ENSURING THE QUALITY AND VOLUME OF GEOLOGICAL-TECHNOLOGICAL DATA FOR APPLICATION OF MACHINE LEARNING METHODS KNOWLEDGE-ORIENTED SYSTEM

Abstract

The article considers the issues of complications arising during the technological processes of drilling related to geological, geophysical and external conditions, urbanization and detection of inconsistencies between the actual drilling conditions from the projected ones due to changes in climate and geological changes, which occur in the period from the end of the project and the actual start of development of the field. The interrelation of the factors that complicate the drilling process with the stages of design and organization of precedents, on the basis of which the technological processes of drilling are modeled, is analyzed. The phases of the cycle of reasoning based on knowledge with a projection on the information cycle of drilling process control are revealed. The architecture of automation of technological processes of drilling with reference to the pyramid of computer-integrated production is presented. The article shows the possibility of applying machine learning methods to data analysis tasks related to the drilling process. The use of a combined approach to adapt the data used for knowledge-based decision-making is proposed. The problem of insufficient precedent for training knowledge-oriented system of intellectual support of decision-making processes is considered and the provision of full-scale simulators with the necessary amount of data for modeling complications of high-risk drilling process is substantiated. The authors determine the level of the expected ratio between the main objects of the knowledge-oriented system of intelligent decision-making on the course of the technological process: the problems of machine learning on the one hand and oil and gas wells on the other.