Szczegóły publikacji
Opis bibliograficzny
Comparing artificial intelligence algorithms with empirical correlations in shear wave velocity prediction / Mitra KHALILIDERMANI, Dariusz KNEZ // Applied Sciences (Basel) [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 2076-3417. — 2023 — vol. 13 iss. 24 art. no. 13126, s. 1–22. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 19–22, Abstr. — Publikacja dostępna online od: 2023-12-09
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 150843 |
|---|---|
| Data dodania do BaDAP | 2023-12-21 |
| Tekst źródłowy | URL |
| DOI | 10.3390/app132413126 |
| Rok publikacji | 2023 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Applied Sciences (Basel) |
Abstract
Accurate estimation of shear wave velocity (𝑉𝑠) is crucial for modeling hydrocarbon reservoirs. The 𝑉𝑠 values can be directly measured using the Dipole Shear Sonic Imager data; however, it is very expensive and requires specific technical considerations. To address this issue, researchers have developed different methods for 𝑉𝑠 prediction in underground rocks and soils. In this study, the well logging data of a wellbore in the Iranian Aboozar limestone oilfield were used for 𝑉𝑠 estimation. The 𝑉𝑠 values were estimated using five available empirical correlations, linear regression technique, and two machine learning algorithms including multivariate linear regression and gene expression programming. Those values were compared with the real 𝑉𝑠 data. Furthermore, three statistical indices including correlation coefficient (𝑅2), root mean square error (𝑅𝑀𝑆𝐸) were used to evaluate the effectiveness of the applied techniques. The mathematical correlation obtained by the GEP algorithm delivered the most accurate 𝑉𝑠 values with 𝑅2 = 0.972, 𝑅𝑀𝑆𝐸 = 0.000290, and 𝑀𝐴𝐸 = 0.000208. Compared to the available empirical correlations, the obtained correlation from the GEP approach uses multiple parameters to estimate the 𝑉𝑠, thereby leading to more precise predictions. The new correlation can be used to estimate the 𝑉𝑠 values in the Aboozar oilfield and other geologically similar reservoirs.
