Charles A. Kang

Co-Founder and Chief Technology Officer

[email protected]

Charles Kang is an engineer experienced in developing software tools for the modeling, optimization, and visualization of subsurface and energy systems.

Prior to ResFrac, he served as R&D Director at Tiandi Energy, an oilfield data services company. Charles received a PhD in energy resources engineering from Stanford University and a Bachelor of Science degree in engineering physics from UC Berkeley. Charles’s academic research is focused on the optimization of energy systems and hydraulic fracturing.

His awards include:

Hank Ramey Award for Outstanding Research and Service to the Department, Stanford Department of Energy Resources Engineering, 2014
Illich-Sadowsky Interdisciplinary Graduate Fellowship, Stanford Vice Provost for Graduate Education, 2012

Click here for a list of Charles’ publications.

Charles's posts

$Figure 2. The ResFrac model of the original completion: tall fractures that grow up into overlying benches while proppant stays mostly in the target interval.$

Targeting the reserves between clusters: modeling strategic refracs and infills in ResFrac

Older, wide-spacing Delaware Basin wells can leave high-pressure rock stranded between fractures. Devon used ResFrac to design liner refracs whose peak rates came in within 1% of forecast, and to show why an offset infill beats a refrac at tight cluster spacing.

Automatic Report Generation in ResFracPro

This month’s release has a feature that I am particularly excited about – automatic report generation. The reports are generated automatically when a simulation finishes. Also, reports can be generated on-demand for simulations that have already been completed. The reports are highly customizable, allowing the user to generate different line plots, 3D visualizations, preview plots, and tabular data. The user can provide a template PPT, so that the report is made with their preferred PPT style.

Introducing ResFrac’s New Multisector Capability

This month’s ResFrac release has a ton of new capabilities. The most important is called ‘multisector’ – the ability to split different sections of the well across different computers. This capability provides a computationally efficient and practical way to run larger models and handle complex scenarios. Hats off to Danial Zeinabady and Michael Nole, who have been the main developers on this project. Beyond them, it’s been a big team effort, pulling in folks from cloud, simulator, and user-success. First, let’s review – what is sector modeling? Why do we need a ‘multi’ version of it?