21
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Missing log synthesis based on stacking ensemble learning with invariable features

, , , , &

References

  • Bahrpeyma, F., B. Golchin, and C. Cranganu. 2013. Fast fuzzy modeling method to estimate missing logsin hydrocarbon reservoirs. Journal of Petroleum Science and Engineering 112:310–21. doi:10.1016/j.petrol.2013.11.019.
  • Belhouchet, H. E., M. S. Benzagouta, A. Dobbi, A. Alquraishi, and J. Duplay. 2021. A new empirical model for enhancing well log permeability prediction, using nonlinear regression method: Case study from Hassi-Berkine oil field reservoir–Algeria. Journal of King Saud University - Engineering Sciences 33 (2):136–45. doi:10.1016/j.jksues.2020.04.008.
  • Breiman, L. 2001. Random forest. Machine Learning 45 (1):5–32. doi:10.1023/A:1010933404324.
  • Bukar, I., M. B. Adamu, and U. Hassan. 2019. A machine learning approach to shear sonic log prediction. SPE Nigeria Annual International Conference and Exhibition. OnePetro, doi:10.2118/198764-MS.
  • Ch, S., N. Anand, B. K. Panigrahi, and S. Mathur. 2013. Streamflow forecasting by SVM with quantum behaved particle swarm optimization. Neurocomputing 101:18–23. doi:10.1016/j.neucom.2012.07.017.
  • Chen, Y., L. Liu, V. Phonevilay, K. Gu, R. Xia, J. Xie, Q. Zhang, and K. Yang. 2021. Image super-resolution reconstruction based on feature map attention mechanism. Applied Intelligence 51 (7):4367–80. doi:10.1007/s10489-020-02116-1.
  • Chen, Y., and D. Zhang. 2020a. Physics-constrained deep learning of geomechanical logs. IEEE Transactions on Geoscience and Remote Sensing 58 (8):5932–43. doi:10.1109/TGRS.2020.2973171.
  • Chen, Y., and D. Zhang. 2020b. Well log generation via ensemble long short‐term memory (EnLSTM) network. Geophysical Research Letters 47 (23):e2020GL087685. doi:10.1029/2020GL087685.
  • Chen, Y., H. Zhang, L. Liu, J. Tao, Q. Zhang, K. Yang, R. Xia, and J. Xie. 2023. Research on image inpainting algorithm of improved total variation minimization method. Journal of Ambient Intelligence and Humanized Computing 14 (5):5555–64. doi:10.1007/s12652-020-02778-2.
  • Cortes, C., and V. Vapnik. 1995. Support-vector networks. Machine Learning 20 (3):273–97. doi:10.1007/BF00994018.
  • Gowida, A., S. Elkatatny, and A. Abdulraheem. 2019. Application of artificial neural network to predict formation bulk density while drilling. Petrophysics – the SPWLA Journal of Formation Evaluation and Reservoir Description 60 (5):660–74. doi:10.30632/PJV60N5-2019a9.
  • He, J., and S. Misra. 2019. Generation of synthetic dielectric dispersion logs in organic-rich shale formations using neural-network models. Geophysics 84 (3):D117–D129. doi:10.1190/geo2017-0685.1.
  • He, W., and H. Ma. 2005. Fractal estimation method for logging curve. Oil Gas 27 (1):62–5.
  • He, X., R. Santoso, and H. Hoteit. 2020. Application of machine-learning to construct equivalent continuum models from high-resolution discrete-fracture models. International Petroleum Technology Conference. IPTC, D031S075R003. doi:10.2523/IPTC-20040-MS.
  • Huang, S., X. Zheng, L. Ma, H. Wang, Q. Huang, G. Leng, E. Meng, and Y. Guo. 2020. Quantitative contribution of climate change and human activities to vegetation cover variations based on GA-SVM model. Journal of Hydrology 584:124687. doi:10.1016/j.jhydrol.2020.124687.
  • Jian, H., L. Chenghui, C. Zhimin, and M. Haiwei. 2020. Integration of deep neural networks and ensemble learning machines for missing well logs estimation. Flow Measurement and Instrumentation 73:101748. doi:10.1016/j.flowmeasinst.2020.101748.
  • Lawrence, R. L., S. D. Wood, and R. L. Sheley. 2006. Mapping invasive plants using hyperspectral imagery and Breiman Cutler classifications (RandomForest). Remote Sensing of Environment 100 (3):356–62. doi:10.1016/j.rse.2005.10.014.
