AI-Powered Assisted History Matching Focused on Vertical Communication in a Heterogeneous Giant Carbonate Reservoir with 16 Subzones, Offshore Abu Dhabi
Summary This study presents a novel approach to enhancing the accuracy of dynamic reservoir simulation models through the application of Multi-Objective Particle Swarm Optimization (MOPSO) in a heterogeneous giant carbonate reservoir with 16 subzones, delineated by 15 dense layers offshore Abu Dhabi. The focus is on refining the vertical communication model by calibrating kv/kh ratios against Modular Dynamics Test (MDT) pressure matching, cumulative gas, oil and water productions, and Bottom Hole Closed-In Pressures (BHCIP). The study emphasizes the integration of geological insights with AI-driven optimization to achieve a balanced, high-quality history match.
Authors
- Seiji Yamashita (ORCID: https://orcid.org/0000-0002-1736-0331)
- Masaaki Tange (ORCID: https://orcid.org/0009-0005-2762-1178)
- Hiroki Kai
- Yuto Inokuma
- Hiroki Montani
- Ikuko Kondo
- Susumu Kurokawa
Institutions
- Inpex (Japan) (JP)
- Abu Dhabi National Oil (United Arab Emirates) (AE)
Publication Details
- Published
- 2025-06-02
- DOI
- https://doi.org/10.2118/225328-ms
- Primary Topic
- Reservoir Engineering and Simulation Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.00