Machine Learning-Based Enthalpy Prediction Using Geochemical Monitoring Data in Indonesia Geothermal Fields

References

1. Duplyakin, D., Siller, D. L., Johnston, H., Beckers, K., & Martin, M. (2020). Using Machine Learning to Predict Future Temperature Outputs in Geothermal Systems.*Geothermal Resources Council Transactions, 44*, 1300. Retrieved March 6, 2023. from https://www.geothermal-library.org/index.php?mode=pubs&action=view&record=1034309
   [Google Scholar]
2. Gudmundsdottir, H., & Horne, R. N. (2020). Prediction Modeling for Geothermal Reservoirs Using Deep Learning. *45th Workshop on Geothermal Reservoir Engineering.* Stanford, California: Stanford University.
   [Google Scholar]
3. Haklidir, F. S., & Haklidir, M. (2019). Prediction of Reservoir Temperatures Using Hydrogeochemical Data, Western Anatolia Geothermal Systems (Turkey): A Machine Learning Approach. *Natural Resources Research*. doi:https://doi.org/10.1007/s11053-019-09596-0
   [Google Scholar]
4. Hirtz, P., and Lovekin, J.: Tracer Dilution Measurements for Two-Phase Geothermal Production: Comparative Testing and Operating Experience, Proceedings of the World Geothermal Congress, vol. 3, International Geothermal Association, Florence, Italy (1995).
   [Google Scholar]
5. Ibrahim, B., Konduah, J. O., & Ahenkorah, I. (2022). Predicting reservoir temperature of geothermal systems in West Anatolia, Turkey a focus on predictive performance and explainability of machine learning models. *Geothermics*. Retrieved from https://ssrn.com/abstract=4239319
   [Google Scholar]
6. Kshirsagar, A., & Sanghavi, P. (2022). Geothermal, oil, and gas well subsurface temperature prediction employing machine learning. *47th Workshop on Geothermal Reservoir Engineering, February 7–9, 2022.* California: Stanford University.
   [Google Scholar]
7. Okoroafor, E. R., Smith, C. M., Ochie, K. I., Nwosu, C. J., Gudmundsdottir, H., & Aljubran, M. (. (2022, June). Machine learning in subsurface geothermal energy: Two decades in review. *Geothermics, 102*. doi:https://doi.org/10.1016/j.geothermics.2022.102401
   [Google Scholar]
8. Pugliese, R., Regondi, S., & Marini, R. (2021, December). Machine learning-based approach: global trends, research directions, and regulatory standpoints. *Data Science and Management, 4*, 19–29. doi:https://doi.org/10.1016/j.dsm.2021.12.002
   [Google Scholar]
9. Qin, Z., Jiang, A., Faulder, D., Cladouhos, T. T., & Jafarpour, B. (2022). Physics-Guided Deep Learning for Prediction of Geothermal Reservoir Performance. *47th Workshop on Geothermal Reservoir Engineering.* Stanford, California: Stanford University.
   [Google Scholar]
10. Shah, K., Mehta, A., & Shah, M. (2022, February 27). Prediction of geothermal wells location and hot water temperature using machine learning. *Petroleum Research*. doi:https://doi.org/10.1016/j.ptlrs.2022.02.002
    [Google Scholar]
11. Shahdi, A., Lee, S., Karpatne, A., & Nojabaei, B. (2021). Exploratory analysis of machine learning methods in predicting subsurface temperature and geothermal gradient of Northeastern United States. *Geothermal Energy*. doi:https://doi.org/10.1186/s40517-021-00200-4
    [Google Scholar]
12. Shi, Y., Song, X., & Li, G. (2020). Productivity Prediction of a Geothermal System Using a LSTM Neural Network. *GRC Transactions, 44*.
    [Google Scholar]
13. Sutarmin, & Daud, Y. (2021). Subsurface temperature prediction in geothermal field with neural network using 3D MT data inversion and borehole temperature data. *AIP Conference Proceedings 2320.* doi:https://doi.org/10.1063/5.0039616
    [Google Scholar]
14. Suzuki, A., Fukui, K.-i., Onodera, S., Ishizaki, J., & Hashida, T. (2022, March 11). Data-Driven Geothermal Reservoir Modeling: Estimating Permeability Distributions by Machine Learning.
    [Google Scholar]