Skip to content
DraconiticResearch
Search Sources API
203,234 studies31 sourcesUpdated 37 min ago
Peer-reviewedCC BY-NC-ND 4.0Scientific Reports

Modeling of fracture intensity by integrating geostatistical and neural network methods: The asmari reservoir, Rag-e-Sefid Oilfield, SW Iran

  • Zahra Tajmir Riahi
  • Ali Faghih
  • Bahman Soleimany
  • Khalil Sarkarinejad
  • Gholam Reza Payrovian
Open at sourcePDFDOICite

Related

  1. Reservoir eutrophication prediction with explainable framework integrating temporal convolutional networks and bidirectional sequence modeling
    Milad Sharafi, Javad Behmanesh, Vahid Rezaverdinejad +1
    Scientific ReportsPeer-reviewedCC BY 4.0Full textEnvironmental science & pollutionCrossref
  2. Gravity topography modeling of the Denman Glacier region using a geostatistical approach
    Mareen Lösing, Alan Aitken, Michael Field +2
    The CryospherePeer-reviewedCC BY 4.0Atmospheric science & meteorologyCrossref
  3. Spatial distribution and modeling of soil organic carbon using machine learning and a geostatistical approach
    Gizachew Ayalew Tiruneh, Ciro Abbud Righi, Tiringo Yilak Alemayehu +4
    Discover SustainabilityPeer-reviewedCC BYFull textEnvironmental science & pollutionDOAJ
  4. Integrating machine learning to assess climate risks on reservoir’s inflow and hydropower generation across West African basins
    Franck Hervé Akaffou, Salomon Obahoundje, Arona Diedhiou +5
    PLOS ClimatePeer-reviewedCC BY 4.0Full textAtmospheric science & meteorologyClimate sciencePLOS
  5. Geochemical modeling of H2 storage in a sandstone reservoir: Sensitivity analysis of abiotic H2S and CO2 gas formation
    Robin Hintzen, Jeroen Snippe, Tim Tambach +4
    Applied GeochemistryPeer-reviewedCC BY 4.0Full textEnvironmental science & pollutionHAL

Cite

BibTeXRIS
BibTeX
@article{riahi2026modeling,
  title = {{Modeling of fracture intensity by integrating geostatistical and neural network methods: The asmari reservoir, Rag-e-Sefid Oilfield, SW Iran}},
  author = {Zahra Tajmir Riahi and Ali Faghih and Bahman Soleimany and Khalil Sarkarinejad and Gholam Reza Payrovian},
  journal = {Scientific Reports},
  year = {2026},
  month = oct,
  doi = {10.1038/s41598-026-74728-5},
  url = {https://www.nature.com/articles/s41598-026-74728-5},
  note = {CC BY-NC-ND 4.0},
}
RIS
TY  - JOUR
TI  - Modeling of fracture intensity by integrating geostatistical and neural network methods: The asmari reservoir, Rag-e-Sefid Oilfield, SW Iran
AU  - Zahra Tajmir Riahi
AU  - Ali Faghih
AU  - Bahman Soleimany
AU  - Khalil Sarkarinejad
AU  - Gholam Reza Payrovian
PY  - 2026
DA  - 2026/10/06
JO  - Scientific Reports
DO  - 10.1038/s41598-026-74728-5
UR  - https://www.nature.com/articles/s41598-026-74728-5
SN  - 2045-2322
ER  -