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Pregled bibliografske jedinice broj: 1198410

Enhancing time to depth relation estimations in subsurface exploration using supervised neural networks


Cvetković, Marko; Brcković, Ana; Kapuralić, Josipa
Enhancing time to depth relation estimations in subsurface exploration using supervised neural networks // Abstract book of the GEOMATES 2022 / Gábor Hatvani, István ; Erdélyi, Dániel ; Fedor, Ferenc (ur.).
Pécs, Mađarska, 2022. str. 70-70 (poster, međunarodna recenzija, sažetak, znanstveni)


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Naslov
Enhancing time to depth relation estimations in subsurface exploration using supervised neural networks

Autori
Cvetković, Marko ; Brcković, Ana ; Kapuralić, Josipa

Vrsta, podvrsta i kategorija rada
Sažeci sa skupova, sažetak, znanstveni

Izvornik
Abstract book of the GEOMATES 2022 / Gábor Hatvani, István ; Erdélyi, Dániel ; Fedor, Ferenc - , 2022, 70-70

Skup
GEOMATES 2022

Mjesto i datum
Pécs, Mađarska, 19.-21.05.2022

Vrsta sudjelovanja
Poster

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Artificial neural networks ; seismic ; wells ; hydrocarbon exploration

Sažetak
Conventional methods of solving time to depth relations needed for successful interpretation of seismic data and later construction of geological model include vertical seismic profiling and construction of synthetic seismogram based on available well log data from acoustic and density well logs. However, when re-evaluating the potential of mature basins that had its peak exploration and production in the pre-1990’s, this data is often lacking and/or the spatial distribution data is too irregular. As the main controlling factor of the acoustic properties of the rocks is lithology in relation to compaction, we propose the usage of “sandstone to shale” ratio and general lithology in regard to the stratigraphic interval (Pliocene, Upper Miocene, Lower and Middle Miocene and Base Neogene categories) along with burial depth as a controlling factor for determining time to depth relations in wells lacking data for the abovementioned conventional approach. In this way the time to depth relation is controlled as permeable versus impermeable lithologies based on the interpretation of conventional well logs. Initial research was performed using data from four wells with vertical seismic profiling data using the multi-layer perceptron networks. Successfulness of the learning was tested on one additional well which also had vertical seismic profiling measurements. The successfulness of the prediction, although lower than in learning dataset, proved to be more accurate than using the time to depth relations from the nearest well.

Izvorni jezik
Engleski

Znanstvena područja
Geologija, Geofizika



POVEZANOST RADA


Projekti:
HRZZ-UIP-2019-04-3846 - GEOloška karakterizacija podzemlja istočnog dijela Dravske depresije s ciljem procjene Energetskog Potencijala (GEODEP) (Cvetković, Marko, HRZZ - 2019-04) ( POIROT)

Ustanove:
Rudarsko-geološko-naftni fakultet, Zagreb

Profili:

Avatar Url Josipa Kapuralić (autor)

Avatar Url Ana Brcković (autor)

Avatar Url Marko Cvetković (autor)

Poveznice na cjeloviti tekst rada:

Pristup cjelovitom tekstu rada geomates.eu

Citiraj ovu publikaciju:

Cvetković, Marko; Brcković, Ana; Kapuralić, Josipa
Enhancing time to depth relation estimations in subsurface exploration using supervised neural networks // Abstract book of the GEOMATES 2022 / Gábor Hatvani, István ; Erdélyi, Dániel ; Fedor, Ferenc (ur.).
Pécs, Mađarska, 2022. str. 70-70 (poster, međunarodna recenzija, sažetak, znanstveni)
Cvetković, M., Brcković, A. & Kapuralić, J. (2022) Enhancing time to depth relation estimations in subsurface exploration using supervised neural networks. U: Gábor Hatvani, I., Erdélyi, D. & Fedor, F. (ur.)Abstract book of the GEOMATES 2022.
@article{article, author = {Cvetkovi\'{c}, Marko and Brckovi\'{c}, Ana and Kapurali\'{c}, Josipa}, year = {2022}, pages = {70-70}, keywords = {Artificial neural networks, seismic, wells, hydrocarbon exploration}, title = {Enhancing time to depth relation estimations in subsurface exploration using supervised neural networks}, keyword = {Artificial neural networks, seismic, wells, hydrocarbon exploration}, publisherplace = {P\'{e}cs, Ma\djarska} }
@article{article, author = {Cvetkovi\'{c}, Marko and Brckovi\'{c}, Ana and Kapurali\'{c}, Josipa}, year = {2022}, pages = {70-70}, keywords = {Artificial neural networks, seismic, wells, hydrocarbon exploration}, title = {Enhancing time to depth relation estimations in subsurface exploration using supervised neural networks}, keyword = {Artificial neural networks, seismic, wells, hydrocarbon exploration}, publisherplace = {P\'{e}cs, Ma\djarska} }




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