ADNOC sought to optimize waterflood management and field development
planning (FDP) across multiple reservoirs. Traditional
approaches—manual pattern analysis and full-physics simulation—took
6–18 months and limited the ability to evaluate multiple
scenarios, slowing optimization and reducing confidence in well and
injection planning decisions.
Objective
Deploy Resermine’s HawkEye Surveillance™ and HawkEye FDP™ to:
Automate reservoir surveillance and connectivity analysis for the
entire field,
Identify high-impact injectors, optimize injection rates, and
recommend conformance/maintenance jobs.
Evaluate infill drilling opportunities and optimize well trajectories.
Reduce planning time while increasing oil recovery and lowering CAPEX.
Approach
The project integrated hybrid modeling workflows—CRM
(production/injection data), FMM (geological models), and ML—into a
single automated platform.
Connectivity: Combined multiple models to quantify injector–producer
connectivities and isolate impact of injectors on oil impact.
Injection Efficiency & Optimization: Ranked injectors by oil
contribution and generated optimum injection rates under operational
constraints.
FDP Optimization: Generated Relative Opportunity Ranking (ROR) maps
and ran global optimizers for well placement and trajectory design.
Alternate Planning: Prepared alternate trajectories to ensure
equivalent oil in case of drilling challenges.
Results
Identified high-oil-contribution injectors for targeted optimization.
Recommended new injection rates projected to increase oil production.
Generated infill drilling opportunities with optimized well
trajectories.
Extended planning capacity to multiple reservoirs within the project
timeline.
Key Advantages
Speed: Reduced surveillance and optimization time to 2–4 weeks.
Coverage: Full-field analysis with integration of geological,
production, and injection data.
Flexibility: Scenario testing for water, gas, and WAG injection
strategies.
Accuracy: Validated against simulation and tracer analysis.
Impact
The pilot demonstrated that ADNOC can transition from
manual, pattern-based surveillance to automated, full-field
optimization , achieving higher recovery potential in weeks instead of months. The
methodology supports ADNOC’s “More with Less” strategy—maximizing
recovery while reducing unnecessary CAPEX.
