Artificial Intelligence Applied in Sucker Rod Pumping Wells: Intelligent Dynamometer Card Generation, Diagnosis, and Failure Detection Using Deep Neural Networks
- Yi Peng (PetroChina Riped)
- Document ID
- Society of Petroleum Engineers
- SPE Annual Technical Conference and Exhibition, 30 September - 2 October, Calgary, Alberta, Canada
- Publication Date
- Document Type
- Conference Paper
- 2019. Society of Petroleum Engineers
- Failure Detection, Convolutional Neural Networks, Dynamometer Card, Artificial Intelligent, Artificial Lift
- 15 in the last 30 days
- 402 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 5.00|
|SPE Non-Member Price:||USD 28.00|
For most of the mature fields, the oil well operation and maintenance expenditures continue to put financial pressure on the operators in the low oil price period. Digital oilfields and artificial intelligent technology are the major areas invested to fight for declining oil production and increasing cost. This paper provides a novel artificial intelligent method to monitoring and diagnose the sucker-rod pumping wells using deep learning algorithms.
Traditional method using load and displacement sensors to measure the dynamometer card needs large investment on the equipment installation and maintenance. We build a general model that generates the dynamometer card from electrical parameter using state-of-art deep learning algorithms. The deep learning algorithms can analyze the relationships between the electrical data and corresponding dynamometer card in different conditions, which is very hard for human being to detect. In addition, we build another automated diagnosis deep learning model from thousands of dynamometer cards labeled with different classifications.
We have already tested these newly developed artificial intelligent models on hundreds of sucker rod pumped wells in different oilfields in PetroChina. The field test results show that the dynamometer cards generated from electrical data have above 90% similarity compared to the real dynamometer, which meet the requirement for well diagnosis. The card generation model is stable and prevents the disturbance of hostile environment change and sensor failures. The automated diagnosis model also proved to be a good substitute to the conventional software, with above 95% prediction accuracy. The automated diagnosis model reduces the liability and uncertainty of traditional diagnosis software and can integrated with the former dynamometer card generation model to fulfill well monitoring and diagnosis automatically without any physical model based calculations.
These models developed with artificial intelligent technology will be important components in the "Intelligent Fields". They can also be embedded in the IIoT edge computing machines for automatic diagnosis and control. For ultra-low production wells and the newly producing wells utilized this method, operator can save expenditure and human resources tremendously.
|File Size||1 MB||Number of Pages||12|
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. 2012. ImageNet classification with deep convolutional neural networks. In Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 1 (NIPS'12), F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger (Eds.), Vol. 1. Curran Associates Inc., USA, 1097–1105.