Repository logo
Article

Intelligent control of CO₂-EOR process

creativeworkseries.issn2299-4157
dc.contributor.authorMikołajczak, Edyta
dc.contributor.authorStopa, Jerzy
dc.contributor.authorWojnarowski, Paweł
dc.contributor.authorJaniga, Damian
dc.contributor.authorCzarnota, Robert
dc.date.available2024-11-26T11:55:25Z
dc.date.issued2018
dc.descriptionBibliogr. s. 241-[243].
dc.description.abstractOne of the enhanced oil recovery methods, which enables to recover an additional 15-20% of oil resources is the $CO_2$-EOR method based on carbon dioxide injection into partially depleted reservoirs. Determination of the optimal process control facilitates effective use of natural resources. The idea of this paper is to develop an algorithm that optimizes the $CO_2$-EOR process. This algorithm is based on the combination of artificial intelligence, control theory and computer simulation of hydrocarbon reservoirs. The effect of the proposed solution is the $CO_2$-EOR process control, which is optimal in the case of the adopted objective function expressing the economic value of the project. The obtained results suggest that the use of artificial intelligence methods in the hydrocarbon production allows to improve the process efficiency by an additional 31% compared to the project carried out with the use of engineering knowledge.en
dc.description.placeOfPublicationKraków
dc.description.versionwersja wydawnicza
dc.identifier.doihttps://doi.org/10.7494/drill.2018.35.1.235
dc.identifier.issn2300-7052
dc.identifier.urihttps://repo.agh.edu.pl/handle/AGH/110282
dc.language.isoeng
dc.publisherWydawnictwa AGH
dc.relation.ispartofAGH Drilling, Oil, Gas
dc.rightsAGH Licence - Fair Use
dc.rights.accessotwarty dostęp
dc.rights.urihttps://repo.agh.edu.pl/info/fair-use
dc.subjectCO₂-EORen
dc.subjectproduction optimizationen
dc.subjectintelligent controlen
dc.subjectartificial intelligenceen
dc.titleIntelligent control of CO₂-EOR processen
dc.title.relatedAGH Drilling, Oil, Gasen
dc.typeartykuł
dspace.entity.typePublication
publicationissue.issueNumberNo. 1
publicationissue.paginationpp. 235-242, [1]
publicationvolume.volumeNumberVol. 35
relation.isAuthorOfPublicationdabb2ef5-ca6c-4c14-9a56-7e348e848153
relation.isAuthorOfPublication5ffcdfea-ac89-4b74-aa7d-70267b2a6b35
relation.isAuthorOfPublicationb8b47e89-f280-45fa-b545-0724df21e877
relation.isAuthorOfPublication0fb2a7e5-e6af-4435-b04c-3bf4492fdecf
relation.isAuthorOfPublication.latestForDiscoverydabb2ef5-ca6c-4c14-9a56-7e348e848153
relation.isJournalIssueOfPublication6a46aca6-4ed2-420e-95de-266415e89e17
relation.isJournalIssueOfPublication.latestForDiscovery6a46aca6-4ed2-420e-95de-266415e89e17
relation.isJournalOfPublicationd59ea52a-1306-4ec1-ae8c-7c2d545cf0f7

Files

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
drill.2018.35.1.235.pdf
Size:
782.96 KB
Format:
Adobe Portable Document Format