«Onshore and offshore oil and gas well construction»

Review of mathematical analysis modern methods for solving problems in the field of wells construction

UDC: 622.245
DOI: 10.33285/0130-3872-2022-12(360)-5-10

Authors:

¹ KogalymNIPIneft, Tyumen, Russia
² Tyumen Industrial University, Tyumen, Russia

Keywords: mathematical analysis, machine learning, neural networks, oil and gas industry problems

Annotation:

The modern development of algorithms for mathematical and statistical data analysis makes it possible to model multifactorial production processes with high accuracy, which cannot be achieved using classical statistical analysis tools. The new algorithms are aimed at human thinking simulation, which allows combining formal logics and probability theory for a selective approach to choosing a modeling method depending on the specific conditions of the technological process. In the world practice, such means of mathematical analysis are united by the "machine learning" term. The main conditions for the effectiveness of modeling using modern statistical analysis algorithms are the quality and volume of the input information, by now achieved by domestic and global oil and gas enterprises. The collected information allows solving many problems facing the industry: preventing complications during drilling, correcting a well profile, predicting geomechanical parameters, etc. This article discusses modern problems in the field of well drilling, presents the main methods of machine learning used to model natural processes as well as describes examples of the successful implementation of modern algorithms for mathematical analysis.

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