P-Wave Sonic Log Predictive Modeling with Optimal Artificial Neural Networks Topology
DOI:
https://doi.org/10.29304/jqcm.2021.13.3.855Keywords:
Artificial Neural Networks, Well-log prediction, P-wave, Sonic-waveAbstract
Given the financial challenges facing the oil and gas industry, the value of the information is considered relatively high; therefore, data science has been an alternative compensating tool. This study aimed to find an optimal neural network topology that provides an ideal data solution by studying neural network topology. Therefore, we trained different neural networks topologies in terms of the number of hidden neurons and layers. Volve oil field data is used in this study to predict the compressional sonic wave travel time. Optimal Neural Network topology found using five hidden layers and five hidden neurons while using a single layer with different numbers of hidden neurons was ineffective. The highest training and testing accuracy with a single hidden layer found 0.94 and 0.914, respectively. In contrast, it was found 0.947 and 0.934 with 50 hidden neurons and five hidden layers. Yet, increasing the number of hidden layers and hidden neurons is found to cause overfitting; therefore, only an optimal topology is a critical factor.
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References
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[4] H. AL - Sharoot, M., & A. Saheb, D. (2017). Forecasting of electricity power consumption in AL - Qadisiya province by using time series models & Artificial neural networks models. Journal of Al-Qadisiyah for Computer Science and Mathematics, 6(1), 9-26. Retrieved from https://qu.edu.iq/journalcm/index.php/journalcm/article/view/132
[5] Habib Al- Sharoot, M., & Yousif Abdoon, E. (2017). Prediction by using Artificial Neural Networks and Box-Jenkins methodologies: Comparison Study. Journal of Al-Qadisiyah for Computer Science and Mathematics, 9(2), Stat Page 1 - 16. https://doi.org/10.29304/jqcm.2017.9.2.325
[6] H. Singh, A. K. Pani, and H. K. Mohanta, "Quality monitoring in petroleum refinery with regression neural network: Improving prediction accuracy with appropriate design of training set," Measurement, vol. 134, pp. 698-709, 2019.
[7] M. Sabah, M. Talebkeikhah, D. A. Wood, R. Khosravanian, M. Anemangely, and A. Younesi, "A machine learning approach to predict drilling rate using petrophysical and mud logging data," Earth Science Informatics, vol. 12, no. 3, pp. 319-339, 2019.
[8] H. Cheng, X. Han, P. Zeng, H. Yu, E. Osipov, and V. Vyatkin, "ANN based Interwell Connectivity Analysis in Cyber-Physical Petroleum Systems," in 2019 IEEE 17th International Conference on Industrial Informatics (INDIN), 2019, vol. 1: IEEE, pp. 199-205.
[9] Abduljabbar Saad, I. (2018). An Efficient Classification Algorithms for Image Retrieval Based Color and Texture Features. Journal of Al-Qadisiyah for Computer Science and Mathematics, 10(1), Comp Page 42 - 53. https://doi.org/10.29304/jqcm.2018.10.1.350
[10] Mohammed, Y., & Saleh, E. (2020). Investigating the Applicability of Logistic Regression and Artificial Neural Networks in Predicting Breast Cancer. Journal of Al-Qadisiyah for Computer Science and Mathematics, 12(2), Math Page 63-. https://doi.org/10.29304/jqcm.2020.12.2.697
[11] Menzies, Tim, Ekrem Kocaguneli, Burak Turhan, Leandro Minku, and Fayola Peters. Sharing data and models in software engineering. Morgan Kaufmann, 2014.
[12] K.-L. Du and M. Swamy, "Elements of computational learning theory," in Neural networks and statistical learning: Springer, 2019, pp. 65-79.
[13] M. Zoveidavianpoor, A. Samsuri, and S. R. Shadizadeh, "Prediction of compressional wave velocity by an artificial neural network using some conventional well logs in a carbonate reservoir," Journal of Geophysics and Engineering, vol. 10, no. 4, p. 045014, 2013.
[14] A. Ogbamikhumi, S. Salami, and W. Uwadiae, "Neural Network Prediction of P-wave Log for Reservoir Characterization in the Niger Delta Basin," Nigerian Journal of Technological Development, vol. 17, no. 1, pp. 28-32, 2020.
[15] I. Bukar, M. Adamu, and U. Hassan, "A machine learning approach to shear sonic log prediction," in SPE Nigeria Annual International Conference and Exhibition, 2019: OnePetro.
[16] S. Liu, Y. Zhao, and Z. Wang, "Artificial Intelligence Method for Shear Wave Travel Time Prediction considering Reservoir Geological Continuity," Mathematical Problems in Engineering, vol. 2021, 2021.
[17] Bhamidipati, Subrahmanya Chandra. "Power generation from waste heat during oil production." PhD diss., Missouri University of Science and Technology, 2021.
[18] Johnston, David H., ed. Methods and applications in reservoir geophysics. Society of Exploration Geophysicists, 2010.
[19] S. Weisberg, "Yeo-Johnson power transformations," Department of Applied Statistics, University of Minnesota. Retrieved June, vol. 1, p. 2003, 2001.
[20] Dawson, C. (2018, November). Outlier Detection with One-Class SVMs. Retrieved from towards data science: https://towardsdatascience.com/outlier-detection-with-one-class-svms-5403a1a1878c.
[21] B. Schölkopf, R. C. Williamson, A. J. Smola, J. Shawe-Taylor, and J. C. Platt, "Support vector method for novelty detection," in NIPS, 1999, vol. 12: Citeseer, pp. 582-588.
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Published
2021-10-14
How to Cite
Alhelfia, L. M., Ali, H. M., & Ahmed, S. H. (2021). P-Wave Sonic Log Predictive Modeling with Optimal Artificial Neural Networks Topology. Journal of Al-Qadisiyah for Computer Science and Mathematics, 13(3), Comp Page 142 – 154. https://doi.org/10.29304/jqcm.2021.13.3.855
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Computer Articles
