Effectiveness of the method for selecting rotary-steerable systems based on the machine learning algorithm Random Forest Classifier

Abstract

Relevance. Urgent need to consider and determine possible ways to use machine learning methods in drilling industry, since artificial intelligence is developing rapidly. Achieving this task will provide industrial enterprises with a huge competitive advantage and make an important contribution to the scientific community for its future research. This is emphasized by such regulations as the Decree of the President of the Russian Federation dated 10.10.2019 G. No. 490 "On the development of Artificial Intelligence in the Russian Federation" and "The National Strategy for the Development of Artificial Intelligence for the period up to 2030". Aim. To study the effectiveness of using the machine learning method Random Forest Classifier, to develop methods for selecting rotary-steerable systems, to consider the efficiency of machine learning to determine target parameters when solving the task assigned to it within the drilling industry and to determine the approximate amount of time that can be spent by the algorithm to work out a possible solution. Object. Random Forest Classifier machine learning method in the conditions of solving a problem from the drilling industry on the selection of an optimal rotary-steerable system for specifically specified conditions. Methods. The authors have performed two computational experiments using two computing and electronic machines, namely a laptop and a remote server, the prerequisite for which was the data collected and analyzed on the basis of the study of the scientific literature in the field of research. This article explores the possibility of using the machine learning method Random Forest Classifier, to optimize well construction, using the example of developing a method for selecting rotary-steerable systems. Computational experiments were performed on two computers using the Python programming language, version 3.8.10, as well as the following libraries: NumPy, Pandas, Scikit-learn. Results. The computational experiments carried out proved the ability of the considered method to solve the problems of choosing suitable drilling equipment, an example of which was rotary-steerable systems. This method is able to independently determine the dependencies necessary to perform the task and spends a small amount of time on this process. The totality of these conclusions makes it possible to unequivocally assert the expediency and necessity of developing new approaches to the use of machine learning methods in the drilling industry, as well as performing multiple scientific studies on the possibilities of using machine learning in well construction and analyzing their effectiveness, since this direction is advanced and can radically change existing ideas about the processes occurring during well drilling.