Background
A major carbonate field with 53 producers and 45 injectors had been
under waterflooding since the 1950s. In the mid-1990s,
Water-Alternating-Gas (WAG) was introduced to improve sweep
efficiency. Despite decades of operation, suboptimal injector
performance and inefficient gas allocation limited oil recovery.
Challenges
Difficulty in identifying which injectors were truly effective in
contributing to oil production.
Conventional reservoir simulation was slow, costly, and uncertain.
Need for a rapid, data-driven optimization tool to guide field
decisions.
Solution – CRM-Driven Hybrid Workflow
Applied a Capacitance Resistance Model (CRM) to capture
injector–producer connectivity.
Enhanced CRM with Machine Learning (ML) for higher confidence and
noise reduction.
Integrated with a modified Power Law oil model to quantify injector
impact on oil (not just fluids).
Optimized Gas-to-Water Ratios (GWR) per injector to maximize recovery.
Results
Identified 5 key injectors (out of 45) with the highest impact on oil
production.
Delivered optimized recommendations within 4 months.
Production uplift:
Battery oil rate increased from 560 → 740 bopd (+32%).
Specific producers near optimized injectors saw oil gains up to 300%.
Achieved 1.4% incremental recovery in 2 years, at minimal cost.
Key Takeaways:
Faster decisions: Full-field injector ranking without lengthy
simulations.
Targeted optimization: Focus on injectors that matter most.
Proven field gains: Tangible production increase from quick wins.
Scalable: Approach can be replicated in other mature waterflood or WAG
fields.
