Time Series Data Ingestion of An Oil and Gas Well: A Case Study of HyperDap Ltd

Abstract

Nowadays, big data plays a critical role in society. Big data is massive with high growth rate which is of great significance for oil and gas companies, however, a large amount of data often brings a lot of storage and management costs, such as the cost of hard disks. Here is where our application makes sense. Hence the researchers developed an application for HyperDap Ltd that filters data according to some criteria before writing into the database in order to save storage space and costs. To be specific, users collect the temperature data of oil and natural gas, including relevant location, time, and other information for analysis in order to obtain the data value they needed. When the temperature change value was set to 0.5 degree centigrade, the efficiency of only 2% was obtained. At the temperature value of 0.75 degree centigrade, the efficiency achieved was 32%, and finally, at .99 degree centigrade, the efficiency achieved was 60% on storage space reduction. The application was developed and implemented in Java as the main programming language and Cassandra as a database backend. To build up the GUI, we use JavaScript and React.