Effective use of the difference between longitudinal and converted shear waves in reservoir sensitivity analysis is a key component to reduce the multi-solution problem of using longitudinal waves only in reservoir characterization. Deep neural network (DNN) is proven to be a powerful tool to solve an end-to-end task in a purely data-driven way, which brings a promising potential in reservoir characterization applications. In this paper, we design a reservoir prediction method using cluster analysis and DNN method. Seismic attributes sensitive to oil and gas responses are first optimized by cluster analysis, and then, a multi-component composite operation is carried out on the optimized attributes to extract oil and gas characteristics. Finally, the DNN is tested and trained to determine the best network model. The final DNN model is further examined using multi-component data in the Fenggu structural area (Sichuan, China) for seismic gas reservoir prediction. The results show that the seismic gas reservoir distribution predicted using this scheme is generally consistent with actual drilling information. Compared to single-component data, the multi-component composite seismic attributes trained network provides prediction results with higher accuracy and reduces the uncertainty of inversion results.
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Abdel-Fattah, M. I., Pigott, J. D., & El-Sadek, M. S. (2020). Integrated seismic attributes and stochastic inversion for reservoir characterization: Insights from Wadi field (NE Abu-Gharadig Basin, Egypt). Journal of African Earth Sciences, 161, 103661.
Abdideh, M., & Ameri, A. (2019). Cluster analysis of petrophysical and geological parameters for separating the electrofacies of a gas carbonate reservoir sequence. Natural Resources Research, 29(3), 1843–1856.
Abdulaziz, A. M., & Hawary, S. S. (2020). Prediction of carbonate diagenesis from well logs using artificial neural network: An innovative technique to understand complex carbonate systems. Ain Shams Engineering Journal. https://doi.org/10.1016/j.asej.2020.01.010
Abdulaziz, A. M., Mahdi, H. A., & Sayyouh, M. H. (2018). Prediction of reservoir quality using well logs and seismic attributes analysis with artificial neural network: A case study from farrud reservoir, al-ghani field, Libya. Journal of Applied Geophysics, 161, 239–254.
Babasafari, A. A., Ghosh, D., Salim, A. M. A., & Kordi, M. (2020). Lithology-dependent seismic anisotropic amplitude variation with offset correction in transversely isotropic media. Geophysical Prospecting, 68(8), 2471–2493.
Banerjee, A., & Ahmed Salim, A. M. (2020). Seismic attribute analysis of deep-water Dangerous Grounds in the South China Sea, NW Sabah Platform region, Malaysia. Journal of Natural Gas Science and Engineering, 83, 103534.
Brantson, E. T., Ju, B. S., Ziggah, Y. Y., Akwensi, P. H., Sun, Y., Wu, D., & Addo, B. J. (2018). Forecasting of horizontal gas well production decline in unconventional reservoirs using productivity, soft computing and swarm intelligence models. Natural Resources Research, 28(3), 717–756.
Cersósimo, D. S., Ravazzoli, C. L., & Martinez, R. G. (2016). Prediction of lateral variations in reservoir properties throughout an interpreted seismic horizon using an artificial neural network. The Leading Edge, 35(3), 265–269.
Chang, X. C., Wang, Y., Shi, B. B., & Xu, Y. D. (2019). Charging of Carboniferous volcanic reservoirs in the eastern Chepaizi uplift, Junggar Basin (northwestern China) constrained by oil geochemistry and fluid inclusion. AAPG Bulletin, 103(7), 1625–1652.
Feltrin, L., & Bertelli, M. (2019). Using clustered heat maps in mineral exploration to visualize volcanic-hosted massive sulfide alteration and mineralization. Natural Resources Research, 29(1), 311–344.
Feng, Q., Fu, S. T., Zhang, X. L., Chen, Y., Wang, L. Q., & Zhou, F. (2019). Jurassic prototype basin restoration and hydrocarbon exploration prospect in the Qaidam Basin and its adjacent area. Earth Science Frontiers, 26(01), 48–62.
