Shear Wave Travel Time Prediction using Well Log Filtering and Machine Learning
Abstract
Keywords
Full Text:
PDFReferences
M. Rajabi et al., "Predicting shear wave velocity from conventional well logs with deep and hybrid machine learning algorithms" J Pet Explor Prod Technol, vol. 13, no. 1, pp. 19-42, Jan. 2023, doi: 10.1007/s13202-022-01531-z.
N. Mohamadian, H. Ghorbani, D. A. Wood, M. Mehrad, S. Davoodi, S. Rashidi, A. Soleimanian, and A. K. Shahvand., "A geomechanical approach to casing collapse prediction in oil and gas wells aided by machine learning" J Pet Sci Eng, vol. 196, p. 107811, Jan. 2021, doi: 10.1016/j.petrol.2020.107811.
S. Parvizi, R. Kharrat, M. R. Asef, B. Jahangiry, and A. Hashemi, "Prediction of the Shear Wave Velocity from Compressional Wave Velocity for Gachsaran Formation" Acta Geophysica, vol. 63, no. 5, pp. 1231-1243, Oct. 2015, doi: 10.1515/acgeo-2015-0048.
S. Liu, Y. Zhao, and Z. Wang, "Artificial Intelligence Method for Shear Wave Travel Time Prediction considering Reservoir Geological Continuity" Math Probl Eng, vol. 2021, pp. 1-18, Mar. 2021, doi: 10.1155/2021/5520428.
M. Dehghani, S. Jahani, and A. Ranjbar, "Comparing the performance of machine learning methods in estimating the shear wave transit time in one of the reservoirs in southwest of Iran" Sci Rep, vol. 14, no. 1, p. 4744, Feb. 2024, doi: 10.1038/s41598-024-55535-2.
G. R. Pickett, "Acoustic Character Logs and Their Applications in Formation Evaluation" Journal of Petroleum Technology, vol. 15, no. 06, pp. 659-667, Jun. 1963, doi: 10.2118/452-PA.
J. P. Castagna, M. L. Batzle, and R. L. Eastwood, "Relationships between compressional"wave and shear"wave velocities in clastic silicate rocks" GEOPHYSICS, vol. 50, no. 4, pp. 571-581, Apr. 1985, doi: 10.1190/1.1441933.
T. M. Brocher, "Empirical Relations between Elastic Wavespeeds and Density in the Earth's Crust" Bulletin of the Seismological Society of America, vol. 95, no. 6, pp. 2081-2092, Dec. 2005, doi: 10.1785/0120050077.
X. Fu, Y. Wei, Y. Su, and H. Hu, "Shear Wave Velocity Prediction Based on the Long Short-Term Memory Network with Attention Mechanism" Applied Sciences, vol. 14, no. 6, p. 2489, Mar. 2024, doi: 10.3390/app14062489.
J. Liu, Z. Gui, G. Gao, Y. Li, Q. Wei, and Y. Liu, "Predicting Shear Wave Velocity Using a Convolutional Neural Network and Dual-Constraint Calculation for Anisotropic Parameters Incorporating Compressional and Shear Wave Velocities" Processes, vol. 11, no. 8, p. 2356, Aug. 2023, doi: 10.3390/pr11082356.
E. Alshdaifat, D. Alshdaifat, A. Alsarhan, F. Hussein, and S. M. F. S. El-Salhi, "The Effect of Preprocessing Techniques, Applied to Numeric Features, on Classification Algorithms' Performance" Data (Basel), vol. 6, no. 2, p. 11, Jan. 2021, doi: 10.3390/data6020011.
T. Johnson, A. J. Liu, S. Raza, and A. McGuire, "A Comparison of Modeling Preprocessing Techniques" Feb. 2023, [Online]. Available: http://arxiv.org/abs/2302.12042.
