Water-alternating-gas (WAG) injection is a gas-based enhanced oil recovery (EOR) technique used to overcome problems related with gas injection including gravity override, viscous fingering, and channeling. The WAG EOR technique is used to control gas mobility, which boosts project economics. Water alternating gas (WAG) has the dual benefit of higher recovery than continuous gas injection and CO2 sequestration. Higher sweep efficiencies and conformance control have been shown to increase the life cycle net present value (NPV) for improved field development and deployment planning. Nevertheless, a poor WAG design often results in unfavorable oil recovery. This study investigates WAG optimization in a sandstone field using a hybrid numerical-machine learning (ML) model. In this work, we present a hybrid neural approach for optimizing the WAG injection process that can be easily integrated as a workflow with any existing reservoir simulator for optimal WAG parameters to maximize reservoir life cycle cumulative recoveries.

The reservoir simulator is treated as a sample generator to form an ensemble of recovery scenarios with the WAG parameters as inputs to a dense neural network (DNN) and outputs/labels as cumulative recoveries. The neural network then serves two roles: 1) a readily available map between WAG parameters and cumulative recoveries for reduced computational cost and hence faster on-demand evaluation, and 2) as a repository condensing important correlations that can be appended with additional samples or reduced by removing redundant samples (simulation runs). Consequently, the hybrid neural approach also provides a clear picture of which simulation runs (or samples) are more conducive to optimal recovery predictions for an effective strategy to sample the high dimensional WAG parameter space and reduced compute times. This becomes especially important when we consider field scale optimization scenarios with multiple wells each with their separate injection schedules requiring exponentially increasing samples with a brute force ensemble approach (add an example in the introduction section or later and cross-refer here).

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