Part 12/14:
The company advocates for hiring professionals capable of understanding data, building end-to-end models, and working confidently under uncertainty.
Conclusion: Embracing Data-Driven Optimization
Shell’s experience showcases how Bayesian Optimization, especially with parallelization, provides a powerful method to solve complex, costly blackbox problems like well location selection. While there are limitations in scalability, the approach delivers significant time and cost savings, enabling faster and better-informed decisions.