SC-02: Using All Your Data: Integration of Geology and Geophysics

• Earth science subsurface practitioners (geology, geophysics, petrophysics, geomechanics, ...) who are interested in tighter integration of data and models across disciplines.
• Reservoir engineers who want to gain broader insights on how earth science data can inform their dynamic models and forecasts.
• Students and researchers aiming to deepen their understanding of integrated subsurface modeling and workflow approaches.

This short course highlights a range of approaches for the integration of geology, geophysics, and neighboring disciplines and illustrates benefits of bringing these disciplines together. The course provides an overview of how seismic is used to assess the subsurface, including reservoir structure, connectivity, static and dynamic modeling, and geohazards. We will discuss practical integration methods such as subsurface model validation with seismic data and uncertainty assessment including depth and structural uncertainty. Advanced technologies for integration of geology and geophysics will be covered, including high-resolution computational stratigraphy to bridge the gap between seismic and well log scale and automation and machine learning technologies to facilitate cross-discipline integration of subsurface assessments. The emphasis throughout the course is on integrated workflows and using all available data from seismic to flow simulation to enable reliable subsurface assessment.

Why attend:
• Gain insights: Learn practical ways to integrate data and models across disciplines for subsurface characterization.
• Increase efficiency: Use machine learning/AI and workflow automation to reduce subsurface cycle time.
• Improve decision making: Explore integrated methods for more robust subsurface assessment.

Topics covered:
• Conventional methods for integration of geology and geophysics:
o Integration of seismic velocities for depth and structural uncertainty
o Integration of seismic amplitudes and inversion for reservoir modeling
• Advanced and machine learning methods:
o Machine learning/AI technologies for seismic interpretation
o Integration of seismic data with high-resolution stratigraphic modeling
o Integration of seismic data with dynamic reservoir simulation and geomechanics
• Next steps in subsurface workflow automation and integration.