Development of a methodology for empirical scientific research using standard libraries of automatic machine learning on the example of predicting oil and gas reservoirs in the Tomsk Region

Abstract

A methodology for applying machine learning to predict oil and gas reservoirs with a specified accuracy has been developed and illustrated using examples. The methodology includes principles of working with datasets, methods for interpreting the obtained models, methods for filtering local data noise, empirical methods for assessing prediction accuracy, and a method for calculating prediction accuracy based on Bayes' theorem. This work may be of interest to specialists in the field of logic and methodology of natural sciences, petroleum and gas geology, design of geological exploration works, design of oil and gas field development, as well as specialists in data analytics and artificial intelligence in the oil and gas industry.