DOI: 10.24412/2076-6785-2023-6-57-61
Abstract
Currently, there is a growing interest in gas hydrates as an alternative energy source, a method of transporting and natural gas storage. An urgent problem is the prediction of the gas hydrates formation, therefore, there is a need to develop an effective technology capable of predicting the thermobaric conditions of hydrate formation. The purpose of this work is to create a machine learning algorithm for predicting hydrate formation conditions. The tasks that were set are the collection of empirical data on the stability zones of gas hydrates, the analysis of data by machine learning algorithms, the developing of the model capable of accurately predicting the stability zones of gas hydrates.
Materials and methods
In the course of this work, a machine learning algorithm will be built to predict the temperature and structure of hydrate formation and initial data will be analyzed by the model. The model will be based on the Random Forest method. The initial data for training and testing the algorithm will be taken from open sources.
Keywords
gas hydrates, machine learning, forecasting, thermobaric conditions
