Part 11/14:
Handling high-dimensional problems: Researchers are exploring other surrogate models (e.g., random forests, neural networks) suited for larger parameter spaces.
Adaptive evaluation budgets: Strategically determining how many points to evaluate per iteration to balance exploration-exploitation.
Scalability: Developing approximations for GP models or distributed optimization frameworks.
Organizational and Talent Considerations
Shell emphasizes that effective digital transformation isn’t solely about algorithms—it also requires:
Building a data-centric culture
Ensuring data quality and accessibility
Employing talent skilled in data science, machine learning, and reservoir engineering