Unlocking the Power of Artificial Intelligence: Building Digital Twins with Classification Algorithms for Optimized Geothermal Drilling


Abstract views: 22 / PDF downloads: 27

Authors

  • Orkun TEKE Manisa Celal Bayar University

Keywords:

Geothermal Energy, Digital Twin, Artificial Intelligence, Drilling, Classification Algorithms

Abstract

Geothermal energy has emerged as a promising renewable energy source due to its
sustainability for long-term power generation. Effective drilling practices are crucial for the successful
utilization of geothermal resources, as they directly impact the productivity and operational efficiency of
geothermal systems. However, optimizing drilling operations in the geothermal sector presents unique
challenges due to the complex subsurface conditions and the need for continuous monitoring and
optimization. In recent years, the integration of artificial intelligence (AI) technologies has shown great
promise in improving various industrial processes. The energy sector, including geothermal energy, has
started leveraging AI techniques to enhance operational efficiency, reduce costs, and minimize
environmental impacts. Among the diverse AI methods, machine learning algorithms have gained
significant attention for their ability to analyze large datasets, extract patterns, and generate predictive
models. This paper focused on the potential benefits and challenges of utilizing AI, particularly the
classification algorithms, in the context of geothermal drilling. Such as Extreme Gradient Boosting
Machine (XGBoost), Light Gradient Boosting Machine (LightGBM) are a powerful machine learning
algorithms known for its effectiveness in handling structured datasets and achieving high predictive
accuracy.

Downloads

Download data is not yet available.

Author Biography

Orkun TEKE, Manisa Celal Bayar University

XRLab, Turkey

References

United States Department of Energy. (n.d.). Utah FORGE dataset. Retrieved from https://purl.stanford.edu/9d771yv6834. Reaching time: 28/06/2023

CHEN S., KHAN S., Semi parametric Estimation Of a Partially Linear Model, Econometric Theory, 567-590(2001)

Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.

Ho, T. K. (1995). Random decision forests. In Proceedings of 3rd international conference on document analysis and recognition (Vol. 1, pp. 278–282)

Mucherino, A., Papajorgji, P.J., Pardalos, P.M. (2009). k-Nearest Neighbor Classification. In: Data Mining in Agriculture. Springer Optimization and Its Applications, vol 34. Springer, New York, NY. https://doi.org/10.1007/978-0-387-88615-2_4

Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785–794). New York, NY, USA: ACM. https://doi.org/10.1145/2939672.2939785

Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., … Liu, T.-Y. (2017). Lightgbm: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems, 30, 3146–3154.

Bicheng, Yan., Manojkumar, Gudala., Shuyu, Sun. (2023). Geothermal Reservoir Optimization Empowered by a Generalized Thermal Decline Model and Deep Learning. doi: 10.2118/214394-ms

Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32. https://doi.org/10.1023/A:1010933404324

Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785–794). ACM. https://doi.org/10.1145/2939672.2939785

Zhang, Z.; Wei, Z.; Court, S.; Yang, L.; Wang, S.; Thirunavukarasu, A.; Zhao, Y. A Review of Digital Twin Technologies for Enhanced Sustainability in the Construction Industry. Buildings 2024, 14, 1113. https://doi.org/10.3390/buildings14041113

Zhen, Xu., Bicheng, Yan., Manojkumar, Gudala., Zeeshan, Tariq. (2023). A Robust General Physics-Informed Machine Learning Framework for Energy Recovery Optimization in Geothermal Reservoirs. doi: 10.2118/214352-ms

Arnaud, Regis, Kamgue, Lenwoue., Zhonghui, Li., Pengjie, Hu. (2023). Recent Advances and Challenges of the Application of Artificial Intelligence to Predict Wellbore Instabilities during Drilling Operations. Spe Drilling & Completion, 1-18. doi: 10.2118/215830-pa

Michael, Mendez., Ramadan, Ahmed., Hamidreza, Karami., Mustafa, S., Nasser., Ibnelwaleed, A., Hussein., Sergio, Javier, Ramirez, Garcia., Andres, Gonzalez. (2023). Applications of Machine Learning Methods to Predict Hole Cleaning in Horizontal and Highly Deviated Wells. doi: 10.2118/212912-ms

Robello, Samuel., K., Kumar. (2023). Artificial Well Engineering Intelligence (AweI): Is It Drilling Engineer's Dream or Driller's Nightmare?. doi: 10.2118/213686-ms

Downloads

Published

2024-06-27

How to Cite

TEKE, O. (2024). Unlocking the Power of Artificial Intelligence: Building Digital Twins with Classification Algorithms for Optimized Geothermal Drilling. International Journal of Advanced Natural Sciences and Engineering Researches, 8(5), 52–59. Retrieved from https://as-proceeding.com/index.php/ijanser/article/view/1884

Conference Proceedings Volume

Section

Articles