Fu, C., Lin, N. T., Zhang, D., Wen, B., Wei, Q. Q., & Zhang, K. (2018). Prediction of reservoirs using multi-component seismic data and the deep learning method. Chinese Journal of Geophysics, 61(001), 293–303.
Haklidir, F. S. T., & Haklidir, M. (2019). Prediction of reservoir temperatures using hydrogeochemical data, western Anatolia geothermal systems (Turkey): a machine learning approach. Natural Resources Research, 29(4), 2333–2346.
Hinton, G. E., Osindero, S., & Teh, Y. W. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18(7), 1527–1554.
Hossain, S. (2020). Application of seismic attribute analysis in fluvial seismic geomorphology. Journal of Petroleum Exploration and Production Technology, 10(3), 1009–1019.
Li, Q., Zhong, H., Wang, Y., Leng, Y., & Guo, C. (2016). Integrated development optimization model and its solving method of multiple gas fields. Petroleum Exploration and Development, 43(2), 293–300.
Li, Y., Chang, X., Yin, W., Wang, G., Zhang, J., Shi, B., et al. (2019a). Quantitative identification of diagenetic facies and controls on reservoir quality for tight sandstones: A case study of the Triassic Chang 9 oil layer, Zhenjing area, Ordos Basin. Marine and Petroleum Geology, 102, 680–694.
Li, Y., Chang, X., Zhang, J., Xu, Y., & Gao, D. (2019b). Genetic mechanism of heavy oil in the carboniferous volcanic reservoirs of the eastern chepaizi uplift, junggar basin. Arabian Journal of Geosciences, 12(21), 1–13.
Lin, B. T., Guo, J. C., Liu, X., Xiang, J. H., & Zhong, H. (2020). Prediction of flowback ratio and production in Sichuan shale gas reservoirs and their relationships with stimulated reservoir volume. Journal of Petroleum Science and Engineering, 184, 106529.
Lin, N. T., Fu, C., Zhang, D., Jing, X., Zhang, K., & Wen, B. (2018a). Supervised learning and unsupervised learning for hydrocarbon prediction using multiwave seismic data. Geophysical Prospecting for Petroleum, 57(4), 601–610.
Lin, N. T., Zhang, D., Zhang, K., Wang, S. J., Fu, C., & Zhang, J. B. (2018b). Predicting distribution of hydrocarbon reservoirs with seismic data based on learning of the small-sample convolution neural network. Chinese Journal of Geophysics, 061(010), 4110–4125.
Lin, N. T., Liu, H., Li, G. H., Tang, J. J., & Wei, L. J. (2013). Auto-picking velocity by path-integral optimization and surface fairing. Chinese Journal of Geophysics, 056(001), 246–254.
Liu, B., Fu, C., Ren, Y., Zhang, Q., Xu, X., & Chen, Y. (2020). Structural complexity-guided predictive filtering. Geophysical Prospecting, 68(5), 1509–1522.
Liu, Z. P., & He, Y. F. (1995). Application of artificial neural networks in log analysis. Chinese Journal of Geophysics, 38(1), 323–330.
Luo, L., Meng, W. B., Feng, M. S., Tang, X. F., Zhang, S. H., Sun, R., & Xiao, C. H. (2015). Selica sources of quartz cements and its effects on the reservoir in tight sandstones: a case study on the 2th member of the xujiahe formation in xinchang structural belt western sichuan depression. Natural Gas Geoscience, 26(003), 435–443.
Masoudi, P., Tokhmechi, B., Jafari, M., & Moshiri, B. (2012). Application of fuzzy classifier fusion in determining productive zones in oil wells. Energy Exploration & Exploitation, 30(3), 403–416.
Meng, F., Chen, S., Zhang, Y., Chen, H., Guo, P., Mu, T., & Liu, X. (2015). Characterization of motor oil by laser-induced fluorescence. Analytical Letter, 48(13), 2090–2095.
Mohebbi, A., Kamalpour, R., Keyvanloo, K., & Sarrafi, A. (2012). The prediction of permeability from well logging data based on reservoir zoning, using artificial neural networks in one of an Iranian heterogeneous oil reservoir. Liquid Fuels Technology, 30(19), 1998–2007.