  • Liao, H. M. 2014. Multivariate regression method for correcting the influence of expanding diameter on acoustic curve of density curve.Geophysical and Geochemistry Exploration 38 (1):174–9.
  • Liu, H., Q. Xiao, Z. Jiao, et al. 2020. LightGBM-based prediction of remaining useful life for electric vehicle battery under driving conditions. 2020 IEEE Sustainable Power and Energy Conference (iSPEC). IEEE, 2577–82. doi:10.1109/iSPEC50848.2020.9351029.
  • Long, W., D. Chai, and F. Aminzadeh. 2016. Pseudo density log generation using artificial neural network. SPE Western Regional Meeting. OnePetro. doi:10.2118/180439-MS.
  • Meng, E., S. Huang, Q. Huang, W. Fang, H. Wang, G. Leng, L. Wang, and H. Liang. 2021. A hybrid VMD-SVM model for practical streamflow prediction using an innovative input selection framework. Water Resources Management 35 (4):1321–37. doi:10.1007/s11269-021-02786-7.
  • Obiora, D. N., D. Gbenga, and G. Ogobiri. 2016. Reservoir characterization and formation evaluation of a “Royal onshore field”, Southern Niger Delta using geophysical well log data. Journal of the Geological Society of India 87 (5):591–600. doi:10.1007/s12594-016-0433-6.
  • Salehi, M. M., M. Rahmati, M. Karimnezhad, and P. Omidvar. 2017. Estimation of the non records logs from existing logs using artificial neural networks. Egyptian Journal of Petroleum 26 (4):957–68. doi:10.1016/j.ejpe.2016.11.002.
  • Shan, L., Y. Liu, M. Tang, M. Yang, and X. Bai. 2021. CNN-BiLSTM hybrid neural networks with attention mechanism for well log prediction. Journal of Petroleum Science and Engineering 205:108838. doi:10.1016/j.petrol.2021.108838.
  • Shobana, G., and M. Suguna. 2021. Sports prediction based on random forest algorithm. Advances in Materials Research. Select Proceedings of ICAMR 2019. Springer, Singapore, 993–1000.
  • Wang, F., H. Cheng, H. Dai, and H. Han. 2021. Freeway short-term travel time prediction based on lightgbm algorithm. IOP Conference Series: Earth and Environmental Science, vol. 638, 012029. IOP Publishing. doi:10.1088/1755-1315/638/1/012029.
  • Wang, J., J. Cao, and J. You. 2020. Logging curve reconstruction based on GRU neural network. Oil Geophysical Prospecting 55:510–20.
  • Wang, J., L. Liang, Q. Deng, P. Tian, and W. Tan. 2016. Research and application of log reconstruction based on multiple regression model. Lithologic Reservoirs 28(3):113–120.
  • Wang, X., T. Xia, L. Zhang, Z. Ding, S. He, and Y. Peng. 2021. The XGBoost and the SVM-based prediction models for bioretention cell decontamination effect. Arabian Journal of Geosciences 14 (1):1–11. doi:10.1007/s12517-021-07013-6.
  • Xia, H., X. Wei, Y. Gao, and H. Lv. 2019. Traffic prediction based on ensemble machine learning strategies with bagging and lightgbm. 2019 IEEE International Conference on Communications Workshops (ICC Workshops), 1–6. IEEE. doi:10.1109/ICCW.2019.8757058.
  • Pandis, N. 2016. Linear regression. American journal of orthodontics and dentofacial orthopedics 149(3): 431–434.
  • Zhang, D., C. Yuntian, and M. Jin. 2018. Synthetic well logs generation via recurrent neural networks. Petroleum Exploration and Development 45 (4):629–39. doi:10.1016/S1876-3804(18)30068-5.
  • Zhang, Y., R. Zhang, Q. Ma, Y. Wang, Q. Wang, Z. Huang, and L. Huang. 2020. A feature selection and multi-model fusion-based approach of predicting air quality. ISA Transactions 100:210–20. doi:10.1016/j.isatra.2019.11.023.
  • Zhong, J., X. Zhang, K. Gui, Y. Wang, H. Che, X. Shen, L. Zhang, Y. Zhang, J. Sun, W. Zhang, et al. 2021. Robust prediction of hourly PM2. 5 from meteorological data using LightGBM[J]. National Science Review 8 (10):nwaa307. doi:10.1093/nsr/nwaa307.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.