prikaz prve stranice dokumenta Zvučna karotaža simulirana korištenjem metoda linearne regresije i neuronskih mreža
  Download
PDF 2.65 MB
master's thesis
Zvučna karotaža simulirana korištenjem metoda linearne regresije i neuronskih mreža
diplomski rad
Zagreb: University of Zagreb, Faculty of Mining, Geology and Petroleum Engineering, 2022. urn:nbn:hr:169:110131
Grljević, Stijepo
University of Zagreb
Faculty of Mining, Geology and Petroleum Engineering
Department of Geophysical Exploration and Mine Surveying

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

Cite this document

Grljević, S. (2022). Zvučna karotaža simulirana korištenjem metoda linearne regresije i 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:169:110131

Grljević, Stijepo. "Zvučna karotaža simulirana korištenjem metoda linearne regresije i neuronskih mreža : diplomski rad." Master's thesis, University of Zagreb, Faculty of Mining, Geology and Petroleum Engineering, 2022. https://urn.nsk.hr/urn:nbn:hr:169:110131

Grljević, Stijepo. "Zvučna karotaža simulirana korištenjem metoda linearne regresije i neuronskih mreža : diplomski rad." Master's thesis, University of Zagreb, Faculty of Mining, Geology and Petroleum Engineering, 2022. https://urn.nsk.hr/urn:nbn:hr:169:110131

Grljević, S. (2022). 'Zvučna karotaža simulirana korištenjem metoda linearne regresije i neuronskih mreža : diplomski rad', Master's thesis, University of Zagreb, Faculty of Mining, Geology and Petroleum Engineering, accessed 22 November 2024, https://urn.nsk.hr/urn:nbn:hr:169:110131

Grljević S. Zvučna karotaža simulirana korištenjem metoda linearne regresije i neuronskih mreža : diplomski rad [Master's thesis]. Zagreb: University of Zagreb, Faculty of Mining, Geology and Petroleum Engineering; 2022 [cited 2024 November 22] Available at: https://urn.nsk.hr/urn:nbn:hr:169:110131

S. Grljević, "Zvučna karotaža simulirana korištenjem metoda linearne regresije i neuronskih mreža : diplomski rad", Master's thesis, University of Zagreb, Faculty of Mining, Geology and Petroleum Engineering, Zagreb, 2022. Available at: https://urn.nsk.hr/urn:nbn:hr:169:110131

Metadata
TitleZvučna karotaža simulirana korištenjem metoda linearne regresije i neuronskih mreža : diplomski rad
Title (english)Acoustic log simulated using linear regsression and neural network methods : master's thesis
AuthorStijepo Grljević
MentorJasna Orešković (mentor)
Committee memberJasna Orešković (predsjednik povjerenstva)
Committee memberMarko Cvetković (član povjerenstva)
Committee memberIvan Medved (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Mining, Geology and Petroleum Engineering
(Department of Geophysical Exploration and Mine Surveying)
Zagreb
Defense date and country2022-07-15, Croatia
Scientific / art field,
discipline and subdiscipline		NATURAL SCIENCES
Geology
Abstract
Karotažna mjerenja daju bitne podatke potrebne za karakterizaciju nabušenih stijena, kao i povezivanje bušotinskih i površinskih seizmičkih mjerenja. Zvučna karotaža (AC) pri tome daje podatke potrebne za procjenu poroznosti, izračunavanje akustičnih impedancija potrebnih za konstruiranje sintetskih seizmograma i seizmičku inverziju. Često, a posebno u starim bušotinama, zvučna karotaža nije izvedena ili je izvedena samo na interesantnim intervalima. Softver Interactive Petrophysics sadrži modul Machine Learning u okviru kojeg su alati Neural Network i Multi-Linear regression koji omogućuju predviđanje krivulja karotažnih mjerenja. U ovom radu korišteni su za predviđanje zvučne karotaže na temelju drugih izmjerenih karotažnih krivulja (neutronske karotaže, karotaže gustoće, prirodne gama-radioaktivnosti, karotaže otpornosti). Zvučna karotaža je predviđana u bušotinama polja u Savskoj depresiji (naftno-plinsko polje Stružec), a zatim je uspoređena s izmjerenim krivuljama zvučne karotaže. Rezultati dobiveni objema metodama relativno se dobro podudaraju sa stvarnima, uz manja odstupanja djelomično uzrokovana zasićenjem ugljikovodicima, tankim slojevima ililošom kvalitetom ulaznih podataka i u nekim bušotinama nedostatkom karotažnih krivulja. Također su izračunati sintetski seizmogrami koristeći mjerenu zvučnu karotažu te zvučne karotaže simulirane na dva načina. Dobiveno je vrlo dobro podudaranje u oba slučaja, no nešto bolje poklapanje ostvareno je kod sintetskog seizmograma izračunatog pomoću zvučne karotaže simulirane neuronskom mrežom.
Abstract (english)
Well logging produces essential data necessary for characterization of drilled rocks as well as for comparison of borehole and surface geophysical parameters. Acoustic logging (AC) gives the data needed for porosity estimation, synthetic seismogram calculation and seismic inversion. Acoustic well logging is often omitted, especially in older wells or performed only on interesting depth intervals. Interactive Petrophysics software offers Neural Network and Multi-Linear Regression tools through its Machine Learning Module which provide the prediction of well-logs. In this research, they were used to simulate the AC logs using other well-log curves (neutron log, density log, natural gamma-ray log and resistivity log). Acoustic log prediction was performed for wells in a field located in Sava depression (hydrocarbon field „Stružec“) and compared to actual, measured acoustic well logs. Both methods produced relatively good results, i.e. they show good fitting of the simulated and measured AC-logs. Minor offsets occurred due to the hydrocarbon saturation, thin layers, bad input data quality and the lack of well logs in some wells. Synthetic seismograms were calculated using the measured AC log along with the two simulated ones. Good fitting was obtained in both cases, although the best fit was observed on the synthetic seismogram calculated using the Neural Network-simulated AC-log.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:169:110131
Study programmeTitle: Geology; specializations in: Geology of mineral resources and geophysical explorations
Course: Geology of mineral resources and geophysical explorations
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra geologije (magistar/magistra geologije)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2022-10-04 10:49:51