Implementation of machine learning methods in predicting failures in electrical submersible pump machines

Singgih Saptadi; Achmad Widodo; Muhammad Fitrah Athaillah; Muhammad Farid Ayyasyi

doi:10.31893/multiscience.2025137

Keywords:

Abstract

The Electric Submersible Pump (ESP) is one of the artificial lift tools widely used in oil and gas wells. Approximately 25% of oil and gas wells have been equipped with ESP. The ESP unit excels in lifting fluid rates much larger than most other types of artificial lift. The purpose of this research is to develop a machine learning model that can predict the likelihood of ESP installation failure. By making predictions at the early stage, necessary actions can be prepared to prevent and anticipate these issues. Thus, the reduction in the amount of produced fluid can be minimized. The model in this study uses 8 input parameters, namely, motor Ampere, Frequency, Pump Intake Pressure, Temperature Motor, Output Volt, Pump Discharge Pressure, Input Voltage, and Motor Horse Power (HP). There are also 8 failure classifications for which the model will be created. The initial stage involves collecting and cleaning raw data and processing it to detect anomalies. Once the data is processed, it proceeds to the model formation and model validation stage. The method used for the model is based on the comparison of two methods, namely, decision tree and k-nearest neighbor. The output of the research is a dashboard that can display visualizations of data and the prediction results from the created model. Millions of data points are used to create a database consisting of 77 wells over the past 2 years. The model developed generates several types of failures that can be read. These failures have their own characteristic parameters. The model obtained has an accuracy rate above 90%. The output of this model includes the most likely failure prediction based on the latest input data, thus effectively addressing well problems, warning of impending failures, reducing failures, and assisting in scheduling ESP repairs and maintenance.
References
1. Abdalla, R., Nikolaev, D., Gönzi, D., Manasipov, R., Schweiger, A., & Stundner, M. (2023). Deep Insight into Electrical Submersible Pump Maintenance: A Predictive Approach with Deep Learning. SPE Offshore Europe Conference & Exhibition.
2. Abdelaziz, M., Lastra, R., & Xiao, J. J. (2017). ESP Data Analytics: Predicting Failures for Improved Production Performance. Abu Dhabi International Petroleum Exhibition & Conference.
3. Adesanwo, M., Bello, O., Denney, T., & Lazarus, S. (2016). Prescriptive-Based Decision Support System for Online Real-Time Electrical Submersible Pump Operations Management. SPE Intelligent Energy International Conference and Exhibition.
4. Al-Ballam, S., Karami, H., & Devegowda, D. (2023). A Hybrid Physical and Machine Learning Model to Diagnose Failures in Electrical Submersible Pumps. SPE/IADC Middle East Drilling Technology Conference and Exhibition. Abu Dhabi.
5. Bermudez, F., Carvajal, G. A., Moricca, G., Dhar, J., Adam, F. M., Al-Jasmi, A., . . . Nasr, H. (2014). Fuzzy Logic Application to Monitor and Predict Unexpected Behavior in Electric Submersible Pumps (Part of the KwIDF Project). SPE Intelligent Energy Conference & Exhibition.
6. Bermudez, F., Nahhas, N. A., Yazdani, H., LeTan, M., & Shono, M. (2021). Unlocking the Potential of Electrical Submersible Pumps: The Successful Testing and Deployment of a Real-Time Artificially Intelligent System, for Failure Prediction, Run Life Extension, and Production Optimization . Abu Dhabi International Petroleum Exhibition & Conference.
7. Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research, 321–357.
8. Erlin, Desnelita, Y., Nasution, N., Suryati, L., & Zoromi, F. (2022). Impact of SMOTE on Random Forest Classifier Performance based onImbalanced Data. Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer.
9. Fang, M., Raveendran, R., & Huang, B. (2021). Real-time Performance Monitoring of Electrical Submersible Pumps in SAGD Process. IFAC-PapersOnLine Volume 54, Issue 11, 139-144.
