Water-alternating-gas (WAG) injection is an enhanced oil recovery (EOR) technique used to overcome problems related with gas injection, including gravity override, viscous fingering, and channeling. The success of WAG injection is influenced, however, by reservoir characteristics, injector/producer connections, and the injected gas water ratio (GWR). This study proposes a hybrid model that combines the capacitance/resistance model (CRM), a machine-learning (ML) model, and an oil model to assess and optimize WAG injectors in a carbonate field.
Interwell connectivity between injection and production wells is obtained by combining a physics-based reduced-order model (CRM) and an ML model. The hybrid combination of CRM and ML increases confidence in results, eliminating shortcomings associated with CRM models used alone.
Next, the results obtained from injection connectivity are combined with reduced-order power-law oil model to evaluate the impact of injection rate increase on oil production. Finally, injectors are optimized and ranked based on their potential to contribute to oil production.
The proposed workflow is applied to a large, complex carbonate field with more than 93 production wells and 47 injection wells. The hybrid combination of CRM and ML used to obtain interwell connectivity compares wells with one another, resulting in higher confidence in the results.
Using ML, results obtained from multiple signals are aggregated to further identify and verify the injector/producer pair connectivity. The further combination of CRM with an oil model helps evaluate additional oil recovery based on the changing gas/water ratio (GWR). A combination of CRM optimization and the ML connectivity results help rank and prioritize 47 injectors quickly and accurately. Five injectors are selected for field testing, and results show significant improvement in oil production after implementation of suggested injection schedules.
Hybrid models that combine CRM and ML to obtain connectivity address the shortcomings of the individual models. The identification of efficient injectors for WAG injection by the novel use of hybrid models accelerates decision-making. The approach presented can be extended to similar WAG injection fields with many injectors and producers to help optimize the injection strategy. This new approach helps with current digitization strategies in oil companies to accelerate decision making, especially in mature reservoirs where history matching is not available.