TU Geosciences Seminar Presents:

Building subsurface models with AI

Dr. Tao Zhao

Data Science Manager - Interpretation

SLB

tzhao3@slb.com

Wednesday MAR 6, 2024 @ 12pm KEP 3005

No registration necessary

A realistic model that delineates the structure, stratigraphy, and rock properties plays a pivotal

role in our understanding of the Earth’s subsurface, and is essential to natural resource

exploration, carbon storage, and civil engineering. Traditionally, building such models requires

extensive human interaction with multiple data modalities. For example, to build a structural

model, one needs to interpret multiple horizons and faults that define the key structures, which

can be time-consuming even for experienced seismic interpreters.

We attempt to automate and accelerate the subsurface model building workflow with artificial

intelligence (AI), specifically, with deep learning. We use deep learning models in many key

steps of the workflow, including seismic and well log data quality check and conditioning,

structural and stratigraphic interpretation, generation of attributes, as well as predicting rock

properties. We will see the value of AI in building subsurface models with greatly reduced turn

around time, while also discussing some lessons learned along the journey.

Brief Bio:

Tao Zhao is the data science manager for interpretation at SLB. Tao joined

SLB in 2019 as a senior data scientist, developing deep learning applications

for seismic processing and imaging. From 2017 to 2019, Tao was a research

geophysicist at Geophysical Insights. Tao has PhD and MS degrees in

geophysics from the University of Oklahoma and the University of Tulsa, and

BE degree in exploration geophysics from China University of Petroleum

(East China). Tao received the J. Clarence Karcher Award from the Society of

Exploration Geophysicists (SEG) in 2023, and the best paper award from the 2024 SEG-AAPG IMAGE

annual meeting.