Title

Reservoir and lithofacies shale classification based on NMR logging

Document Type

Journal Article

Publication Title

Petroleum Research

ISSN

20962495

Publisher

Elsevier

School

School of Engineering

Comments

Yu, H., Wang, Z., Wen, F., Rezaee, C., Lebedev, M., Li, X., ... & Iglauer, S. (2020). Reservoir and lithofacies shale classification based on NMR logging. Petroleum Research, Advanced online publication. https://doi.org/10.1016/j.ptlrs.2020.04.005

Abstract

© 2020 Chinese Petroleum Society Shale gas reservoirs have fine-grained textures and high organic contents, leading to complex pore structures. Therefore, accurate well-log derived pore size distributions are difficult to acquire for this unconventional reservoir type, despite their importance. However, nuclear magnetic resonance (NMR) logging can in principle provide such information via hydrogen relaxation time measurements. Thus, in this paper, NMR response curves (of shale samples) were rigorously mathematically analyzed (with an Expectation Maximization algorithm) and categorized based on the NMR data and their geology, respectively. Thus the number of the NMR peaks, their relaxation times and amplitudes were analyzed to characterize pore size distributions and lithofacies. Seven pore size distribution classes were distinguished; these were verified independently with Pulsed-Neutron Spectrometry (PNS) well-log data. This study thus improves the interpretation of well log data in terms of pore structure and mineralogy of shale reservoirs, and consequently aids in the optimization of shale gas extraction from the subsurface.

DOI

10.1016/j.ptlrs.2020.04.005

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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