Research on Big Data Storage Model of Oilfield Assay Data Based on MongoDB

In sample analysis, because of different types of sample assaying project result in different types of data models to be used for storing these different types of assaying data. So far, all assaying data models almost employ relational data model, it is well known that system retrieval efficiency will reduce with the accumulation of data volume. Not only that, some of assaying data models possess sparseness issues, which must occupy a lot of data storage space. Today, it becomes possible to solve these issues with the emergence of various advanced big data platforms and models. Based on the study of the assay data model used in the oil field and the data model in the NoSQL, a method for storing assay data using a document model is proposed in this paper, which effectively solves the problem of inefficient and difficult management in the traditional relational model. Finally, we implemented the prototype system of assay data storage based on MongoDB, and verified the feasibility of storing data by document model. The results show that the proposed assaying data model can improve efficiency of data retrieving and data storage, which possesses better extensibility and pervasive.