Matches in SemOpenAlex for { <https://semopenalex.org/work/W2079766306> ?p ?o ?g. }
Showing items 1 to 76 of
76
with 100 items per page.
- W2079766306 abstract "Abstract Miscible gas injection is one of the most effective enhanced oil recovery techniques and minimum miscibility pressure (MMP) is an important parameter in miscible gas injection processes. Exact determination of this parameter is critical to the designing of the injection equipments and the project investment prospect. The purpose of this paper is to develop a new artificial neural network (ANN) model to predict the minimum miscibility pressure of hydrocarbon gas flooding. Different MMP correlations and models regarding the kind of injection gas and the mechanism of miscibility have been proposed, which are respectively based on mathematical and thermodynamic calculations. Almost all the correlations proposed in the literature are representative of either condensing or vaporizing mechanism or give reasonable results only in the limited range of data they are based on. In this article, by taking into consideration both condensing and vaporizing mechanisms and by using a wider range of data, the new model is introduced. Experimental data from different crude oil reservoirs carried out by slim tube test have been used to obtain a new model in which both mechanisms are included. An Iranian oil reservoir is a candidate for miscible gas recycling with API=23 and initial pressure of 5500 psi. Sampling and recombination is done on the reservoir fluids. Flash and differential expansions are performed for fluid characterization and then miscible experiments are carried out for MMP determination with slim-tube apparatus. The significance of this correlation is that MMP can be determined for any composition of oil and gas, no matter which mechanism is dominant in achieving miscibility. The sensitivity analysis is done and consequently the percentage of error for the model is compared with the literature data. It is shown that the results obtained from the new MMP model are more accurate when compared with other most common correlations reported." @default.
- W2079766306 created "2016-06-24" @default.
- W2079766306 creator A5046467965 @default.
- W2079766306 creator A5075659725 @default.
- W2079766306 creator A5076378795 @default.
- W2079766306 creator A5077462622 @default.
- W2079766306 date "2007-03-11" @default.
- W2079766306 modified "2023-10-17" @default.
- W2079766306 title "Development of a New Artificial-Neural-Network Model for Predicting Minimum Miscibility Pressure in Hydrocarbon Gas Injection" @default.
- W2079766306 cites W1965775669 @default.
- W2079766306 cites W1987004302 @default.
- W2079766306 cites W1989649557 @default.
- W2079766306 cites W2009343133 @default.
- W2079766306 cites W2055929657 @default.
- W2079766306 doi "https://doi.org/10.2118/105407-ms" @default.
- W2079766306 hasPublicationYear "2007" @default.
- W2079766306 type Work @default.
- W2079766306 sameAs 2079766306 @default.
- W2079766306 citedByCount "8" @default.
- W2079766306 countsByYear W20797663062013 @default.
- W2079766306 countsByYear W20797663062018 @default.
- W2079766306 countsByYear W20797663062019 @default.
- W2079766306 countsByYear W20797663062020 @default.
- W2079766306 countsByYear W20797663062023 @default.
- W2079766306 crossrefType "proceedings-article" @default.
- W2079766306 hasAuthorship W2079766306A5046467965 @default.
- W2079766306 hasAuthorship W2079766306A5075659725 @default.
- W2079766306 hasAuthorship W2079766306A5076378795 @default.
- W2079766306 hasAuthorship W2079766306A5077462622 @default.
- W2079766306 hasConcept C110884469 @default.
- W2079766306 hasConcept C121332964 @default.
- W2079766306 hasConcept C127413603 @default.
- W2079766306 hasConcept C154945302 @default.
- W2079766306 hasConcept C159985019 @default.
- W2079766306 hasConcept C178790620 @default.
- W2079766306 hasConcept C185592680 @default.
- W2079766306 hasConcept C192562407 @default.
- W2079766306 hasConcept C2777207669 @default.
- W2079766306 hasConcept C2779681308 @default.
- W2079766306 hasConcept C41008148 @default.
- W2079766306 hasConcept C50644808 @default.
- W2079766306 hasConcept C521977710 @default.
- W2079766306 hasConcept C78762247 @default.
- W2079766306 hasConcept C97355855 @default.
- W2079766306 hasConceptScore W2079766306C110884469 @default.
- W2079766306 hasConceptScore W2079766306C121332964 @default.
- W2079766306 hasConceptScore W2079766306C127413603 @default.
- W2079766306 hasConceptScore W2079766306C154945302 @default.
- W2079766306 hasConceptScore W2079766306C159985019 @default.
- W2079766306 hasConceptScore W2079766306C178790620 @default.
- W2079766306 hasConceptScore W2079766306C185592680 @default.
- W2079766306 hasConceptScore W2079766306C192562407 @default.
- W2079766306 hasConceptScore W2079766306C2777207669 @default.
- W2079766306 hasConceptScore W2079766306C2779681308 @default.
- W2079766306 hasConceptScore W2079766306C41008148 @default.
- W2079766306 hasConceptScore W2079766306C50644808 @default.
- W2079766306 hasConceptScore W2079766306C521977710 @default.
- W2079766306 hasConceptScore W2079766306C78762247 @default.
- W2079766306 hasConceptScore W2079766306C97355855 @default.
- W2079766306 hasLocation W20797663061 @default.
- W2079766306 hasOpenAccess W2079766306 @default.
- W2079766306 hasPrimaryLocation W20797663061 @default.
- W2079766306 hasRelatedWork W165918313 @default.
- W2079766306 hasRelatedWork W2023689079 @default.
- W2079766306 hasRelatedWork W2073015248 @default.
- W2079766306 hasRelatedWork W2177477571 @default.
- W2079766306 hasRelatedWork W2385241743 @default.
- W2079766306 hasRelatedWork W3013879209 @default.
- W2079766306 hasRelatedWork W3200674898 @default.
- W2079766306 hasRelatedWork W4281788031 @default.
- W2079766306 hasRelatedWork W2077021248 @default.
- W2079766306 hasRelatedWork W2189317089 @default.
- W2079766306 isParatext "false" @default.
- W2079766306 isRetracted "false" @default.
- W2079766306 magId "2079766306" @default.
- W2079766306 workType "article" @default.