F. Branisa, "FILTERING OF WELL"LOG CURVES" GEOPHYSICS, vol. 39, no. 4, pp. 545-549, Aug. 1974, doi: 10.1190/1.1440447.
M. J. Duchesne and P. Gaillot, "Did you smooth your well logs the right way for seismic interpretation?" Journal of Geophysics and Engineering, vol. 8, no. 4, pp. 514-523, Dec. 2011, doi: 10.1088/1742-2132/8/4/004.
S. Soltani, M. Kordestani, and P. Karim Aghaee, "New estimation methodologies for well logging problems via a combination of fuzzy Kalman filter and different smoothers" J Pet Sci Eng, vol. 145, pp. 704-710, Sep. 2016, doi: 10.1016/j.petrol.2016.06.032.
J. Wang, J. Cao, and S. Yuan, "Shear wave velocity prediction based on adaptive particle swarm optimization optimized recurrent neural network" J Pet Sci Eng, vol. 194, p. 107466, Nov. 2020, doi: 10.1016/j.petrol.2020.107466.
H. Azami, K. Mohammadi, and B. Bozorgtabar, "An Improved Signal Segmentation Using Moving Average and Savitzky-Golay Filter" Journal of Signal and Information Processing, vol. 03, no. 01, pp. 39-44, 2012, doi: 10.4236/jsip.2012.31006.
S. R. Moosavi, J. Qajar, and M. Riazi, "A comparison of methods for denoising of well test pressure data" J Pet Explor Prod Technol, vol. 8, no. 4, pp. 1519-1534, Dec. 2018, doi: 10.1007/s13202-017-0427-y.
D. C. Stone, "Application of median filtering to noisy data" Can J Chem, vol. 73, pp. 1573-1581, 1997.
Z. Deng, X. Zhu, D. Cheng, M. Zong, and S. Zhang, "Efficient kNN classification algorithm for big data" Neurocomputing, vol. 195, pp. 143-148, Jun. 2016, doi: 10.1016/j.neucom.2015.08.112.
E. Scornet, "Tuning parameters in random forests" ESAIM Proc Surv, vol. 60, pp. 144-162, 2017, doi: 10.1051/proc/201760144.
T. Chen and C. Guestrin, "XGBoost" in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA: ACM, Aug. 2016, pp. 785-794. doi: 10.1145/2939672.2939785.
Equinor, "Disclosing all Volve data." Accessed: Sep. 19, 2024. [Online]. Available: https://www.equinor.com/energy/volve-data-sharing.
C. C. Aggarwal, "An Introduction to Outlier Analysis" in Outlier Analysis, Cham: Springer International Publishing, 2017, pp. 1-34. doi: 10.1007/978-3-319-47578-3_1.
P. Venkataanusha, Ch. Anuradha, Dr. P. S. R. Chandra Murty, and Dr. S. K. Chebrolu, "Detecting Outliers in High Dimensional Data Sets Using Z-Score Methodology" International Journal of Innovative Technology and Exploring Engineering, vol. 9, no. 1, pp. 48-53, Nov. 2019, doi: 10.35940/ijitee.A3910.119119.
V. Aggarwal, V. Gupta, P. Singh, K. Sharma, and N. Sharma, "Detection of Spatial Outlier by Using Improved Z-Score Test" in 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI), IEEE, Apr. 2019, pp. 788-790. doi: 10.1109/ICOEI.2019.8862582.
L. Sun et al., "Ensemble stacking rockburst prediction model based on Yeo-Johnson, K-means SMOTE, and optimal rockburst feature dimension determination" Sci Rep, vol. 12, no. 1, Dec. 2022, doi: 10.1038/s41598-022-19669-5.
D. Singh and B. Singh, "Investigating the impact of data normalization on classification performance" Appl Soft Comput, vol. 97, Dec. 2020, doi: 10.1016/j.asoc.2019.105524.
A. Jierula, S. Wang, T.-M. OH, and P. Wang, "Study on Accuracy Metrics for Evaluating the Predictions of Damage Locations in Deep Piles Using Artificial Neural Networks with Acoustic Emission Data" Applied Sciences, vol. 11, no. 5, p. 2314, Mar. 2021, doi: 10.3390/app11052314.
