A Review on Applications of Big Data Analytics in Upstream and Downstream of Oil and Gas Industry

  • 2019 Technical Conference Program
  • May 29, 2019
  • 3:00 pm - 3:30 pm

WBPC 2019 Abstract #25:

Co-author: Farshid Torabi, PhD, PEng. Faculty of Engineering and Applied Sciences, Petroleum Systems Engineering, University of Regina

This paper includes a comprehensive review on the applications of Big Data analytics in both upstream and downstream oil and gas industry. Big Data analytics (also called Big Data or business analytics) refers to a new technology, which can be employed to handle large datasets with six main characteristics of volume, variety, velocity, veracity, value, and complexity. With the recent advent of data recording sensors in exploration, drilling, and production operations, oil and gas industry has become a massive data-intensive industry. It has been reported that methods such as principal component analysis (PCA) or platforms such as Hadoop can be used to interpret seismic and micro-seismic data. In the field of drilling engineering, the data obtained through automated drilling state detection monitoring service, logging while drilling, or measurement while drilling can be analyzed to improve the drilling time, operation, and safety. Furthermore, analyzing the data from distributed downhole sensors have improved the reservoir characterization and simulation. Big Data has been successfully used in production engineering in areas such as optimizing the performance of electric submersible pumps and production allocation techniques. Big data has also been successfully used in downstream oil and gas industry in fields such as oil refining and petrochemical plants, oil and gas shipping, and health and safety executive (HSE). Although Big Data is gaining interest by E&P companies, but there are still some major challenges which are required to be addressed in order to apply the Big Data efficiently. These challenges mainly include lack of business support and awareness about the Big Data within the industry, quality of the data, and understanding the complexity of the problem.