10. Grasiian, D., Bahatem, M., Scott, T., & Olsen, D. (2017). Application of a Fuzzy Expert System to Analyze and Anticipate ESP Failure Modes. Society of Petroleum Engineers.
11. Karsito, & Susanti, S. (2019). Klasifikasi Kelayakan Peserta Pengajuan Kredit Rumah dengan Algoritma Naive Bayes di Perumahan Azzura Residencia. Jurnal Teknologi Pelita Bangsa.
12. Lastra, R. A., & Xiao, J. (2022). Machine Learning Engine for Real-Time ESP Failure Detection and Diagnostics. SPE Middle East Artificial Lift Conference and Exhibition.
13. Meddaoui, A., Hachmoud, A., & Hain, M. (2024). Advanced ML for predictive maintenance: a case study on remaining useful life prediction and reliability enhancement. The International Journal of Advanced Manufacturing Technology, 323–335.
14. Mello, L. H., Oliveira-Santos, T., Varejão, F. M., Ribeiro, M. P., & Rodrigues, A. L. (2022). Ensemble of metric learners for improving electrical submersible pump fault diagnosis. Journal of Petroleum Science and Engineering.
15. Melo, R. A., Worth, D. J., & Swaffield, S. (2023). Assessment of Real-Time ESP Failure Prediction Using Digital Twin, Machine Learning and Damage Modelling . SPE Gulf Coast Section - Electric Submersible Pumps Symposium.
16. Nadiah, Soim, S., & Sholihin. (2022). Implementation of Decision Tree Algorithm Machine Learning in Detecting Covid-19 Virus Patients Using Public Datasets. Indonesian Journal of Artificial Intelligence and Data Mining (IJAIDM), 37 – 43.
17. Nasteski, V. (2017). An overview of the supervised machine learning methods. Horizons, 51-62.
18. Nguyen, N., Subervie, Y.-M., & Chong, J. (2020). Real-Time Automated Event Detection Framework for Electrical Submersible Pumps. the Abu Dhabi International Petroleum Exhibition & Conference. Abu Dhabi.
19. Panbarasan, M., Sankar, S., Venkateshbabu, S., & Balasubramanian, A. (2022). Characterization and performance enhancement of electrical submersible pump (ESP) using artificial intelligence (AI). Materials Today: Proceedings, 6864-6872.
20. Salem, A. M., Yakoot, M., & Mahmoud, O. (2022). A novel machine learning model for autonomous analysis and diagnosis of well integrity failures in artificial-lift production systems. Advances in Geo-Energy Research, 123-142.
21. Santoso, H. B., Vidrianto, M., Rahmawati, S. D., & Siagian, U. W. (2020). Predicting Failure in Electric Submersible Pump by Utilizing Machine Learning Based on Real-Time Sensor Data. Proceeding Simposium IATMI 2020.
22. Sharma, A., Songchitruksa, P., & Sinha, R. R. (2022). Integrating Domain Knowledge with Machine Learning to Optimize Electrical Submersible Pump Performance . SPE Canadian Energy Technology Conference.
23. Silvia, S., & Furlong, T. (2023). Case Study: Predicting Electrical Submersible Pump Failures Using Artificial Intelligence and Physics-Based Hybrid Models . SPE Gulf Coast Section - Electric Submersible Pumps Symposium.
24. Singh, B. K., Verma, K., & Thoke, A. S. (2015). Investigations on impact of feature normalization techniques on classifier's performance in breast tumor classification. International Journal of Computer Applications, 116.
25. Suyal, M., & Goyal, P. (2022). A Review on Analysis of K-Nearest Neighbor Classification Machine Learning Algorithms based on Supervised Learning. International Journal of Engineering Trends and Technology.
26. Tang, X., Wu, Z., Liu, W., Tian, J., & Liu, L. (2023). Exploring effective ways to increase reliable positive samples for machine learning-based urban waterlogging susceptibility assessments. Journal of Environmental Management.
27. Xiao, J. J., Shepler, R., Windiarto, Y., Parkinson, S., Fox, R., & Matlack, B. (2018). Development and Field Test of an Electric-Submersible-Pump Reliable-Power-Delivery System. SPE Prod & Oper 33, 449–458.
28. Zhen, X., Ning, Y., Du, W., Huang, Y., & Vinnem, J. E. (2023). An interpretable and augmented machine-learning approach for causation analysis of major accident risk indicators in the offshore petroleum industry . Process Safety and Environmental Protection, 922-933.

