Research of actual information on well casing using machine learning and neural networks
https://doi.org/10.31660/0445-0108-2021-3-108-119
Abstract
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.
About the Authors
D. V. Shalyapin
Industrial University of Tyumen;
KogalymNIPIneft Branch of LUKOIL-Engineering LLC
Russian Federation
Russian Federation
Denis V. Shalyapin, Postgraduate, Industrial University of Tyumen, Engineer of 2nd grade of the Well Construction Monitoring Department, KogalymNIPIneft Branch of LUKOIL-Engineering LLC
Tyumen
D. L. Bakirov
Industrial University of Tyumen;
KogalymNIPIneft Branch of LUKOIL-Engineering LLC
Russian Federation
Russian Federation
Daniyar L. Bakirov, Candidate of Engineering, Acting Head of the Basic Department of KogalymNIPIneft Branch of LUKOILEngineering LLC in Tyumen, Assistant Director of Branch, KogalymNIPIneft Branch of LUKOIL-Engineering LLC
Tyumen
M. M. Fattahov
KogalymNIPIneft Branch of LUKOIL-Engineering LLC
Russian Federation
Russian Federation
Marsel M. Fattahov, Head of the Department of Drilling and Cement Muds
Tyumen
A. D. Shalyapina
Industrial University of Tyumen
Russian Federation
Russian Federation
Adelya D. Shalyapina, Postgraduate, Assistant at the Department of Drilling Oil and Gas Wells
Tyumen
V. G. Kuznetsov
Industrial University of Tyumen
Russian Federation
Russian Federation
Vladimir G. Kuznetsov, Doctor of Engineering, Professor at the Department of Drilling Oil and Gas Wells
Tyumen
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Review
For citations:
Shalyapin D.V., Bakirov D.L., Fattahov M.M., Shalyapina A.D., Kuznetsov V.G. Research of actual information on well casing using machine learning and neural networks. Oil and Gas Studies. 2021;(3):108-119. (In Russ.) https://doi.org/10.31660/0445-0108-2021-3-108-119
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