Improved identification of pay zones in complex environments through wavelet analysis on nuclear magnetic resonance log data

Autor: Ali Mohammad Bagheri, Ezatallah Kazemzadeh, Ali Moradzadeh, Mohammad Heidary
Rok vydání: 2019
Předmět:
Zdroj: Journal of Petroleum Science and Engineering. 172:465-476
ISSN: 0920-4105
Popis: Identification of pay zones is as a challenging topic in the reservoir characterization. Various methods have been employed to tackle this prominent task. Well logs are one of the most useful means for identifying pay zones, chiefly resistivity logs. However, resistivity measurements in some complex environments such as mixed lithology reservoirs, low resistivity and low contrast pay zones may fail to unveil productive zones. As an essentially lithology-independent tool, nuclear magnetic resonance (NMR) log may be the only approach to deal with detecting such subtleties. Using wavelet analysis technique and the NMR log data, this paper was aimed at identifying hydrocarbon bearing zones in two carbonate reservoirs. Discrete wavelet transform (DWT) was applied to the spin echo train at each depth to extract transverse relaxation time ( T 2 ). By comparing the generated T 2 log and resistivity log, a striking similarity was found between them. The T 2 log manifested strong correlation with porosity log. Therefore, the DWT was repeatedly applied to the T 2 log so as to remove the correlation. Various wavelets were adopted to remove the effect of porosity from the T 2 log, leading to achieve pore fluid transverse relaxation time, referred to as T 2 f . Scrutinizing the T 2 f log demonstrated that this log is not only highly functional in detecting productive zones but also highly capable of highlighting the subtle changes of pay zones. Consequently, the T 2 f log revealed the latent pay zones unseen by resistivity log.
Databáze: OpenAIRE
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