Oil & Gas 4.0: Data Science and Artificial Intelligence Applications in the Oil & Gas Industry

A drop of oil, on its incredible journey from several thousand feet underground to the gas station (petrol pump), leaves behind tremendous amount of data. This data often remains scattered, and unused. Analysis shows that oil & gas industry uses less than 1% of the total available data [1]. Digital transformation to leverage these large amounts of data is estimated to have a value between US $1.5 to $2.6 trillion for the oil & gas industry [2].


This talk provides an overview of the different oil & gas industry segments, and their interconnections, for the benefit of larger audience. Then, selected Data Science and Artificial Intelligence use cases for each of these industry segments are presented, which have potential of having significant impact on economics, operational safety, and environmental aspects. Finally, the challenges and opportunities facing the digital transformation of oil & gas industry are discussed, from a Data Science and Artificial Intelligence perspective.


  1. https://www.mckinsey.com/industries/oil-and-gas/our-insights/why-oil-and-gas-companies-must-act-on-analytics
  2. Digital Transformation Initiative: Oil and Gas Industry (White Paper), World Economic Forum, January 2017.
Outline for the talk
  1. Introduction of the Oil & Gas industry; industry segments:

    • Upstream
    • Midstream
    • Downstream
  2. Overview of Upstream industry sub-sections:

    • Geosciences (Geophysics + Geology + Petrophysics)
    • Reservoir Engineering
    • Drilling
    • Production Engineering
  3. Overview of Midstream industry sub-sections

    • Field gathering
    • Processing
    • Transportation and Storage
  4. Overview of Downstream industry sub-section

    • Refining of crude oil
    • Purification of natural gas
  5. Selected Data Science and AI use cases for:

    • Upstream

      • Geophysics use cases
      • Geology and Petrophysics use cases
      • Reservoir and Production Engineering use cases
      • Drilling use cases
    • Midstream

      • Predictive Maintenance of Pipelines
      • Predictive Storage Planning
    • Downstream

      • Predictive Maintenance and Equipment Failure Analytics
      • Design of Advance Materials, and Hydrocarbon Storage
  6. Challenges and opportunities

  7. Conclusions
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