Computational sciences in the upstream oil and gas industry

Area of Science:

• Geosciences
• Computational Science
• Petroleum Engineering

Background:

• Subsurface uncertainty is a primary challenge in the upstream oil and gas industry.
• Remote sensing (e.g., seismic waves) and well data provide limited insights into subsurface conditions.
• Computational sciences offer powerful tools to mitigate subsurface uncertainty.

Purpose of the Study:

• To review the current state of computational science applications in the upstream oil and gas sector.
• To discuss advancements in seismic imaging, reservoir simulation, and data analytics.
• To provide a non-technical overview of these technologies' impact.

Main Methods:

• Review of current literature and industry practices.
• Discussion of algorithmic advances and high-performance computing in seismic imaging (full wavefield inversion).
• Examination of parallel computing architectures for reservoir simulation and the role of data analytics.

Main Results:

• Full wavefield inversion, powered by algorithmic progress and petascale computing, is revolutionizing seismic imaging.
• Modern parallel computing architectures are significantly advancing reservoir simulation capabilities.
• Data analytics is playing an increasingly important role in the upstream industry.

Conclusions:

• Computational science is crucial for managing subsurface uncertainty in oil and gas exploration and production.
• Emerging technologies like full wavefield inversion and advanced computing architectures are transforming industry practices.
• Data analytics represents a vital component for future upstream operations.