Application of artificial intelligence methods for identifying and predicting complications in the construction of oil and gas wells: problems and solutions

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Год публикации

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 process. The analysis of these issues is carried out, and the main directions for their solution are determined.
Keywords: artificial intelligence, machine learning methods, geological and technological research, neural network model, regression model, construction of oil and gas wells, identification and prediction of complications, prevention of emergency situations

The article was prepared as part of the work of the Federal Target Program «Research and Development in Priority Areas of Development of the Scientific and Technological Complex of Russia for 2014–2020» on the topic: «Development of a high performance automated system for preventing complications and emergencies during the construction of oil and gas wells based on permanent geological and technological models of fields using artificial intelligence technologies and industrial blockchain to reduce the risks of geological exploration, incl. on offshore projects «under the Agreement with the Ministry of Science and Higher Education of the Russian Federation on the allocation of a subsidy in the form of a grant dated November 22, 2019 No. 075-15-2019-1688.


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