Szczegóły publikacji
Opis bibliograficzny
Classification of Polish shale gas formations from Baltic Basin, Poland based on well logging data by statistical methods / Kamila WAWRZYNIAK-GUZ, Edyta PUSKARCZYK, Paulina I. KRAKOWSKA, Jadwiga A. JARZYNA // W: SGEM 2016 : 16th international multidisciplinary scientific geoconference : science and technologies in geology, exploration and mining : 30 June–6 July, 2016, Albena, Bulgaria : conference proceedings. Vol. 3, Hydrogeology, engineering geology and geotechnics, applied and environmental geophysics, oil and gas exploration. — Sofia : STEF92 Technology Ltd., cop. 2016. — (International Multidisciplinary Scientific GeoConference SGEM ; ISSN 1314-2704). — ISBN: 978-619-7105-57-5. — S. 761–768. — Bibliogr. s. 768, Abstr.
Autorzy (4)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 99158 |
|---|---|
| Data dodania do BaDAP | 2016-07-14 |
| Rok publikacji | 2016 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Konferencja | 16th international multidisciplinary scientific geoconference |
| Czasopismo/seria | International Multidisciplinary Scientific GeoConference SGEM |
Abstract
The Ordovician-Silurian shales in the Baltic Basin, Poland have become a major target for unconventional shale gas exploration. The purpose of the research was to characterize the heterogeneity of the Polish shale gas deposits and examine how formations considered as sweet spots can be distinguished .from other fine-grained deposits in the profile with regard to petrophysical parameters derived from well logging. Two statistical methods were used for grouping and classification data according to the natural physical features of rocks. The first one was simple but effective analysis of box and whiskers plots that were applied to the elemental weight percent logs from geochemical logging. The plots clearly grouped the formation into three types regarding distribution of the elements. An elemental composition strictly associated with mineral composition and thus petrophysical properties of rocks allowed to classify the shales into claystones, claystones with mudstones, and calcareous deposits. The second technique was more advanced and included automatic classification solution based on Kohonen neural network algorithm and clustering the input data (i.e. a set of well logs) by hierarchical clustering algorithm. Neural network combined with cluster analysis confirmed lithostratigraphic division of the Ordovician Silurian shales and additionally revealed internal diversity within investigated formations. Both methods that were applied to shale classification gave successful results. Statistical approach helped to determine similarities and differences between the Ordovician and Silurian shales and other fine clastic rocks enriched in organic matter that may contain natural gas. Polish shales that have been recently extensively investigated in Poland due to their hydrocarbon potential are very inhomogeneous. Characterization of sweet spots and differentiation intervals rich in hydrocarbons from surrounding rocks are crucial in more efficient prospection of shale gas deposits.
