@inproceedings{8abf77e54a2f430bae5551e894f277f5,
title = "A warning thresholds scheme with dynamic oil parameters based on lasso regression and 6sigma",
abstract = "Dynamic early warning makes great sense for oil management to keep safety and stability of oil production. In this paper, we derive the production regression model, predict production with 10 oil parameters based on Least Absolute Shrinkage and Selection Operator (Lasso) and Least Angle Regression (LARS) methods. The 10 most relevant oil parameters are decided by the warning parameters selection method from kinds of different parameters, which makes the prediction more reliable. The accuracy of regression model achieves 97\%. Then we get the warning thresholds based on 6σ. Oil parameters for warning threshold partition experiment are from the database of Tianjin oilfield. The experiment results show that our method is capable of warning both mild and severe situation, and the accuracy is 95\%, which runs ahead in oil industry and has great popularization value.",
keywords = "6σ, early warning, lasso, oil production, regression",
author = "Yuyan Wu and Yueyang Chen and Yueting Shi and Chang Lu and Dongpeng Song",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE International Conference on Digital Signal Processing, DSP 2016 ; Conference date: 16-10-2016 Through 18-10-2016",
year = "2016",
month = jul,
day = "2",
doi = "10.1109/ICDSP.2016.7868543",
language = "English",
series = "International Conference on Digital Signal Processing, DSP",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "190--193",
booktitle = "Proceedings - 2016 IEEE International Conference on Digital Signal Processing, DSP 2016",
address = "United States",
}