Luo, Z.R.; Zhou, Y.; Li, Y.X.; Guo, L.; Tuo, J.J.; Xia, X.L. Intelligent Identification Method of Sedimentary Microfacies Based on DMC-BiLSTM. Preprints2021, 2021030459. https://doi.org/10.20944/preprints202103.0459.v1
APA Style
Luo, Z.R., Zhou, Y., Li, Y.X., Guo, L., Tuo, J.J., & Xia, X.L. (2021). Intelligent Identification Method of Sedimentary Microfacies Based on DMC-BiLSTM. Preprints. https://doi.org/10.20944/preprints202103.0459.v1
Chicago/Turabian Style
Luo, Z.R., Juan Juan Tuo and Xia Li Xia. 2021 "Intelligent Identification Method of Sedimentary Microfacies Based on DMC-BiLSTM" Preprints. https://doi.org/10.20944/preprints202103.0459.v1
Abstract
Sedimentary microfacies division is the basis of oil and gas exploration research. The traditional sedimentary microfacies division mainly depends on human experience, which is greatly influenced by human factor and is low in efficiency. Although deep learning has its advantage in solving complex nonlinear problems, there is no effective deep learning method to solve sedimentary microfacies division so far. Therefore, this paper proposes a deep learning method based on DMC-BiLSTM for intelligent division of well-logging—sedimentary microfacies. First, the original curve is reconstructed multi-dimensionally by trend decomposition and median filtering, and spatio-temporal correlation clustering features are extracted from the reconstructed matrix by Kmeans. Then, taking reconstructed features, original curve features and clustering features as input, the prediction types of sedimentary microfacies at current depth are obtained based on BiLSTM. Experimental results show that this method can effectively classify sedimentary microfacies with its recognition efficiency reaching 96.84%.
Keywords
Trend decomposition; Median filtering; kmeans; BiLSTM
Subject
Environmental and Earth Sciences, Atmospheric Science and Meteorology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