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to cite

Saptadi, S., Widodo, A., Athaillah, M. F., & Ayyasyi, M. F. (2024). Implementation of machine learning methods in predicting failures in electrical submersible pump machines. Multidisciplinary Science Journal, 7(3), 2025137. https://doi.org/10.31893/multiscience.2025137

[1] Abdalla, R., Nikolaev, D., Gönzi, D., Manasipov, R., Schweiger, A., & Stundner, M. (2023). Deep Insight into Electrical Submersible Pump Maintenance: A Predictive Approach with Deep Learning. SPE Offshore Europe Conference & Exhibition.

[2] Abdelaziz, M., Lastra, R., & Xiao, J. J. (2017). ESP Data Analytics: Predicting Failures for Improved Production Performance. Abu Dhabi International Petroleum Exhibition & Conference.

[3] Adesanwo, M., Bello, O., Denney, T., & Lazarus, S. (2016). Prescriptive-Based Decision Support System for Online Real-Time Electrical Submersible Pump Operations Management. SPE Intelligent Energy International Conference and Exhibition.

[4] Al-Ballam, S., Karami, H., & Devegowda, D. (2023). A Hybrid Physical and Machine Learning Model to Diagnose Failures in Electrical Submersible Pumps. SPE/IADC Middle East Drilling Technology Conference and Exhibition. Abu Dhabi.

[5] Bermudez, F., Carvajal, G. A., Moricca, G., Dhar, J., Adam, F. M., Al-Jasmi, A., . . . Nasr, H. (2014). Fuzzy Logic Application to Monitor and Predict Unexpected Behavior in Electric Submersible Pumps (Part of the KwIDF Project). SPE Intelligent Energy Conference & Exhibition.

[6] Bermudez, F., Nahhas, N. A., Yazdani, H., LeTan, M., & Shono, M. (2021). Unlocking the Potential of Electrical Submersible Pumps: The Successful Testing and Deployment of a Real-Time Artificially Intelligent System, for Failure Prediction, Run Life Extension, and Production Optimization . Abu Dhabi International Petroleum Exhibition & Conference.

[7] Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research, 321–357.

[8] Erlin, Desnelita, Y., Nasution, N., Suryati, L., & Zoromi, F. (2022). Impact of SMOTE on Random Forest Classifier Performance based onImbalanced Data. Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer.

[9] Fang, M., Raveendran, R., & Huang, B. (2021). Real-time Performance Monitoring of Electrical Submersible Pumps in SAGD Process. IFAC-PapersOnLine Volume 54, Issue 11, 139-144.

[10] Grasiian, D., Bahatem, M., Scott, T., & Olsen, D. (2017). Application of a Fuzzy Expert System to Analyze and Anticipate ESP Failure Modes. Society of Petroleum Engineers.

[11] Karsito, & Susanti, S. (2019). Klasifikasi Kelayakan Peserta Pengajuan Kredit Rumah dengan Algoritma Naive Bayes di Perumahan Azzura Residencia. Jurnal Teknologi Pelita Bangsa.

[12] Lastra, R. A., & Xiao, J. (2022). Machine Learning Engine for Real-Time ESP Failure Detection and Diagnostics. SPE Middle East Artificial Lift Conference and Exhibition.

