Journal Home > Volume 29 , Issue 4
Tsinghua Science and Technology 2024, 29(4): 1206-1218 https://doi.org/10.26599/TST.2023.9010077
Open Access | Issue | Published: 09 February 2024

Lithological Facies Classification Using Attention-Based Gated Recurrent Unit

Show Author's Information Yuwen Liu1,  Yulan Zhang2,  Xingyuan Mao1,  Xucheng Zhou1,  Jingwen Chang3,  Wenwei Wang4,  Pan Wang5,  Lianyong Qi1( )
College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China
Shandong Provincial University Laboratory for Protected Horticulture, Weifang University of Science and Technology, Shouguang 262700, China
School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai 264209, China
Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan 250022, China
School of Modern Posts, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
Keywords:
attention mechanism, facies classification, GRU, MLP (multilayer perceptron)
Cite this article:
Liu Y, Zhang Y, Mao X, et al. Lithological Facies Classification Using Attention-Based Gated Recurrent Unit. Tsinghua Science and Technology, 2024, 29(4): 1206-1218. https://doi.org/10.26599/TST.2023.9010077
Download citation
EndNote(RIS)
BibTeX

735

Views

124

Downloads

Citations

1

Crossref

0

WoS

0

Scopus

0

CSCD

Abstract Full text About this article

Lithological facies classification is a pivotal task in petroleum geology, underpinning reservoir characterization and influencing decision-making in exploration and production operations. Traditional classification methods, such as support vector machines and Gaussian process classifiers, often struggle with the complexity and nonlinearity of geological data, leading to suboptimal performance. Moreover, numerous prevalent approaches fail to adequately consider the inherent dependencies in the sequence of measurements from adjacent depths in a well. A novel approach leveraging an attention-based gated recurrent unit (AGRU) model is introduced in this paper to address these challenges. The AGRU model excels by exploiting the sequential nature of well-log data and capturing long-range dependencies through an attention mechanism. This model enables a flexible and context-dependent weighting of different parts of the sequence, enhancing the discernment of key features for classification. The proposed method was validated on two publicly available datasets. Results demonstrate a considerably improvement over traditional methods. Specifically, the AGRU model achieved superior performance metrics considering precision, recall, and F1-score.


menu
Abstract
Full text
Outline
About this article

Lithological Facies Classification Using Attention-Based Gated Recurrent Unit

Show Author's information Yuwen Liu1, Yulan Zhang2, Xingyuan Mao1, Xucheng Zhou1, Jingwen Chang3, Wenwei Wang4, Pan Wang5, Lianyong Qi1( )
College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China
Shandong Provincial University Laboratory for Protected Horticulture, Weifang University of Science and Technology, Shouguang 262700, China
School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai 264209, China
Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan 250022, China
School of Modern Posts, Nanjing University of Posts and Telecommunications, Nanjing 210023, China

Abstract

Lithological facies classification is a pivotal task in petroleum geology, underpinning reservoir characterization and influencing decision-making in exploration and production operations. Traditional classification methods, such as support vector machines and Gaussian process classifiers, often struggle with the complexity and nonlinearity of geological data, leading to suboptimal performance. Moreover, numerous prevalent approaches fail to adequately consider the inherent dependencies in the sequence of measurements from adjacent depths in a well. A novel approach leveraging an attention-based gated recurrent unit (AGRU) model is introduced in this paper to address these challenges. The AGRU model excels by exploiting the sequential nature of well-log data and capturing long-range dependencies through an attention mechanism. This model enables a flexible and context-dependent weighting of different parts of the sequence, enhancing the discernment of key features for classification. The proposed method was validated on two publicly available datasets. Results demonstrate a considerably improvement over traditional methods. Specifically, the AGRU model achieved superior performance metrics considering precision, recall, and F1-score.

Keywords: attention mechanism, facies classification, GRU, MLP (multilayer perceptron)

References(36)

[1]

J. Ali, U. Ashraf, A. Anees, S. Peng, M. U. Umar, H. Vo Thanh, U. Khan, M. Abioui, H. N. Mangi, M. Ali, et al, Hydrocarbon potential assessment of carbonate-bearing sediments in a meyal oil field, pakistan: Insights from logging data using machine learning and quanti elan modeling, ACS omega, vol. 7, no. 43, pp. 39 375–39 395, 2022.
DOI Google Scholar
[2]

H. Liu, A. Aljbri, J. Song, J. Jiang, and C. Hua, Research advances on ai-powered thermal management for data centers, Tsinghua Science and Technology, vol. 27, no. 2, pp. 303–314, 2021.
DOI Google Scholar
[3]

F. Wang, L. Wang, G. Li, Y. Wang, C. Lv, and L. Qi, Edge-cloud-enabled matrix factorization for diversified apis recommendation in mashup creation, World Wide Web, vol. 25, no. 5, pp. 1809–1829, 2022.
DOI Google Scholar
[4]