Moosavi, S. R., Wood, D. A., Ahmadi, M. A., & Choubineh, A. (2019). Ann-based prediction of laboratory-scale performance of CO2-foam flooding for improving oil recovery. Natural Resources Research, 28(4), 1619–1637.
Nguyen, H., & Bui, X. N. (2018). Predicting blast-induced air overpressure: A robust artificial intelligence system based on artificial neural networks and random forest. Natural Resources Research, 28(3), 893–907.
Nguyen, H., Drebenstedt, C., Bui, X. N., & Bui, D. T. (2019). Prediction of blast-induced ground vibration in an open-pit mine by a novel hybrid model based on clustering and artificial neural network. Natural Resources Research, 29(2), 691–709.
Nwachukwu, A., Jeong, H., Pyrcz, M., & Lake, L. W. (2018). Fast evaluation of well placements in heterogeneous reservoir models using machine learning. Journal of Petroleum Science & Engineering, 163, 463–475.
Oetomo, M. A., Harmoko, U., & Yuliyanto, G. (2019). Reservoir characterization by petrophysical analysis and core data validation, a case study of the “x” field prospect zone. Journal of Physics Conference Series, 1217(1), 12015.
Olden, J. D., & Jackson, D. A. (2002). Illuminating the ‘“black box”’: A randomization approach for understanding variable contributions in artificial neural networks. Ecological Modelling, 154(1–2), 135–150.
Olden, J. D., Joy, M. K., & Death, R. G. (2004). An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data. Ecological Modelling, 178(3–4), 389–397.
Ouadfeul, S., & Aliouane, L. (2016). Total organic carbon estimation in shale-gas reservoirs using seismic genetic inversion with an example from the Barnett shale. Leading Edge, 35(09), 790–794.
Pathak, A. A., & Dodamani, B. M. (2018). Trend analysis of groundwater levels and assessment of regional groundwater drought: Ghataprabha river basin India. Natural Resources Research, 28(3), 631–643.
Rajabinasab, B., & Asghari, O. (2019). Geometallurgical domaining by cluster analysis: Iron ore deposit case study. Natural Resources Research, 28(3), 665–684.
Rezaee, M. R., Ilkhchi, A. K., & Barabadi, A. (2007). Prediction of shear wave velocity from petrophysical data utilizing intelligent systems: An example from a sandstone reservoir of Carnarvon Basin, Australia. Journal of Petroleum Science & Engineering, 55(3–4), 201–212.
Sebtosheikh, M. A., & Salehi, A. (2015). Lithology prediction by support vector classifiers using inverted seismic attributes data and petrophysical logs as a new approach and investigation of training data set size effect on its performance in a heterogeneous carbonate reservoir. Journal of Petroleum Science & Engineering, 134, 143–149.
Shang, Y., Nguyen, H., Bui, X. N., Hieu, T. Q., & Moayedi, H. (2019). A novel artificial intelligence approach to predict blast-induced ground vibration in open-pit mines based on the firefly algorithm and artificial neural network. Natural Resources Research, 29(2), 723–737.
Shi, B. B., Chang, X. C., Yin, W., Li, Y., & Mao, L. X. (2019). Quantitative evaluation model for tight sandstone reservoirs based on statistical methods-A case study of the Triassic Chang 8 tight sandstones, Zhenjing area, Ordos Basin, China. Journal of Petroleum Science and Engineering, 173, 601–616.
Somasundaram, S., Mund, B., Soni, R., & Sharda, R. (2017). Seismic attribute analysis for fracture detection and porosity prediction: A case study from tight volcanic reservoirs, Barmer Basin. India. The Leading Edge, 36(11), 947b1-947b7.
Suhag, A., Ranjith, R., & Aminzadeh, F. (2017). Comparison of shale oil production forecasting using empirical methods and artificial neural networks. In SPE Annual Technical Conference and Exhibition.
