Multiple correlation-regression analysis of the impact of major factors on oil production

Abstract

This article discusses the multiple regression analysis techniques to determine the effectiveness of the factors used. The study examines the various relationships between the elements. It is important to identify which factor will be the most important when selecting wells to determine the amount of oil recovery. Nowadays, the most important problem in the fields of Tatarstan and Bashkortostan is the depletion of deposits. To maintain the profitability of mining companies, therefore, the issue of preparing new reserves remains relevant. This process involves high costs and risks. For a more reliable picture, it is crucial to determine the most relevant factors. The use of the triad of studies proposed by the authors makes it possible to more reliably determine the effectiveness of oil companies. The initial data are direct measurements and methods of mathematical statistics that allow more accurate predictions. Statistical analysis made it possible to identify the parameters on which the effectiveness of the factors depends. In domestic practice, the assessment of resources and reserves of hydrocarbons is usually made by deterministic methods, while abroad the statistical method is used. When studying the relationships between objects, the analyst should be interested not only in the presence and quantitative assessment of the relations but also in the form and relationship of the effective and factor characteristics, its analytical expression. Correlation and regression analysis help to solve these problems. Correlation analysis aims to measure the tightness of the relationship between the varying variables and to evaluate the factors that have the greatest impact on the resulting trait. Regression analysis is designed to select the form of the relationship, to determine the calculated values of the dependent variable (the effective feature) [1]. For the factor analysis, we used data on the oil industry published in the annual statistical collections of Rosstat, as well as specialized periodicals for ten years.