The Umm Shaif Field, operated by ADNOC, is an offshore asset
with complex reservoir heterogeneities and a mix of oil and gas
development challenges. Traditional full-physics reservoir simulations
for field development planning (FDP) were taking 9–12 months,
limiting the ability to evaluate multiple scenarios and reducing
decision-making confidence.
Objective
Deploy HawkEye FDP™— AI/ML-driven hybrid modeling platform and
optimize current FDP to see if we can get more oil with less wells
Optimize well number, placement, and trajectories.
Reduce CAPEX while maintaining or have a higher recovery.
Approach
HawkEye FDP™ integrates Fast Marching Method (FMM),
hybrid machine learning models, and
advanced optimizers to identify optimum well locations and
trajectories using only a single simulation file as input. The
workflow:
Preprocess field data and history-matched dynamic models.
Generate Relative Opportunity Ranking maps to identify high-potential
zones.
Run hybrid optimization integrating geological, surface, and
operational constraints.
Validate optimized solutions via reservoir simulation.
Results
Optimized Well Count: Fewer new wells compared to the base case (e.g.,
-9 OP / +3 WI in Arab D5 scenario).
Higher Recovery: Up to 17% additional oil for new reduced well cases.
Improved Plateau: Extended production plateau with higher reservoir
pressure.
CAPEX Savings: Significant reduction through drilling fewer,
higher-impact wells.
Key Advantages
Speed: Reduced FDP optimization cycle from 9–12 months to 4 weeks.
Flexibility: Ability to propose alternate well trajectories delivering
equivalent oil recovery in case of drilling challenges.
Accuracy: Validated by ADNOC’s Thamama Excellence Center (TEC) through
a “blind test” on Arab D5 model.
Impact
The POC demonstrated ADNOC’s ability to move from
static, simulation-heavy workflows to a
fast, automated , and data-driven FDP process . The
“More with Less” concept—higher recovery with fewer wells—proved both
technically and economically viable, paving the way for broader
deployment across ADNOC’s asset portfolio.
