The article is devoted to the development of a hybrid method for predicting and preventing the development of troubles in the process of drilling wells based on machine learning methods and modern neural network models. Troubles during the drilling process, such as filtrate leakoff; gas, oil and water shows and sticking, lead to an increase in unproductive time, i.e. time that is not technically necessary for well construction and is caused by various violations of the production process. Several different approaches have been considered, including based on the regression model for predicting the indicator function, which reflects an approach to a developing trouble, as well as anomaly extraction models built both on basic machine learning algorithms and using the neural network model of deep learning. Showing visualized examples of the work of the developed methods on simulation and real data. Intelligent analysis of Big Geodata from geological and technological measurement stations is based on well-proven machine learning algorithms. Based on these data, a neural network model was proposed to prevent troubles and emergencies during the construction of wells. The use of this method will minimize unproductive drilling time.
Keywords: machine learning, neural networks, detection of anomalies, prediction of troubles, hybrid simulation, drilling of oil and gas wells, geological and technological information, prevention of accidents and complications, artificial intelligence, automated system, neural network modeling
The article was prepared based on the results of work carried out within the framework of the Program of State Academies of Sciences for 2013– 2020. Section 9 «Earth Sciences»; direction of fundamental research: 132 «Integrated development and conservation of the Earth’s interior, innovative processes for the development of mineral deposits and deep processing of mineral raw materials», on the topic of the state assignment «Fundamental basis of innovative technologies in the oil and gas industry», No. АААА-А16-116031750016-3; No. 0139-2019-0009 in Parus and No. АААА-А19-119013190038-2 in ROSRID.
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