[13] Meddaoui, A., Hachmoud, A., & Hain, M. (2024). Advanced ML for predictive maintenance: a case study on remaining useful life prediction and reliability enhancement. The International Journal of Advanced Manufacturing Technology, 323–335.

[14] Mello, L. H., Oliveira-Santos, T., Varejão, F. M., Ribeiro, M. P., & Rodrigues, A. L. (2022). Ensemble of metric learners for improving electrical submersible pump fault diagnosis. Journal of Petroleum Science and Engineering.

[15] Melo, R. A., Worth, D. J., & Swaffield, S. (2023). Assessment of Real-Time ESP Failure Prediction Using Digital Twin, Machine Learning and Damage Modelling . SPE Gulf Coast Section - Electric Submersible Pumps Symposium.

[16] Nadiah, Soim, S., & Sholihin. (2022). Implementation of Decision Tree Algorithm Machine Learning in Detecting Covid-19 Virus Patients Using Public Datasets. Indonesian Journal of Artificial Intelligence and Data Mining (IJAIDM), 37 – 43.

[17] Nasteski, V. (2017). An overview of the supervised machine learning methods. Horizons, 51-62.

[18] Nguyen, N., Subervie, Y.-M., & Chong, J. (2020). Real-Time Automated Event Detection Framework for Electrical Submersible Pumps. the Abu Dhabi International Petroleum Exhibition & Conference. Abu Dhabi.

[19] Panbarasan, M., Sankar, S., Venkateshbabu, S., & Balasubramanian, A. (2022). Characterization and performance enhancement of electrical submersible pump (ESP) using artificial intelligence (AI). Materials Today: Proceedings, 6864-6872.

[20] Salem, A. M., Yakoot, M., & Mahmoud, O. (2022). A novel machine learning model for autonomous analysis and diagnosis of well integrity failures in artificial-lift production systems. Advances in Geo-Energy Research, 123-142.

[21] Santoso, H. B., Vidrianto, M., Rahmawati, S. D., & Siagian, U. W. (2020). Predicting Failure in Electric Submersible Pump by Utilizing Machine Learning Based on Real-Time Sensor Data. Proceeding Simposium IATMI 2020.

[22] Sharma, A., Songchitruksa, P., & Sinha, R. R. (2022). Integrating Domain Knowledge with Machine Learning to Optimize Electrical Submersible Pump Performance . SPE Canadian Energy Technology Conference.

[23] Silvia, S., & Furlong, T. (2023). Case Study: Predicting Electrical Submersible Pump Failures Using Artificial Intelligence and Physics-Based Hybrid Models . SPE Gulf Coast Section - Electric Submersible Pumps Symposium.

[24] Singh, B. K., Verma, K., & Thoke, A. S. (2015). Investigations on impact of feature normalization techniques on classifier's performance in breast tumor classification. International Journal of Computer Applications, 116.

[25] Suyal, M., & Goyal, P. (2022). A Review on Analysis of K-Nearest Neighbor Classification Machine Learning Algorithms based on Supervised Learning. International Journal of Engineering Trends and Technology.

[26] Tang, X., Wu, Z., Liu, W., Tian, J., & Liu, L. (2023). Exploring effective ways to increase reliable positive samples for machine learning-based urban waterlogging susceptibility assessments. Journal of Environmental Management.

[27] Xiao, J. J., Shepler, R., Windiarto, Y., Parkinson, S., Fox, R., & Matlack, B. (2018). Development and Field Test of an Electric-Submersible-Pump Reliable-Power-Delivery System. SPE Prod & Oper 33, 449–458.

[28] Zhen, X., Ning, Y., Du, W., Huang, Y., & Vinnem, J. E. (2023). An interpretable and augmented machine-learning approach for causation analysis of major accident risk indicators in the offshore petroleum industry . Process Safety and Environmental Protection, 922-933.

Abstract

References

How to cite