In the process of field exploration, along with regular flooding, a significant part of the wells is flooded prematurely due to leakage of the string and outer annulus. In an effort to intensify the flow of oil to the bottom of wells in field conditions, specialists often try to solve this problem by using various technologies that change the reservoir characteristics of the formation. Any increase in pressure that exceeds the strength of the rocks in compression or tension leads to rock deformation (destruction of the cement stone, creation of new cracks). Moreover, repeated operations under pressure, as a rule, lead to an increase in water cut and the appearance of behind-the-casing circulations. For that reason, an important condition for maintaining their efficient operation is the timely forecasting of such negative phenomena as behind-casing cross flow and casing leakage. The purpose of the work is to increase the efficiency of well interventions and workover operations by using machine learning algorithms for predicting well disturbances. Prediction based on machine learning methods, regression analysis, identifying outliers in the data, visualization and interactive processing. The algorithms based on oil wells operation data allow training the forecasting model and, on its basis, determine the presence or absence of disturbances in the wells. As a result, the machine forecast showed high accuracy in identifying wells with disturbances. Based on this, candidate wells can be selected for further work. For each specific well, an optimal set of studies can be planned, as well as candidate wells can be selected for further repair and isolation work. In addition, in the course of this work, a set of scientific and technical solutions was developed using machine learning algorithms. This approach will allow predicting disturbances in the well without stopping it.
This paper discusses the concept of “motivation” in relation to the process of music education. The most effective ways of teaching are highlighted, which allow achieving a high level of motivation among students learning music, among which the method of active learning is recognized as the most effective for achieving this goal. The system of various motives that induce the student to educational activity is considered. Particular attention is paid to the process of formation and development of motives and needs; emphasis is placed on the importance of a personal approach to learning, taking into account the individual characteristics and abilities of each student. The personality of the teacher and the nature of his relationship with students are recognized as one of the fundamental factors affecting the success of training. The main pedagogical conditions that contribute to the increase of the motivational sphere of students are highlighted, such as: a personal approach, the creation of a special motivational atmosphere, the aspiration for the transition to self-education and self-development, the choice of the most effective forms of work. The conclusion is made about the necessary presence of internal motivation for the process of learning a subject (in this case, music) for productive educational activity. The problems in the field of music education are revealed, which consists in the lack of psychological knowledge among music teachers about the specifics of the formation of the motivational sphere of students.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.