H. Dai, J. Yu, M. Li, W. Wang, A. X. Liu, J. Ma, L. Qi, and G. Chen, Bloom filter with noisy coding framework for multi-set membership testing, IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 7, pp. 6710–6724, 2023.
Google Scholar
[5]

D. A. Otchere, T. O. A. Ganat, R. Gholami, and S. Ridha, Application of supervised machine learning paradigms in the prediction of petroleum reservoir properties: Comparative analysis of ann and svm models, Journal of Petroleum Science and Engineering, vol. 200, pp. 108182, 2021.
DOI Google Scholar
[6]
M. ShaAbani, N. Fuad, N. Jamal, and M. Ismail, knn and svm classification for eeg: a review, in InECCE2019 : Proceedings of the 5th International Conference on Electrical, Control & Computer Engineering, Kuantan, Pahang, 2019, pp. 555–565.
DOI
[7]
A. Parmar, R. Katariya, and V. Patel, A review on random forest: An ensemble classifier, in International Conference on Intelligent Data Communication Technologies and Internet of Things (ICICI ) 2018. Springer, 2019, pp. 758–763.
DOI
[8]

Y. Chen, Q. Da, W. Liang, P. Xiao, B. Dai, and G. Zhao, Bagged ensemble of gaussian process classifiers for assessing rockburst damage potential with an imbalanced dataset, Mathematics, vol. 10, no. 18, pp. 3382, 2022.
DOI Google Scholar
[9]

S. Wu, S. Shen, X. Xu, Y. Chen, X. Zhou, D. Liu, X. Xue, and L. Qi, Popularity-aware and diverse web apis recommendation based on correlation graph, IEEE Transactions on Computational Social Systems, vol. 10, no. 2, pp. 771–782, 2023.
DOI Google Scholar
[10]

F. Wang, H. Zhu, G. Srivastava, S. Li, M. R. Khosravi, and L. Qi, Robust collaborative filtering recommendation with user-item-trust records, IEEE Transactions on Computational Social Systems, vol. 9, no. 4, pp. 986–996, 2022.
DOI Google Scholar
[11]

L. Qi, W. Lin, X. Zhang, W. Dou, X. Xu, and J. Chen, A correlation graph based approach for personalized and compatible web apis recommendation in mobile app development, IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 6, pp. 5444–5457, 2023.
Google Scholar
[12]
K. Cho, B. Van Merriënboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, Learning phrase representations using rnn encoder-decoder for statistical machine translation. arxiv 2014, arXiv preprint arXiv : 1406.1078, 2020.
DOI
[13]

F. Liu, Z. Zhang, and R. Zhou, Automatic modulation recognition based on CNN and GRU, Tsinghua Science and Technology, vol. 27, no. 2, pp. 422–431, 2021.
DOI Google Scholar
[14]

J. Halotel, V. Demyanov, and A. Gardiner, Value of geologically derived features in machine learning facies classification, Mathematical Geosciences, vol. 52, pp. 5–29, 2020.
DOI Google Scholar
[15]

D. J. A. Ferreira, W. M. Lupinacci, I. de Andrade Neves, J. P. R. Zambrini, A. L. Ferrari, L. A. P. Gamboa, and M. O. Azul, Unsupervised seismic facies classification applied to a presalt carbonate reservoir, santos basin, offshore brazil, AAPG Bulletin, vol. 103, no. 4, pp. 997–1012, 2019.
DOI Google Scholar
[16]

P. P. Mandal and R. Rezaee, Facies classification with different machine learning algorithm– an efficient artificial intelligence technique for improved classification, ASEG Extended Abstracts, vol. 2019, no. 1, pp. 1–6, 2019.
DOI Google Scholar
[17]
S. Son, J. Hou, Y. Liu, S. Cao, C. Hu, X. Wang, and Z. Chang, Application of artificial neural network in geology: Porosity estimation and lithological facies classification, in 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNCFSKD ). IEEE, 2016, pp. 740–744.
DOI
[18]

L. Kong, G. Li, W. Rafique, S. Shen, Q. He, M. R. Khosravi, R. Wang, and L. Qi, Timeaware missing healthcare data prediction based on arima model, IEEE/ACM Transactions on Computational Biology and Bioinformatics, pp. 1–10, 2022.
DOI Google Scholar
[19]

D. T. dos Santos, M. Roisenberg, and M. dos Santos Nascimento, Deep recurrent neural networks approach to sedimentary facies classification using well logs, IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1–5, 2021.
DOI Google Scholar
[20]

L. Kong, L. Wang, W. Gong, C. Yan, Y. Duan, and L. Qi, Lsh-aware multitype health data prediction with privacy preservation in edge environment, World Wide Web, pp. 1–16, 2021.
DOI Google Scholar
[21]
A. Saleem, J. Choi, D. Yoon, and J. Byun, Facies classification using semi-supervised deep learning with pseudo-labeling strategy, in SEG International Exposition and Annual Meeting. OnePetro, 2019.
DOI
[22]

