Video: ESP Data Analytics: Use of Deep Autoencoders for Intelligent Surveillance of Electric Submersible Pumps
- Olabode Afolabi Alamu (Shell Global Solutions, US, Inc) | Deval A. Pandya (Shell Global Solutions, US, Inc) | Oscar Warner (Shell Global Solutions, UK, Ltd.) | Igor Debacker (Shell Brasil Petróleo Ltda.)
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- Offshore Technology Conference
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- 2020. Copyright is retained by the author. This document is distributed by OTC with the permission of the author. Contact the author for permission to use material from this document.
- 3 Production and Well Operations, 6.1.5 Human Resources, Competence and Training, 7 Management and Information, 1.6 Drilling Operations, 7.6.6 Artificial Intelligence, 1.6 Drilling Operations, 3.1 Artificial Lift Systems, 7.6 Information Management and Systems, 3.1.2 Electric Submersible Pumps, 6 Health, Safety, Security, Environment and Social Responsibility, 6.1 HSSE & Social Responsibility Management
- Electric Submersible Pumps, Deep learning, Predictive maintenance, Subsea production, Artificial Intelligence
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Electric Submersible Pump (ESP) account for over 60% of artificial lift methods used globally and contribute significantly to the CAPEX and OPEX of a project. They tend to be the least reliable component in the system with an average life-span of 2 years. This paper demonstrates how artificial intelligence was used to unlock insights from sensor data around an ESP to understand the operating conditions which lead to a trip and failure of these systems.
Autoencoders were used for the detection of anomalous behavior in an ESP and the determination of the root cause of an anomalous event. Autoencoders are neural networks trained to reconstruct input data. They have an encoding and decoding section, the encoder compresses the input vector, while the decoder reconstructs the original input from the compressed vector. This process allows the network to understand the patterns in a dataset. We trained the network on stable operating data from a 2-years historical data dump of 97 sensors. This allowed the model to understand the patterns of stability in an ESP.
The autoencoder was developed using the Python programming language along with the Keras deep learning framework. It had 7 layers with the exponential linear unit as the activation function for training. During reconstruction, the autoencoder never produces a perfect reconstruction of input data, it, however, performs a good reconstruction on data similar to what it was trained on. In our case, the model reconstructs stable data well and struggles with unstable data. The reconstruction error is used to distinguish a normal event from an anomalous event because it increases prior to an event and reduces as the system returns to stability. During the historical time period, the ESP experienced 5 major trips, three of them were due to gas locks while the other two were due to electrical issues. The model was able to detect the gas locks on average 5 hrs in advance and electrical issues several days in advance before the actual events. The top ten sensors responsible for each event were determined based on the relative magnitude of the individual sensor reconstruction errors, the validity of this output was confirmed by the Subject Matter Expert.
Autoencoders can make non-linear correlation between features in a dataset and have been used for anomaly detection in images and other fields, this paper demonstrates their usefulness in intelligent surveillance of ESPs. This solution is currently used for near real-time intelligent surveillance of ESPs with the ability to send out email notifications whenever any sensor strays away from stability.