S. M. Malakouti, M. B. Menhaj, and A. A. Suratgar, "The usage of 10-fold cross-validation and grid search to enhance ML methods performance in solar farm power generation prediction" Clean Eng Technol, vol. 15, p. 100664, Aug. 2023, doi: 10.1016/j.clet.2023.100664.
D. Onalo, S. Adedigba, O. Oloruntobi, F. Khan, L. A. James, and S. Butt, "Data-driven model for shear wave transit time prediction for formation evaluation" J Pet Explor Prod Technol, vol. 10, no. 4, pp. 1429-1447, Apr. 2020, doi: 10.1007/s13202-020-00843-2.
Y. Zhang, H.-R. Zhong, Z.-Y. Wu, H. Zhou, and Q.-Y. Ma, "Improvement of petrophysical workflow for shear wave velocity prediction based on machine learning methods for complex carbonate reservoirs" J Pet Sci Eng, vol. 192, p. 107234, Sep. 2020, doi: 10.1016/j.petrol.2020.107234.
J. Wang, J. Cao, and S. Yuan, "Shear wave velocity prediction based on adaptive particle swarm optimization optimized recurrent neural network" J Pet Sci Eng, vol. 194, p. 107466, Nov. 2020, doi: 10.1016/j.petrol.2020.107466.
J. Liu, Z. Gui, G. Gao, Y. Li, Q. Wei, and Y. Liu, "Predicting Shear Wave Velocity Using a Convolutional Neural Network and Dual-Constraint Calculation for Anisotropic Parameters Incorporating Compressional and Shear Wave Velocities" Processes, vol. 11, no. 8, Aug. 2023, doi: 10.3390/pr11082356.
S. Gomaa, J. S. Shahat, T. M. Aboul-Fotouh, and S. Khaled, "Neural Network Model for Predicting Shear Wave Velocity Using Well Logging Data" Arab J Sci Eng, Jun. 2024, doi: 10.1007/s13369-024-09150-y.
L. O. Tedeschi and M. L. Galyean, "A practical method to account for outliers in simple linear regression using the median of slopes" Sci Agric, vol. 81, 2024, doi: 10.1590/1678-992x-2022-0209.
F. A. Tyas, M. Nurayuni, and H. Rakhmawati, "Optimasi Algoritma K-Nearest Neighbors Berdasarkan Perbandingan Analisis Outlier (Berbasis Jarak, Kepadatan, LOF)" Jurnal Nasional Teknik Elektro dan Teknologi Informasi, vol. 13, no. 2, pp. 108-115, May 2024, doi: 10.22146/jnteti.v13i2.9579.
R. Hossain and D. Timmer, "Machine Learning Model Optimization with Hyper Parameter Tuning Approach" 2021.
T. Chen and C. Guestrin, "XGBoost" in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA: ACM, Aug. 2016, pp. 785-794. doi: 10.1145/2939672.2939785.
DOI: https://doi.org/10.37905/euler.v12i2.29021
Refbacks
- There are currently no refbacks.
Copyright (c) 2024 Indra Rivaldi Siregar, Adhiyatma Nugraha, Anwar Fitrianto, Erfiani Erfiani, L.M. Risman Dwi Jumansyah

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi has been indexed by:
EDITORIAL OFFICE OF EULER : JURNAL ILMIAH MATEMATIKA, SAINS, DAN TEKNOLOGI |
![]() | Department of Mathematics, Faculty of Mathematics and Natural Science, Universitas Negeri Gorontalo Jl. Prof. Dr. Ing. B. J. Habibie, Tilongkabila, Kabupaten Bone Bolango 96554, Gorontalo, Indonesia |
![]() | Email: euler@ung.ac.id |
![]() | +6287743200854 (WhatsApp Only) |
![]() | Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi (p-ISSN: 2087-9393 | e-ISSN:2776-3706) by Department of Mathematics Universitas Negeri Gorontalo is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. Powered by Public Knowledge Project OJS. |