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Tytuł artykułu

Artificial neural networks as a tool for pattern recognition and electrofacies analysis in Polish palaeozoic shale gas formations

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Unconventional oil and gas reservoirs from the lower Palaeozoic basin at the western slope of the East European Craton were taken into account in this study. The aim was to supply and improve standard well logs interpretation based on machine learning methods, especially ANNs. ANNs were used on standard well logging data, e.g. P-wave velocity, density, resistivity, neutron porosity, radioactivity and photoelectric factor. During the calculations, information about lithology or stratigraphy was not taken into account. We apply different methods of classification: cluster analysis, support vector machine and artificial neural network—Kohonen algorithm. We compare the results and analyse obtained electrofacies. Machine learning method–support vector machine SVM was used for classification. For the same data set, SVM algorithm application results were compared to the results of the Kohonen algorithm. The results were very similar. We obtained very good agreement of results. Kohonen algorithm (ANN) was used for pattern recognition and identification of electrofacies. Kohonen algorithm was also used for geological interpretation of well logs data. As a result of Kohonen algorithm application, groups corresponding to the gas-bearing intervals were found. Analysis showed diversification between gas-bearing formations and surrounding beds. It is also shown that internal diversification in gas-saturated beds is present. It is concluded that ANN appeared to be a useful and quick tool for preliminary classification of members and gas-saturated identification.
Czasopismo
Rocznik
Strony
1991--2003
Opis fizyczny
Bibliogr. 22 poz.
Twórcy
  • AGH University of Science and Technology, al. A. Mickiewicza 30, 30‑059 Kraków, Poland
Bibliografia
  • 1. Doveton J (1994) Geologic log analysis using computer methods. AAPG Computer Applications in Geology, vol 2, Tulsa
  • 2. Hair JF Jr, Black WC, Babin BJ, Anderson RE, Tatham RL (2006) Multivariate data analysis. Pearson Prentice Hall, New Jersey
  • 3. Jarzyna JA, Krakowska PI, Puskarczyk E, Wawrzyniak-Guz K, Zych M (2018) Comprehensive interpretation of the laboratory experiments results to construct model of the polish shale gas rocks. In: POL-VIET 2017: scientific-research cooperation between Vietnam and Poland: Krakow, Poland, November 20–22, 2017; E3S Web of conferences, vol 35, art. no. 03009, pp 1–8. ISSN 2267-1242
  • 4. Kohonen T (1982) Biol Cybern 43:1
  • 5. Krakowska PI, Jarzyna JA, Wawrzyniak-Guz K, Puskarczyk E, Zych M (2016) Heterogeneity analysis of the Polish shale gas formations based on results of laboratory measurements. In: International Multidisciplinary Scientific GeoConference SGEM, pp 817–823. ISSN 1314-2704; ISBN: 978-619-7105-57-5
  • 6. Modliński Z, Szymański B (2008) Lithostratigraphy of the Ordovician in the Podlasie depression and the basement of the Płock-Warsaw trough (Eastern Poland). Biul Państw Inst Geol 430:79–112 (in Polish)
  • 7. Modliński Z, Szymański B, Teller L (2006) The Silurian lithostratigraphy of the Polish part of the Per-Baltic depression (N Poland). Prz Geol 54(9):787–796 (in Polish)
  • 8. Moss B (1997) The partitioning of petrophysical data: a review. In: Lovell MA, Harvey PK (eds) Developments in petrophysics, vol 122. Geological Society Special Publication, pp 181–252
  • 9. Passey QR, Creaney S, Kulla JB, Moretti FJ, Stroud JD (1990) A practical model for organic richness form porosity and resistivity logs. Bull Am Assoc Pet Geol 74(12):1777–1794
  • 10. Poprawa P (2010) Potencjal wystepowania zlóz gazu ziemnego w lupkach dolnego paleozoiku w basenie baltyckim i lubelsko-podlaskim. Prz Geol 58:226–249
  • 11. Puskarczyk E (2017) Shale gas formation heterogeneity analysis using multidimensional statistical methods (PCA, FA, CA, ANN) on the basis of well logging and laboratory investigations. In: Monography R, Jarzyna J, Wawrzyniak-Guz K (eds) Adaptation to the Polish conditions of the methodologies of the sweet spots determination on the basis of correlation of well logging with drilled core samples: methodology to determine sweet spots based on geochemical, petrophysical and geomechanical properties in connection with correlation of laboratory test with well logs and generation model 3D. GOLDRUK Wojciech Golachowski Printing House (in Polish)
  • 12. Puskarczyk E (2018) Applying of the artificial neural networks (ANN) to identify and characterize sweet spots in shale gas formations. In: E3S Web of conferences, 2018, vol 35, art. no. 03008, pp 1–7. ISSN 2267-1242
  • 13. Puskarczyk E, Jarzyna J, Sz Porębski (2015) Application of multivariate statistical methods for characterizing heterolithic reservoirs based on wireline logs—example from the Carpathian Foreland Basin (Middle Miocene, SE Poland). Geol Q 59(1):157–168. https://doi.org/10.7306/gq.1202
  • 14. Rider M (2002) The geological interpretation of well logs. Rider-French Consulting Ltd., Scotland
  • 15. Sebtosheikh MA, Motafakkerfard R, Riahi MA, Moradi S, Sabety N (2015) Support vector machine method, a new technique for lithology prediction in an Iranian heterogeneous carbonate reservoir using petrophysical well logs. Carbonates Evaporites 30:59–68
  • 16. Serra O, Abbott HT (1980) The contribution of logging data to sedimentology and stratigraphic. In: SPE 9270, 55th annual fall technical conference and exhibition, Dallas, Texas
  • 17. Serra O, Serra L (2004) Well logging data acquisition and application. Serra Log, Méry Corbon
  • 18. Sowiżdżał K, Stadtmüller M, Lis-Śledziona A, Kaczmarczyk W (2016) Analiza porównawcza formacji łupkowych w wybranych strefach basenu bałtyckiego na podstawie interpretacji danych otworowych i wyników modelowania geologicznego 3D. Nafta-Gaz, no.11, pp 891–900 (in Polish)
  • 19. Szabó NP (2011) Shale volume estimation based on the factor analysis of well-logging data. Acta Geophys 59:935. https://doi.org/10.2478/s11600-011-0034-0
  • 20. Szabó NP, Dobroka M, Kavanda R (2013) Cluster analysis assisted float-encoded genetic algorithm for a more automated characterization of hydro-carbon reservoirs. Intell Control Autom 4:362–370
  • 21. Wawrzyniak-Guz K, Puskarczyk E, Krakowska PI, Jarzyna JA (2016) Classification of Polish shale gas formations from Baltic Basin, Poland based on well logging data by statistical methods. In: International multidisciplinary scientific GeoConference SGEM, pp 761–768. ISSN 1314-2704; ISBN: 978-619-7105-57-5
  • 22. Wong KW, Ong YS, Gedeon TD, Fung Ch. Ch. (2005) Reservoir characterization using support vector machines. In: Proceedings of the 2005 international conference on computational intelligence for modelling, control and automation, and international conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC’05)
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-bb97cb00-0eb2-4641-87c1-e27554960b3e
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