In domestic and world practice, despite the measures applied and developed to improve the quality of well casing, there is a problem of leaky structures in almost 50 % of completed wells. The study of actual data using classical methods of statistical analysis (regression and variance analyses) doesn't allow us to model the process with sufficient accuracy that requires the development of a new approach to the study of the attachment process. It is proposed to use the methods of machine learning and neural network modeling to identify the most important parameters and their synergistic impact on the target variables that affect the quality of well casing. The formulas necessary for translating the numerical values of the results of acoustic and gamma-gamma cementometry into categorical variables to improve the quality of probabilistic models are determined. A database consisting of 93 parameters for 934 wells of fields located in Western Siberia has been formed. The analysis of fastening of production columns of horizontal wells of four stratigraphic arches is carried out, the most weighty variables and regularities of their influence on target indicators are established. Recommendations are formulated to improve the quality of well casing by correcting the effects of acoustic and gamma-gamma logging on the results.
