Drill-Hub: AI-Driven Optimization of Future Drilling Centers Planning in Giant Offshore Fields Abu Dhabi
Abstract Planning future drilling centers is a highly complex process with multiple potential solutions, making manual screening and optimization infeasible due to the sheer volume of data and constraints involved. Drill-Hub, developed in house by Operating company, automates most of the workflows and optimizes drilling center planning by leveraging cutting-edge AI algorithms and advanced mathematical models. Drill-Hub encompasses six key modules: Drill-Hub 3D for precise well trajectory calculations, Drill-Hub GS for optimal drilling center placement via grid search, Drill-Hub Allocate for well-to-DC assignment using clustering and facility allocation algorithms, Drill-Hub FT for formation top calculations, Drill-Hub Maxi for constrained well trajectory optimization, and Drill-Hub Slot for efficient slot allocation. By evaluating billions of potential scenarios, Drill-Hub delivers precise and automated solutions, replacing traditional, time-consuming manual processes. The tool has been successfully implemented in several offshore fields, including both greenfields and brownfields, reducing cost by 3-6% in long-term project costs. Drill-Hub's deployment resulted in optimized well placements, reduced infrastructure costs, and significantly accelerated planning workflows. One of the key benefits of Drill-Hub is its ability to accelerate the drilling center planning process, which is often prone to frequent changes due to new data inputs. The automated workflows allow rapid re-evaluation of scenarios, reducing dependency on third-party contractors and enabling operating company to keep critical decision-making in-house, ensuring strategic control and operational efficiency.
Authors
- Benoit Jaffres
- Jose Victor Álvarez (ORCID: https://orcid.org/0000-0003-3735-1341)
- Fatema Al-Zaabi
- Rail Salimov
- Ahmed Abdelrahman (ORCID: https://orcid.org/0000-0002-7624-7101)
- H. Al Kuwaiti
- Elyés Draoui
- Muna Al Hosani
Institutions
- Abu Dhabi National Oil (United Arab Emirates) (AE)
Publication Details
- Journal
- Offshore Technology Conference
- Published
- 2025-04-28
- DOI
- https://doi.org/10.4043/35743-ms
- Citations
- 1
- Primary Topic
- Reservoir Engineering and Simulation Methods
- Type
- article
- Field-Weighted Citation Impact
- 2.13