Pretražite po imenu i prezimenu autora, mentora, urednika, prevoditelja

Napredna pretraga

Pregled bibliografske jedinice broj: 1048910

Lithology prediction in the subsurface by artifcial neural networks on well and 3D seismic data in clastic sediments: a stochastic approach to a deterministic method


Kamenski, Ana; Cvetković, Marko; Kolenković Močilac, Iva; Saftić, Bruno
Lithology prediction in the subsurface by artifcial neural networks on well and 3D seismic data in clastic sediments: a stochastic approach to a deterministic method // GEM - International journal on geomathematics, 11 (2020), 8; 1-24 doi:10.1007/s13137-020-0145-3 (međunarodna recenzija, članak, znanstveni)


Naslov
Lithology prediction in the subsurface by artifcial neural networks on well and 3D seismic data in clastic sediments: a stochastic approach to a deterministic method

Autori
Kamenski, Ana ; Cvetković, Marko ; Kolenković Močilac, Iva ; Saftić, Bruno

Izvornik
GEM - International journal on geomathematics (1869-2672) 11 (2020), 8; 1-24

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Geological modelling ; Artifcial neural networks ; Stochastics ; Lithology ; Probability ; Pannonian Basin

Sažetak
A small area covered by a seismic volume was selected for the analysis of using artifcial neural networks for the purpose of lithology modelling in a stochastic approach to an otherwise deterministic method. Subsurface lithology was simplifed to three categories (sandstone, marl and coal) in accordance with the general geological composition of the Pannonian age sediments in the eastern part of Drava Depression. Two approaches to artifcial neural networks were used—training and prediction with a large number of networks with diferent architecture, and with the same architecture but with the variability of dataset distribution of cases for error calculation in the learning process. Out of a 1000 total cases, 100 realizations of each approach were singled out upon which the data points with probability of 50%, 75% and 90% of occurrence of certain lithology category were upscaled in the model. Six models were generated by indicator kriging. Although in theory, the higher accuracy data should provide a more accurate result, the geologically most sound results were obtained by 50% accuracy data. In higher accuracy results, sandstone lithology was unrealistically over emphasized as a result of the upscaling process, variography and statistical analysis. Presented research can be used in all geoenergy-related subsurface explorations, including hydrocarbon and geothermal explorations, and subsurface characterization for CO2 storage potential and underground energy storage potential as well.

Izvorni jezik
Engleski

Znanstvena područja
Matematika, Geologija



POVEZANOST RADA


Ustanove
Hrvatski geološki institut,
Rudarsko-geološko-naftni fakultet, Zagreb

Citiraj ovu publikaciju

Kamenski, Ana; Cvetković, Marko; Kolenković Močilac, Iva; Saftić, Bruno
Lithology prediction in the subsurface by artifcial neural networks on well and 3D seismic data in clastic sediments: a stochastic approach to a deterministic method // GEM - International journal on geomathematics, 11 (2020), 8; 1-24 doi:10.1007/s13137-020-0145-3 (međunarodna recenzija, članak, znanstveni)
Kamenski, A., Cvetković, M., Kolenković Močilac, I. & Saftić, B. (2020) Lithology prediction in the subsurface by artifcial neural networks on well and 3D seismic data in clastic sediments: a stochastic approach to a deterministic method. GEM - International journal on geomathematics, 11 (8), 1-24 doi:10.1007/s13137-020-0145-3.
@article{article, year = {2020}, pages = {1-24}, DOI = {10.1007/s13137-020-0145-3}, keywords = {Geological modelling, Artifcial neural networks, Stochastics, Lithology, Probability, Pannonian Basin}, journal = {GEM - International journal on geomathematics}, doi = {10.1007/s13137-020-0145-3}, volume = {11}, number = {8}, issn = {1869-2672}, title = {Lithology prediction in the subsurface by artifcial neural networks on well and 3D seismic data in clastic sediments: a stochastic approach to a deterministic method}, keyword = {Geological modelling, Artifcial neural networks, Stochastics, Lithology, Probability, Pannonian Basin} }

Časopis indeksira:


  • Web of Science Core Collection (WoSCC)
    • Emerging Sources Citation Index (ESCI)
  • Scopus


Citati