In Steam-Assisted Gravity Drainage (SAGD) recovery, optimal real-time steam allocation from a shared steam generator to the physically coupled multi-pads can significantly improve long-term performance goals. However, multi-pad real-time optimization (RTO) with first-principle models can be computationally intensive. Furthermore, general-purpose optimization algorithms in RTO do not consider the future state beyond the prediction horizons to be optimized and treat the optimization problem as a long-term optimization process. Since steam is the primary cost factor in SAGD, Key Performance Indicators (KPI) such as Net Present Value (NPV), when used in RTO, result in low steam injection impeding steam chamber growth during the build-up and normal SAGD operational phase. Therefore, balancing steam chamber development and economics becomes essential for SAGD well-pads using RTO to meet long-term goals.

In this contribution, we implement the Alternating Direction Method of Multipliers (ADMM) and a dynamic data-driven model to reduce the computational cost of RTO. ADMM coordinates in real-time field-wide use of shared steam generation. The shared steam generation is a market commodity traded between the pads, with global coordination in real-time perturbation of their market prices. Four SAGD KPIs are implemented for a multi-pad RTO of the SAGD normal operations phase to see which KPI eventually grows the steam chamber without negatively affecting the long-term economic performance.

A SAGD field with four pads with 33 well-pairs shows that for all four pads, an economic-based KPI limits the achievement of long-term goals because it cannot account for the future state beyond the horizon under consideration due to hindered steam chamber growth. For the steam chamber expansion and bitumen recovery KPI, high recovery and economic performance are achieved, but with a high resource requirement, leading to a high carbon footprint. On the other hand, an alternating economic and bitumen recovery KPI achieves high economic performance while minimizing resource requirements that decrease carbon footprint.

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