2026–2027 Distinguished Instructor Short Course

Using All your Data: Integration of Geology and Geophysics

Maisha Amaru, Chevron

Description

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.

Who Should Attend

• 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

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

Biography

Maisha Amaru

Chevron

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Maisha Amaru

Chevron

Maisha Amaru is a Geophysicist and Senior Manager for Computational Reservoir Geophysics in Chevron’s Subsurface organization, where she manages research and development of machine learning technologies for geophysical applications, and advanced solutions for integrated subsurface modeling and forecasting. Maisha previously led an earth science reservoir management research team at Chevron and conducted research and applied technical projects for integration of geophysics and geology​, velocity modeling and seismic imaging. She has published articles and holds patents on different aspects of these research topics. Maisha received the Gabriel Dengo Memorial Award for best international paper at the 2019 AAPG International Conference for her paper on “Integrated Computational Stratigraphy Reservoir Characterization and Seismic Validation”. Maisha holds a PhD in Geophysics from Utrecht University in the Netherlands and a Master’s degree in Geophysics from University of Muenster in Germany.

If you are interested in hosting our 2025–2026 DISC, please contact us.