prikaz prve stranice dokumenta Lithology and porosity distribution of high-porosity sandstone reservoir in North Adriatic using machine learning synthetic well catalogue
  Download
PDF 7.91 MB
Scientific paper - Original scientific paper
Lithology and porosity distribution of high-porosity sandstone reservoir in North Adriatic using machine learning synthetic well catalogue
Applied Sciences, 13 (2023), 13; 7671. https://doi.org/10.3390/app13137671
Vukadin, Domagoj; Čogelja, Zoran; Vidaček, Renata; Brkić, Vladislav

Cite this document

Vukadin, D., Čogelja, Z., Vidaček, R. & Brkić, V. (2023). Lithology and porosity distribution of high-porosity sandstone reservoir in North Adriatic using machine learning synthetic well catalogue. Applied Sciences, 13. (13). doi: 10.3390/app13137671

Vukadin, Domagoj, et al. "Lithology and porosity distribution of high-porosity sandstone reservoir in North Adriatic using machine learning synthetic well catalogue." Applied Sciences, vol. 13, no. 13, 2023. https://doi.org/10.3390/app13137671

Vukadin, Domagoj, Zoran Čogelja, Renata Vidaček and Vladislav Brkić. "Lithology and porosity distribution of high-porosity sandstone reservoir in North Adriatic using machine learning synthetic well catalogue." Applied Sciences 13, no. 13 (2023). https://doi.org/10.3390/app13137671

Vukadin, D., et al. (2023) 'Lithology and porosity distribution of high-porosity sandstone reservoir in North Adriatic using machine learning synthetic well catalogue', Applied Sciences, 13(13). doi: 10.3390/app13137671

Vukadin D, Čogelja Z, Vidaček R, Brkić V. Lithology and porosity distribution of high-porosity sandstone reservoir in North Adriatic using machine learning synthetic well catalogue. Applied Sciences [Internet]. 2023 June 28 [cited 2025 April 02];13(13). doi: 10.3390/app13137671

D. Vukadin, Z. Čogelja, R. Vidaček and V. Brkić, "Lithology and porosity distribution of high-porosity sandstone reservoir in North Adriatic using machine learning synthetic well catalogue", Applied Sciences, vol. 13, no. 13, June 2023. [Online]. Available at: https://urn.nsk.hr/urn:nbn:hr:169:699204. [Accessed: 02 April 2025]

Please login to the repository to save this object to your list.
Metadata
Title (english)Lithology and porosity distribution of high-porosity sandstone reservoir in North Adriatic using machine learning synthetic well catalogue
AuthorDomagoj Vukadin
AuthorZoran Čogelja
AuthorRenata Vidaček
AuthorVladislav Brkić
Author's institutionUniversity of Zagreb
Faculty of Mining, Geology and Petroleum Engineering
(Department of Petroleum and Gas Engineering and energy)
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Mining, Petroleum and Geology Engineering
Abstract (english)
Reservoir characterization on offshore fields often faces specific challenges due to limited or unevenly distributed well data. The object of this study is the North Adriatic poorly consolidated clastic reservoir characterized by high porosity. The seismic data indicate notable differences in reservoir quality spatially. The only two wells on the field drilled the best reservoir area. Seismic data, seismic reservoir characterization, and accurate integration with scarce well data were crucial. This paper demonstrates how the application of machine learning algorithms, specifically a Deep Forward Neural Network (DFNN), and the incorporation of pseudo-well data into the reservoir characterization process can improve reservoir properties prediction. The methodology involves creating different reservoir porosity and thickness scenarios using pseudo-well data, synthetic pre-stack seismic data generation, seismic inversion, and DFNN utilization to improve porosity prediction. This study also highlights the importance of lithology discrimination in the geological model to better constrain reservoir properties distribution in the entire reservoir volume. Facies probability analysis was utilized to define interdependence between litho–fluid classes established from the well data and acoustic impedance volume. Apart from the field well data, seismic inversion results, and DFNN porosity volume as main inputs, acknowledgments from the neighboring fields also had an important role.
Keywords (english)
Languageenglish
Publication typeScientific paper - Original scientific paper
Publication statusPublished
Peer reviewPeer review - international
Publication versionPublished version
Journal titleApplied Sciences
Numberingvol. 13, no. 13, 7671
e-ISSN2076-3417
DOIhttps://doi.org/10.3390/app13137671
URN:NBNurn:nbn:hr:169:699204
Publication2023-06-28
Document URLhttps://www.mdpi.com/2076-3417/13/13/7671
Type of resourceText
Access conditionsOpen access
Terms of useLicence Attribution 4.0 International (CC BY 4.0) icon
Created on2025-03-03 16:00:54