Forecasting the abnormal events at well drilling with machine learning
نویسندگان
چکیده
We present a data-driven and physics-informed algorithm for drilling accident forecasting. The core machine-learning uses the data from telemetry representing time-series. have developed Bag-of-features representation of time series that enables to predict probabilities six types accidents in real-time. model is trained on 125 past 100 different Russian oil gas wells. Validation shows can forecast 70% with false positive rate equals 40%. addresses partial prevention at well construction.
منابع مشابه
Machine Learning Models for Housing Prices Forecasting using Registration Data
This article has been compiled to identify the best model of housing price forecasting using machine learning methods with maximum accuracy and minimum error. Five important machine learning algorithms are used to predict housing prices, including Nearest Neighbor Regression Algorithm (KNNR), Support Vector Regression Algorithm (SVR), Random Forest Regression Algorithm (RFR), Extreme Gradient B...
متن کاملForecasting the Tehran Stock market by Machine Learning Methods using a New Loss Function
Stock market forecasting has attracted so many researchers and investors that many studies have been done in this field. These studies have led to the development of many predictive methods, the most widely used of which are machine learning-based methods. In machine learning-based methods, loss function has a key role in determining the model weights. In this study a new loss function is ...
متن کاملMachine Learning Strategies for Time Series Forecasting
The increasing availability of large amounts of historical data and the need of performing accurate forecasting of future behavior in several scientific and applied domains demands the definition of robust and efficient techniques able to infer from observations the stochastic dependency between past and future. The forecasting domain has been influenced, from the 1960s on, by linear statistica...
متن کاملElectricity Load Forecasting Using Machine Learning Techniques
Electricity load forecasting has become increasingly important due to the strong impact on the operational efficiency of the power system. However, the accurate load prediction remains a challenging task due to several issues such as the nonlinear character of the time series or the seasonal patterns it exhibits. A large variety of techniques have been proposed to this aim, such as statistical ...
متن کاملElectricity Load Forecasting Using Machine Learning Techniques
Electricity load forecasting has become increasingly important due to the strong impact on the operational efficiency of the power system. However, the accurate load prediction remains a challenging task due to several issues such as the nonlinear character of the time series or the seasonal patterns it exhibits. A large variety of techniques have been proposed to this aim, such as statistical ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Applied Intelligence
سال: 2022
ISSN: ['0924-669X', '1573-7497']
DOI: https://doi.org/10.1007/s10489-021-03013-x