AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
PDF (8.4 MB)
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Open Access

Lithological Facies Classification Using Attention-Based Gated Recurrent Unit

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
Show Author Information

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.

References

[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.

[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.

[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.

[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.

[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.

[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.
[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.
[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.

[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.

[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.

[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.

[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.
[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.

[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.

[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.

[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.

[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.
[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.

[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.

[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.

[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.
[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.

[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.

[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.

[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.

[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.

[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.

[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.
[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.

[30]

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

[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.

[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.

[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.

[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
[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.

Tsinghua Science and Technology
Pages 1206-1218
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

1387

Views

163

Downloads

1

Crossref

1

Web of Science

0

Scopus

0

CSCD

Altmetrics

Received: 20 June 2023
Revised: 07 July 2023
Accepted: 22 July 2024
Published: 09 February 2024
© The Author(s) 2024.

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