2883. Data-driven robust optimization for offshore rig scheduling under uncertainty
Invited abstract in session WC-46: Optimal operation planning in energy systems, stream Energy Economics & Management.
Wednesday, 12:30-14:00Room: Newlyn 1.07
Authors (first author is the speaker)
| 1. | Silvio Hamacher
|
| PUC-Rio | |
| 2. | Luana Mesquita Carrilho
|
| Department of Industrial Engeneering, PUC-Rio | |
| 3. | FabrÃcio Oliveira
|
| Industrial Engineering, PUC-Rio |
Abstract
Rig scheduling is a critical oil and gas industry activity involving substantial resource acquisition and operations investments. The process is complex due to well sequencing, technical and precedence constraints, and long planning horizons. Offshore drilling relies on expensive rigs, with daily rates reaching hundreds of thousands of dollars, and is subject to unpredictable geological conditions, economic fluctuations, and operational challenges that impact optimal scheduling outcomes. This paper addresses an offshore rig scheduling problem with uncertain processing times. We propose a robust optimization model that minimizes the worst-case total completion time, representing uncertainty through an ellipsoidal uncertainty set. Our approach generates schedules that are more resilient to processing time variations. Computational experiments using real-world instances evaluate the impact of different levels of protection against uncertainty. Results show that while higher protection levels produce less conservative and more stable schedules under disruptions, lower protection levels offer greater flexibility for adjustments. In all cases, robust solutions outperform nominal schedules that disregard uncertainty.
Keywords
- Robust Optimization
- Scheduling
- OR in Energy
Status: accepted
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