Management

This study identifies critical knowledge gaps in wellbore integrity and underscores areas that require further investigation, providing insights into how wellbores must evolve to meet the technical demands of the energy transition.
This paper describes an auto-adaptive workflow that leverages a complex interplay between machine learning, physics of fluid flow, and a gradient-free algorithm to enhance the solution of well-placement problems.
This article is the sixth and final Q&A in series from the SPE Research and Development Technical Section focusing on emerging energy technologies. In this final edition, Matthew T. Balhoff, SPE, of The University of Texas at Austin shares his views on the future of upstream education.

Page 85 of 366