforward feature-based machine-learning algorithm: the linear model. The method can be successfully applied to forecast both oil-rate and liquid-rate in oil fields under (i) water injection, (ii) gas injection, and (iii) simultaneous water and steam injection. Our data-driven algorithm learns the underlying reservoir dynamics from 3 sets of time-series, namely, (i) injection-rate, (ii) liquid and oil-rate, and (iii) number of producers. That is all the data we need to make reliable forecasts, no geological model or numerical reservoir simulators were used." @default.
- W2982224914 created "2019-11-01" @default.
- W2982224914 creator A5089516065 @default.
- W2982224914 creator A5089538026 @default.
- W2982224914 date "2019-10-28" @default.
- W2982224914 modified "2023-09-26" @default.
- W2982224914 title "Application of Machine Learning to Oil Production Forecast under Uncertainties-The Linear Model" @default.
- W2982224914 cites W1482932425 @default.
- W2982224914 cites W2016103205 @default.
- W2982224914 cites W2298497999 @default.
- W2982224914 cites W2406593126 @default.
- W2982224914 cites W2508163625 @default.
- W2982224914 cites W2761663138 @default.
- W2982224914 cites W2802822790 @default.
- W2982224914 cites W2974464356 @default.
- W2982224914 doi "https://doi.org/10.4043/29883-ms" @default.
- W2982224914 hasPublicationYear "2019" @default.
- W2982224914 type Work @default.
- W2982224914 sameAs 2982224914 @default.
- W2982224914 citedByCount "1" @default.
- W2982224914 countsByYear W29822249142022 @default.
- W2982224914 crossrefType "proceedings-article" @default.
- W2982224914 hasAuthorship W2982224914A5089516065 @default.
- W2982224914 hasAuthorship W2982224914A5089538026 @default.
- W2982224914 hasConcept C127413603 @default.
- W2982224914 hasConcept C139719470 @default.
- W2982224914 hasConcept C162324750 @default.
- W2982224914 hasConcept C175234220 @default.
- W2982224914 hasConcept C21880701 @default.
- W2982224914 hasConcept C2778059233 @default.
- W2982224914 hasConcept C2778348673 @default.
- W2982224914 hasConcept C2984309096 @default.
- W2982224914 hasConcept C3020597237 @default.
- W2982224914 hasConcept C41008148 @default.
- W2982224914 hasConcept C78762247 @default.
- W2982224914 hasConceptScore W2982224914C127413603 @default.
- W2982224914 hasConceptScore W2982224914C139719470 @default.
- W2982224914 hasConceptScore W2982224914C162324750 @default.
- W2982224914 hasConceptScore W2982224914C175234220 @default.
- W2982224914 hasConceptScore W2982224914C21880701 @default.
- W2982224914 hasConceptScore W2982224914C2778059233 @default.
- W2982224914 hasConceptScore W2982224914C2778348673 @default.
- W2982224914 hasConceptScore W2982224914C2984309096 @default.
- W2982224914 hasConceptScore W2982224914C3020597237 @default.
- W2982224914 hasConceptScore W2982224914C41008148 @default.
- W2982224914 hasConceptScore W2982224914C78762247 @default.
- W2982224914 hasLocation W29822249141 @default.
- W2982224914 hasOpenAccess W2982224914 @default.
- W2982224914 hasPrimaryLocation W29822249141 @default.
- W2982224914 hasRelatedWork W1445015017 @default.
- W2982224914 hasRelatedWork W2126067773 @default.
- W2982224914 hasRelatedWork W2360981686 @default.
- W2982224914 hasRelatedWork W2415731916 @default.
- W2982224914 hasRelatedWork W2765889516 @default.
- W2982224914 hasRelatedWork W2767097019 @default.
- W2982224914 hasRelatedWork W2898044248 @default.
- W2982224914 hasRelatedWork W2920938200 @default.
- W2982224914 hasRelatedWork W3107474891 @default.
- W2982224914 hasRelatedWork W4280550577 @default.
- W2982224914 isParatext "false" @default.
- W2982224914 isRetracted "false" @default.
- W2982224914 magId "2982224914" @default.
- W2982224914 workType "article" @default.