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