This paper poses and solves the problem of using artificial intelligence methods for processing large volumes of geodata from geological and technological measurement stations in order to identify and predict complications during well drilling. Digital modernization of the life cycle of wells using artificial intelligence methods, in particular, helps to improve the efficiency of drilling oil and gas wells. In the course of creating and training artificial neural networks, regularities were modeled with a given accuracy, hidden relationships between geological and geophysical, technical and technological parameters were revealed. The clustering of multidimensional data volumes from various types of sensors used to measure parameters during well drilling has been carried out. Artificial intelligence classification models have been developed to predict the operational results of the well construction. The analysis of these issues is carried out, and the main directions for their solution are determined.

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.

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The article discusses the use of automated systems for preventing emergency situa-tions in the process of well construction using artificial intelligence methods to increase the productive time of well construction by reducing the loss of working time to eliminate compli-cations. Key words: problems and complications during drilling, emissions, gas and oil water showings, stuck, artificial neural networks, digitalization, drilling, well, field, oil and gas blockchain, artificial intelligence, machine learning methods, geological and technological research, neural network model, oil and gas construction wells, identification and forecasting of complications, prevention of emergency situations.

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