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- W2977112000 abstract "Despite the steady growth of petroleum products, oil production has become more difficult every year. One reason is production drops of «light» oil, increasing the water influx. This process consists of the entry of alien (external from another oil reservoir) waters into the main flow of oil. The reasons for such a violation may be weak cementing of the production string or disruption of the cement ring. This work aims to develop an algorithm of detection of lost circulation in drilling wells employing sensor data using a machine learning technique. The following classification methods were tested during the development process: k-nearest neighbors, support vector machine, random forest classifier, logistic regression, naive Bayes. The logistic regression model showed the best results. The accuracy of the developed algorithm for wells is 79%, and it can be useful in the detection of the lost circulation in the drilling wells." @default.
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- W2977112000 date "2019-01-01" @default.
- W2977112000 modified "2023-10-16" @default.
- W2977112000 title "Detection of lost circulation in drilling wells employing sensor data using machine learning technique" @default.
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- W2977112000 doi "https://doi.org/10.1016/j.procs.2019.08.206" @default.
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