Data Driven Approach to Failure Prediction for Electrical Submersible Pump Systems
Abstract
This paper presents a data driven approach for failure prediction for Electrical Submersible Pumps (ESP). ESP system is well known as an effective artificial lift method which has been applied to about 20 percent of almost one million wells worldwide. Well failures lead to production loss and generally the repair cost for an ESP is usually much higher than those of other artificial lift systems, thus predicting ESP failures before they occur will be valuable. We apply advanced machine learning techniques for predicting ESP failures using electrical and frequency data from the field. Data from real-world assets using ESP systems is analyzed to learn examples of normal well and failure conditions. A generalized Support Vector Machine (SVM) trained with a set of selected features is developed and this approach is tested on real world data. Our results show that this approach works well based on feedback from subject matter experts on the results.
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Many examples exist where an entirely data-driven or hybrid model helps optimize pump regimes, prevent unexpected failures, and save on maintenance-on-schedule.
“There are many examples when an entirely data-driven or a hybrid model containing physics-driven and data-driven math helps optimize the regimes, prevent unexpected failures, and save on maintenance-on-schedule [ 36 , 37 ].”
From Artificial intelligence in oil and gas upstream: Trends, challenges, and scenarios for the future (2021) · cites this paper as [37]
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Artificial intelligence in oil and gas upstream: Trends, challenges, and scenarios for the future
Koroteev, D., & Tekic, Z. (2021) · Energy and AI · doi:10.1016/j.egyai.2020.100041 · Cited by 285 · see citing works
- Authors
- Guo, D., Raghavendra, C. S., Yao, K.-T., Harding, M., Anvar, A., & Patel, A.
- Affiliations
- University of Southern California (US)
- Chevron (United States) (US)
- Venue
- SPE Western Regional Meeting
- Publisher
- SPE
- Published
- 2015-04-27
- OpenAlex
- W2019920211
- Cited by
- 49
- References
- 7
- Citations by year
- 2026: 2 · 2025: 6 · 2024: 10 · 2023: 3 · 2022: 11 · 2021: 4 · 2020: 6 · 2019: 1 · 2018: 5 · 2016: 1
- FWCI
- 3.54
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- SPE Western Regional Meeting (publishedVersion)
- Topics
- Physical Sciences › Engineering › Ocean Engineering › Oil and Gas Production Techniques
- Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
- Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
- Keywords
- electric submersible pumps, failure prediction, condition monitoring, artificial lift, support vector machine, feature selection, well maintenance, predictive maintenance
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Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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