Construction of a Coupled Mathematical Model of Oil and Gas Risk Relying on Distributed Computing

Abstract

With the rapid economic development in recent years, the development of oil and gas has become more and more rapid. Oil and gas are essential energy sources, but oil and gas risks hinder the development of the oil and gas industry. Purpose. This article mainly introduces the relevant theoretical knowledge of distributed computing and the coupled mathematical model of oil and gas risk and relies on the distributed calculation to analyze the oil and gas risk, thereby constructing the mathematical model of coupled oil and gas risk. The coupled mathematical model is based on the theories of rock mechanics, seepage mechanics, and heat transfer to study the interaction between fluid seepage and rock mass deformation under nonisothermal conditions in the reservoir and to establish the mathematical equations of the three fields (seepage field, temperature field and stress field) and their coupling action. Methodology. It mainly relies on distributed computing, analyzes oil and gas risks through distributed computing, builds a mathematical model for oil and gas risk coupling, and also inputs oil and gas risks into the network through neural network calculations to achieve the purpose of risk assessment. Finally, through the experiment and analysis of the questionnaire, the whole article is completed. Research Findings. The experiment in this article mentioned that the demand for oil and gas has been increasing in recent years, from 350 million tons in 2011 to 10.3 tons in 2016, an increase of 680 million tons, an increase of 48%, but the amount of oil and gas extracted is far below the demand for oil and gas. In 2011, the amount of oil and gas extracted was only 210 million tons, and in 2016, it was only 570 million tons, so the extraction of oil and gas needs to be accelerated. However, there are many risks in oil and gas exploitation. Therefore, how to build a mathematical model of oil and gas risk coupling based on distributed computing is the most important problem to be solved at present. Research Implications. Based on previous research results, this paper systematically studies the related issues of oil and gas exploration risk assessment. The thesis first summarizes the current research status of oil and gas exploration risk assessment. The risk of oil and gas exploration is a hot topic in the current research field of oil and gas exploration and development. Its research focuses on the adverse effects of the uncertainty of geology, technology, engineering, ecological environment, etc., on the entire exploration investment project, finds out their gaps and problems through comparison, and clarifies the direction of the next oil and gas exploration risk assessment. Practical Implications. This paper uses evidence theory to effectively realize the basic probability distribution of attributes while solving the difficult problems of most qualitative indicators in risk assessment. The two effective combinations provide new ideas for risk assessment and scientific decision-making.

Research on the evolutionary control of unsafe behavior of construction personnel based on multi-field coupled-homogeneous analysis model

“…Bayesian network models, hybrid fuzzy DEMATE-L-ANP approaches and the PSO-SVR algorithm have been used for the quantitative assessment of petroleum engineering hazards [11][12][13]. For risk control, qualitative methods such as surveys, interviews, and case studies [14][15][16], as well as quantitative methods that combine risk control with mathematical models and artificial intelligence algorithms [17,18], have been employed. However, research in this area has been limited in its examination of the coupling effects of multiple risk factors that can contribute to petroleum engineering construction accidents.…”