Szczegóły publikacji
Opis bibliograficzny
Applying of the Artificial Neural Networks (ANN) to identify and characterize sweet spots in shale gas formations / Edyta PUSKARCZYK // E3S Web of Conferences [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 2267-1242. — 2018 — vol. 35 art. no. 03008, s. 1–7. — Wymagania systemowe: Adobe Reader. — Tryb dostępu: https://www.e3s-conferences.org/articles/e3sconf/pdf/2018/10/... [2018-04-04]. — Bibliogr. s. 7, Abstr. — Publikacja dostępna online od: 2018-03-23. — POL-VIET 2017 : scientific-research cooperation between Vietnam and Poland : Krakow, Poland, November 20–22, 2017
Autor
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 113135 |
|---|---|
| Data dodania do BaDAP | 2018-04-27 |
| DOI | 10.1051/e3sconf/20183503008 |
| Rok publikacji | 2018 |
| Typ publikacji | referat w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | E3S Web of Conferences |
Abstract
The main goal of the study was to enhance and improve information about the Ordovician and Silurian gas-saturated shale formations. Author focused on: firstly, identification of the shale gas formations, especially the sweet spots horizons, secondly, classification and thirdly, the accurate characterization of divisional intervals. Data set comprised of standard well logs from the selected well. Shale formations are represented mainly by claystones, siltstones, and mudstones. The formations are also partially rich in organic matter. During the calculations, information about lithology of stratigraphy weren’t taken into account. In the analysis, selforganizing neural network – Kohonen Algorithm (ANN) was used for sweet spots identification. Different networks and different software were tested and the best network was used for application and interpretation. As a results of Kohonen networks, groups corresponding to the gas-bearing intervals were found. The analysis showed diversification between gas-bearing formations and surrounding beds. It is also shown that internal diversification in sweet spots is present. Kohonen algorithm was also used for geological interpretation of well log data and electrofacies prediction. Reliable characteristic into groups shows that Ja Mb and Sa Fm which are usually treated as potential sweet spots only partially have good reservoir conditions. It is concluded that ANN appears to be useful and quick tool for preliminary classification of members and sweet spots identification.
