Research on the application of big data in the petroleum industry

Abstract

In the past decade, the amount of data in the petroleum industry has increased rapidly, and the demand for data mining has continued to increase. The method of big data analysis can more scientifically guide the exploration and development, oil refining and chemical industry, transportation, storage and marketing of the petroleum industry. This paper briefly describes the big data theory and the development status and problems of the petroleum industry, and puts forward an overall design architecture of big data platform in the petroleum industry, which is divided into five levels, including data source, acquisition technology, storage technology, processing and analysis technology, as well as big data applications in the upstream, midstream and downstream of the petroleum industry. This paper analyzes and summarizes the characteristics of big data in the petroleum industry, studies the common algorithms of supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning in data mining technology, and finally gives six typical application scenarios of the petroleum industry chain.