Improved Data Mining for Production Diagnosis of Gas Wells with Plunger Lift through Dynamic Simulations
- Jianjun Zhu (University of Tulsa) | Guangqiang Cao (PetroChina Company Ltd.) | Wei Tian (PetroChina Company Ltd.) | Qingqi Zhao (University of Tulsa) | Haiwen Zhu (University of Tulsa) | Jie Song (PetroChina Company Ltd.) | Jianlin Peng (University of Tulsa) | Zimo Lin (University of Tulsa) | Hong-Quan Zhang (University of Tulsa)
- 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
- Gas deliquification, Dynamic simulation, Plunger lift, Data mining, Multiphase flow
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- 185 since 2007
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Plunger lift has been widely used in unconventional gas wells to remove liquid accumulation from the well.. Production surveillance provides large amount of data of production process and normal and abnormal operations, which can be used in machine learning (ML) and Artificial Intelligence (AI) to develop algorithms for anomaly diagnosis and operation optimization. However, in the surveillance data the majority is related to daily operation and the data of failure cases are rare. Also the failure cases may not be repeatable and many failure case signatures are not available until they happen. Large data size of anomaly cases are needed to improve the ML model accuracy. Dynamic simulation of the plunger lift process offers an alternative way to generate synthetic data on the specified anomalies to be used to train the ML model. It also helps better understand the trends reflected in the surveillance data and their root causes.
From the available surveillance data of gas wells equipped with plunger lift, the simultaneous measurements of different parameters at different points in a production system with normal and abnormal occurrences can be analyzed and the correspondent trends/signatures can be identified. The typical signatures that conform to pre-determined anomalous patterns can be obtained. Using a commercial transient multiphase flow simulator, the actual field data of tubing/casing pressures can be matched through a tuning process. Trial-and-error is needed to improve the dynamic plunger lift model so that a good agreement with the production data can be achieved by adjusting the reservoir performance, plunger parameters or surface pipeline boundary conditions. Following the validation under different flow conditions, synthetic datasets for various operational and flow conditions can be generated by performing parametric studies. Unlike the field data, the synthetic data from the dynamic simulations mainly comprise anomaly signatures (e.g. tubing rupture, missed arrival of plunger, etc.), which can be added to the ML data pool to reduce the data covariance and increase independency.
|File Size||1 MB||Number of Pages||14|
Zhu, H., Zhu, J., Rutter, R., Zhang, J. and Zhang, H.Q., 2018, July. Sand Erosion Model Prediction, Selection and Comparison for Electrical Submersible Pump (ESP) using CFD Method. In ASME 2018 5th Joint US-European Fluids Engineering Division Summer Meeting (pp. V003T17A003–V003T17A003). American Society of Mechanical Engineers.