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master's thesis
Kartiranje ležišta ugljikovodika metodom umjetnih neuronskih mreža
diplomski rad
Zagreb: University of Zagreb, Faculty of Mining, Geology and Petroleum Engineering, 2016. urn:nbn:hr:169:369345
Šapina, Marijan
University of Zagreb
Faculty of Mining, Geology and Petroleum Engineering
Department of Petroleum and Gas Engineering and energy

Institutional repository: Faculty of Mining, Geology and Petroleum Engineering Repository, University of Zagreb

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Šapina, M. (2016). Kartiranje ležišta ugljikovodika metodom umjetnih neuronskih mreža : diplomski rad (Master's thesis). Zagreb: University of Zagreb, Faculty of Mining, Geology and Petroleum Engineering. Retrieved from https://urn.nsk.hr/urn:nbn:hr:169:369345

Šapina, Marijan. "Kartiranje ležišta ugljikovodika metodom umjetnih neuronskih mreža : diplomski rad." Master's thesis, University of Zagreb, Faculty of Mining, Geology and Petroleum Engineering, 2016. https://urn.nsk.hr/urn:nbn:hr:169:369345

Šapina, Marijan. "Kartiranje ležišta ugljikovodika metodom umjetnih neuronskih mreža : diplomski rad." Master's thesis, University of Zagreb, Faculty of Mining, Geology and Petroleum Engineering, 2016. https://urn.nsk.hr/urn:nbn:hr:169:369345

Šapina, M. (2016). 'Kartiranje ležišta ugljikovodika metodom umjetnih neuronskih mreža : diplomski rad', Master's thesis, University of Zagreb, Faculty of Mining, Geology and Petroleum Engineering, accessed 17 February 2025, https://urn.nsk.hr/urn:nbn:hr:169:369345

Šapina M. Kartiranje ležišta ugljikovodika metodom umjetnih neuronskih mreža : diplomski rad [Master's thesis]. Zagreb: University of Zagreb, Faculty of Mining, Geology and Petroleum Engineering; 2016 [cited 2025 February 17] Available at: https://urn.nsk.hr/urn:nbn:hr:169:369345

M. Šapina, "Kartiranje ležišta ugljikovodika metodom umjetnih neuronskih mreža : diplomski rad", Master's thesis, University of Zagreb, Faculty of Mining, Geology and Petroleum Engineering, Zagreb, 2016. Available at: https://urn.nsk.hr/urn:nbn:hr:169:369345

Metadata
TitleKartiranje ležišta ugljikovodika metodom umjetnih neuronskih mreža : diplomski rad
Title (english)Mapping of hydrocarbon reservoirs using artificial neural networks : master’s thesis
AuthorMarijan Šapina
MentorTomislav Malvić (mentor)
Committee memberTomislav Malvić (predsjednik povjerenstva)
Committee memberJosipa Velić (član povjerenstva)
Committee memberZdenko Krištafor (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Mining, Geology and Petroleum Engineering
(Department of Petroleum and Gas Engineering and energy)
Zagreb
Defense date and country2016-06-24, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Mining, Petroleum and Geology Engineering
Petroleum Mining
Abstract
Računalno kartiranje ležišta ugljikovodika moguće je raznim metodama i tehnikama, primjerice metodama geostatističke procjene, ali i geomatematičkim metodama, kao što su primjena umjetnih neuronskih mreža. Metoda umjetnih neuronskih mreža se primarno koristi u slučaju manjkavih podataka i podataka u nelinearnom odnosu, a karakteristika te metode je da simulira postupak ljudskog učenja uvježbavanjem parametara nizom ponavljanja. U ovomu radu je statističkom analizom i usporedbom s geostatistiĉkim interpolacijskim metodama potvrđena postavljena hipoteza o uspješnom kartiranju izvornim algoritmom umjetne neuronske mreţe izrađenom u programu za statističku obradu podataka „R“ na primjeru kartiranja dubine EK markera (elektrokarotaţnog markera) Rs5 u području Bjelovarske subdepresije unutar Dravske depresije u hrvatskom dijelu Panonskog bazenskog sustava. Arhitektura mreže koja je dala najbolji izlaz je mreža s dva skrivena sloja, od kojih se prvi sastojao od 10 neurona, a drugi od 6. Korišten je algoritam s povratnim postupkom. Kros-validacijom su uspoređene dvije metoda kartiranja, s time da je algoritmom neuronske mreže dobivena vrijednost sume kvadrata pogrješke 16294,5, a običnim krigiranjem 14638,35.
Abstract (english)
Abstract: Computerized mapping of hydrocarbon reservoirs can be achieved using a wide range of methods and techniques, for example, geostatistical interpolation. The use of geomathematical methods is also possible, for example, the use of artificial neural networks. Artificial neural networks are primarily used in case of flawed data and data that are in a non-linear relation. The characteristic of this method is that it simulates the process of human learning by training and optimizing parameters through a series of repetitions. In this thesis, using statistical analysis and comparison with geostatistical interpolation methods, the hypothesis of successful mapping using this original artificial neural network algorithm has been confirmed. The algorithm has been made in “R“, a statistical computing software, and is used for mapping of depth of e-log border “Rs5“ in the Bjelovar Sub-depression, as a part of the Drava Depression in the Croatian part of the Pannonian Basin System. The neural network architecture that produced best responses is a network with two hidden layers, with 10 and 6 neurons, respectively. Back-propagation algorithm was used. Two methods were compared by cross-validation and the neural network produced a mean squared error as 16294.5, and Ordinary Kriging produced 14638.35.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:169:369345
Study programmeTitle: Petroleum engineering; specializations in: Petroleum engineering, Energy production and supply, Environmental protection in petroleum engineering
Course: Energy production and supply
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka naftnog rudarstva (magistar/magistra inženjer/inženjerka naftnog rudarstva)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2016-08-31 12:24:36