E3S Web Conf.
Volume 245, 20212021 5th International Conference on Advances in Energy, Environment and Chemical Science (AEECS 2021)
|Number of page(s)||5|
|Section||Environmental Resource Protection and Pollution Control|
|Published online||24 March 2021|
Research on Pump Inspection Cycle Early Warning Method Based on Big Data
1 Engineering Technology Research Institute, PetroChina Huabei Oilfield Company, Renqiu 062550, China
2 Science and Technology Division, PetroChina Huabei Oilfield Company, Renqiu 062550, China
* Corresponding author: email@example.com
At present, the existing indicator diagram can only be used for expost judgment and can not give early warning, and the influencing factors of pump inspection period are nonlinear, multi constrained and multi variable. In this paper, big data machine learning method is used to carry out relevant research. Firstly, around the influencing factors of pump inspection cycle, relevant data are collected and the evaluation index of pump inspection cycle is designed. Then, based on feature engineering technology, the production parameters of oil wells in different pump inspection periods are calculated to form the analysis sample set of pump inspection period. Finally, the early warning model of pump inspection period is established by using machine learning technology. The experimental results show that: the pump inspection cycle early warning model established by stochastic forest algorithm can identify the pump inspection status of single well, and the accuracy rate is about 85%.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.