Wang, H. Q., Gao, J. H., Chen, K., Guan, X., Gui, J. Y., Guo, X., & Branch, N. (2018). Gas-bearing detection in carbonates with multi-wave amplitude attributes. Oil Geophysical Prospecting, 53(S1), 234–241.
Wang, J., & S., Wang, X. B., Yang, J., & Zhang, Y. (2012). Hydrocarbon prediction based on multi-wave data in Sanhu area. Oil Geophysical Prospecting, 47(4), 605–609.
Wang, K. Y., Xu, Y. Q., Zhang, G. F., Cheng, M. C., & Li, P. H. (2013). Summary of seismic attribute analysis. Progress in Geophysics, 28(002), 815–823.
Wang, Z., Zheng, J., Wang, Y., Yin, J., & Liang, S. (2020). Fracture prediction of thin carbonate reservoir based on Wide-Azimuth Seismic Data. Proceedings of the International Field Exploration and Development Conference, 2018, 1130–1136.
Winer, J. M. (1991). Predicting carbonate permeabilities from wireline logs using a back-propagation neural network. SEG Technical Program Expanded Abstracts, 10(1), 285–288.
Wu, S. K., & Cao, J. X. (2016). Lithology identification method based on continuous restricted Boltzmann machine and support vector machine. Progress in Geophysics, 31(02), 821–828.
Xiao, C. X., Luo, J. H., Xiao, M., & Cai, Y. X. (1993). Forecasting the porosity of carbonate reservoirs by neural net technique. Natural Gas Industry, 013(006), 31–34.
Yin, X. Y., Ye, D. N., & Zhang, G. Z. (2012). Application of kernel fuzzy C-means method to reservoir prediction. Journal of China University of Petroleum (Edition of Natural Science), 36(1), 53–59.
Yue, D. L., Li, W., Wang, W. R., Hu, G. Y., Qiao, H. L., Hu, J. J., Zhang, M. L., & Wang, W. F. (2019). Fused spectral-decomposition seismic attributes and forward seismic modelling to predict sand bodies in meandering fluvial reservoirs. Marine and Petroleum Geology, 99, 27–44.
Zhang, J., & H, Zhu, H., Gao, R. T., & Zhou, Z. X. . (2007). Compound Attribute as New Method for Pickup and Interpretation of Seismic Attributes. Xinjiang Petroleum Geology, 28(4), 494–496.
Zhang, K., Lin, N. T., Fu, C., Zhang, D., Jin, X., & Zhang, C. (2019). Reservoir characterization method with multi-component seismic data by unsupervised learning and colour feature blending. Exploration Geophysics, 50(3), 269–280.
Zhang, Y., & Ruan, G. (2009). Bernoulli Neural network with weights directly determined and with the number of hidden-layer neurons automatically determined. In International symposium on neural networks (vol. 5551, pp. 36–45). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01507-6_5.
Zhao, X. F., & Mendel, J. M. (1988). Minimum-variance deconvolution using artificial neural networks. Seg Technical Program Expanded Abstracts, 7(1), 738–741.
This work was supported by the National Natural Science Foundation of China (Grant No. 41174098). We thank Sinopec Petroleum Exploration and Production Research Institute for providing data for this study and Professor Xiucheng Wei and senior engineers Yuxin Ji, Tiansheng Chen, Chunyuan Liu, and Tao Liu for their helpful suggestions. We are grateful to Elsevier for editing and improving the readability of this article. We would like to thank Dong Zhang, Bo Wen, Jianbin Zhang, Qianqian Wei, Chuanwei Zhao, Xiuchao Yang, Xiangchao Liu, Jie Peng, and Jian Sun for their contributions to this study.
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Yang, J., Lin, N., Zhang, K. et al. Reservoir Characterization Using Multi-component Seismic Data in a Novel Hybrid Model Based on Clustering and Deep Neural Network. Nat Resour Res (2021). https://doi.org/10.1007/s11053-021-09863-z
- Multi-component composite attributes
- Cluster analysis
- Deep neural network
- Supervised learning
- Reservoir prediction