Y. Yang, X. Yang, M. Heidari, M. A. Khan, G. Srivastava, M. Khosravi, and L. Qi, Astream: Datastream-driven scalable anomaly detection with accuracy guarantee in iiot environment, IEEE Transactions on Network Science and Engineering, 2022.
DOI Google Scholar
[23]

Y. Liu, H. Wu, K. Rezaee, M. R. Khosravi, O. I. Khalaf, A. A. Khan, D. Ramesh, and L. Qi, Interaction-enhanced and time-aware graph convolutional network for successive point-of-interest recommendation in traveling enterprises, IEEE Transactions on Industrial Informatics, vol. 19, no. 1, pp. 635–643, 2022.
DOI Google Scholar
[24]

L. Qi, Y. Liu, Y. Zhang, X. Xu, M. Bilal, and H. Song, Privacy-aware point-of-interest category recommendation in internet of things, IEEE Internet of Things Journal, vol. 21, no. 9, pp. 21 398–21 408, 2022.
DOI Google Scholar
[25]

Y. Imamverdiyev and L. Sukhostat, Lithological facies classification using deep convolutional neural network, Journal of Petroleum Science and Engineering, vol. 174, pp. 216–228, 2019.
DOI Google Scholar
[26]

R. Feng, N. Balling, D. Grana, J. S. Dramsch, and T. M. Hansen, Bayesian convolutional neural networks for seismic facies classification, IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 10, pp. 8933–8940, 2021.
DOI Google Scholar
[27]

Y. Liu, D. Li, S. Wan, F. Wang, W. Dou, X. Xu, S. Li, R. Ma, and L. Qi, A long short-term memory-based model for greenhouse climate prediction, International Journal of Intelligent Systems, vol. 37, no. 1, pp. 135–151, 2022.
DOI Google Scholar
[28]
F. Wang, G. Li, Y. Wang, W. Rafique, M. R. Khosravi, G. Liu, Y. Liu, and L. Qi, Privacyaware traffic flow prediction based on multi-party sensor data with zero trust in smart city, ACM Trans. Internet Technol., 2022, https: //doi.org/10.1145/3511904.
DOI
[29]

A. Kakouei, M. Masihi, B. S. Sola, and E. Biniaz, Lithological facies identification in iranian largest gas field: A comparative study of neural network methods, Journal of the Geological Society of India, vol. 84, pp. 326–334, 2014.
DOI Google Scholar
[30]

V. Puzyrev and C. Elders, Unsupervised seismic facies classification using deep convolutional autoencoder, Geophysics, vol. 87, no. 4, pp. IM125–IM132, 2022.
DOI Google Scholar
[31]

M. Liu, M. Jervis, W. Li, and P. Nivlet, Seismic facies classification using supervised convolutional neural networks and semisupervised generative adversarial networks, Geophysics, vol. 85, no. 4, pp. O47–O58, 2020.
DOI Google Scholar
[32]

M. Ippolito, J. Ferguson, and F. Jenson, Improving facies prediction by combining supervised and unsupervised learning methods, Journal of Petroleum Science and Engineering, vol. 200, p. 108300, 2021.
DOI Google Scholar
[33]
G. Bohling and M. Dubois, An integrated application of neural network and markov chain techniques to the prediction of lithofacies from well logs: Kansas geological survey open-file report 2003-50, 6 p, Group, 2003.
[34]

Y. Liu, A. Pei, F. Wang, Y. Yang, X. Zhang, H. Wang, H. Dai, L. Qi, and R. Ma, An attentionbased category-aware gru model for the next poi recommendation, International Journal of Intelligent Systems, vol. 36, no. 7, pp. 3174–3189, 2021.
DOI Google Scholar
[35]
M. Tian and S. Verma, Recurrent neural network: application in facies classification, in Advances in Subsurface Data Analytics, S. Bhattacharya and H.B. Di, eds. Amsterdam, the Netherlands: Elesvier, 2022, pp. 65−94
DOI
[36]

J. Lin, H. Li, N. Liu, J. Gao, and Z. Li, Automatic lithology identification by applying lstm to logging data: A case study in x tight rock reservoirs, IEEE Geoscience and Remote Sensing Letters, vol. 18, no. 8, pp. 1361–1365, 2020.
DOI Google Scholar
Publication history
Copyright
Acknowledgements
Rights and permissions

Publication history

Received: 20 June 2023
Revised: 07 July 2023
Accepted: 22 July 2024
Published: 09 February 2024
Issue date: August 2024

Copyright

© The Author(s) 2024.

Acknowledgements

Acknowledgment

The paper was supported by National Natural Science Fundation (General Program) of China (Grant: 61972211).

Rights and permissions

The articles published in this open access journal are distributed under the terms of theCreative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

Return