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Отправлено: 30.08.17 06:41. Заголовок: Kpr-NMR_NN (Google отыскал)
Inversion of the permeability of a tight gas reservoir with the combination of a deep Boltzmann kernel extreme learning machine and nuclear magnetic resonance logging transverse relaxation time spectrum data Zhu, Linqi Zhang, Chong Wei, Yang Zhou, Xueqing Huang, Yuyang Zhang, Chaomo Interpretation-2016 Abstract In view of the low accuracy of the existing NMR logging permeability model in tig ht sandstone reservoirs, we derive a relationship between the nuclear magnetic resonance T2 spectrum and permeability based on the transverse relaxation theory of nuclear magnet ic resonance and the Kozeny-Carman equation. We determined the reasons for the low a ccuracy of the model through the theoretical analysis. We propose the deep Boltzmann k ernel extreme learning machine to improve the deep learning algorithm and to predict the reservoir permeability based on nuclear magnetic resonance logging with a deep Boltzm ann machine. We use the permeability data of 200 rock specimens in a tight gas reservo ir in a certain area and the corresponding T2 spectra from NMR logging for modeling. We apply the model to the evaluation of permeability in this area. The results show that the accuracy of the deep learning algorithm is higher than that of the existing NMR lo gging permeability model and the shallow layer machine learning model. Further, the acc uracy of the deep Boltzmann kernel extreme learning machine proposed by this paper is higher than that of the deep Boltzmann machine, which indicates that deep Boltzmann ke rnel extreme learning machine is more suitable for the prediction of reservoir permeabilit y. Therefore, deep learning theory can be effectively used in oil exploration and develop ment, it can improve the interpretation accuracy of reservoir parameters. These findings c ontribute to the interpretation of reservoir parameters.
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