Prediction of permeability of tight sandstones from mercury injection capillary pressure tests assisted by a machine-learning approach
| dc.contributor.author | Abbasi, Jassem | |
| dc.contributor.author | Zhao, Jiuyu | |
| dc.contributor.author | Ahmed, Sameer | |
| dc.contributor.author | Jiao, Liang | |
| dc.contributor.author | Andersen, Pål Østebø | |
| dc.contributor.author | Cai, Jianchao | |
| dc.date.accessioned | 2023-04-03T09:41:01Z | |
| dc.date.available | 2023-04-03T09:41:01Z | |
| dc.date.created | 2022-10-10T14:04:02Z | |
| dc.date.issued | 2022 | |
| dc.identifier.citation | Abbasi, J., Zhao, J., Ahmed, S., Jiao, L., Andersen, P. Ø., & Cai, J. (2022). Prediction of permeability of tight sandstones from mercury injection capillary pressure tests assisted by a machine-learning approach. Capillarity, 5(5), 91-104. | en_US |
| dc.identifier.issn | 2709-2119 | |
| dc.identifier.uri | https://hdl.handle.net/11250/3061728 | |
| dc.language.iso | eng | en_US |
| dc.publisher | Yandi Scientific Press | en_US |
| dc.title | Prediction of permeability of tight sandstones from mercury injection capillary pressure tests assisted by a machine-learning approach | en_US |
| dc.type | Peer reviewed | en_US |
| dc.type | Journal article | en_US |
| dc.description.version | publishedVersion | en_US |
| dc.subject.nsi | VDP::Teknologi: 500 | en_US |
| dc.source.journal | Capillarity | en_US |
| dc.identifier.doi | 10.46690/capi.2022.05.02 | |
| dc.identifier.cristin | 2060113 | |
| dc.relation.project | Norges forskningsråd: 331644 | en_US |
| cristin.ispublished | true | |
| cristin.fulltext | postprint | |
| cristin.fulltext | original | |
| cristin.qualitycode | 1 |
