The applying of machine learning methods to improve the quality of well casing
https://doi.org/10.31660/0445-0108-2020-5-81-93
Abstract
The article is devoted to the quality of well casing at the Pyakyakhinskoye oil and gas condensate field. The issue of improving the quality of well casing is associated with many problems, for example, a large amount of work on finding the relationship between laboratory studies and actual data from the field; the difficulty of finding logically determined relationships between the parameters and the final quality of well casing. The text gives valuable information on a new approach to assessing the impact of various parameters, based on a mathematical apparatus that excludes subjective expert assessments, which in the future will allow applying this method to deposits with different rock and geological conditions. We propose using the principles of mathematical processing of large data sets applying neural networks trained to predict the characteristics of the quality of well casing (continuity of contact of cement with the rock and with the casing). Taking into account the previously identified factors, we developed solutions to improve the tightness of the well casing and the adhesion of cement to the limiting surfaces.
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 the 2nd grade at the Department of the Research Works in Drilling and Cement Muds
Tyumen
D. L. Bakirov
KogalymNIPIneft Branch of LUKOIL-Engineering LLC
Russian Federation
Russian Federation
Daniyar L. Bakirov, Candidate of Engineering, Assistant Director of Branch
TyumenM. M. Fattakhov
KogalymNIPIneft Branch of LUKOIL-Engineering LLC
Russian Federation
Russian Federation
Marsel M. Fattahov, Head of the Department of the Research Works in Drilling and Cement Muds
TyumenA. D. Shalyapina
Industrial University of Tyumen
Russian Federation
Russian Federation
Adelya D. Shalyapina, Postgraduate, Assistant at the Department of Drilling Oil and Gas Wells
TyumenA. V. Melekhov
KogalymNIPIneft Branch of LUKOIL-Engineering LLC
Russian Federation
Russian Federation
Alexander V. Melekhov, Senior Researcher at the Department of the Research Works in Drilling and Cement Muds
TyumenA. V. Sherbakov
KogalymNIPIneft Branch of LUKOIL-Engineering LLC
Russian Federation
Russian Federation
Andrey V. Sherbakov, Head of the Department of Design and Reconstruction of Wells
TyumenV. 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
TyumenReferences
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Review
For citations:
Shalyapin D.V., Bakirov D.L., Fattakhov M.M., Shalyapina A.D., Melekhov A.V., Sherbakov A.V., Kuznetsov V.G. The applying of machine learning methods to improve the quality of well casing. Oil and Gas Studies. 2020;(5):81-93. (In Russ.) https://doi.org/10.31660/0445-0108-2020-5-81-93
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