Risk Management and Optimization in Real-Time Noncondensable Gas Co-injection under Economic Uncertainty

Sibaweihi, Najmudeen (University of Alberta) | Trivedi, Japan (University of Alberta (Corresponding author))


Summary When the oil price is volatile, maximizing steam allocation and noncondensable gas (NCG) is essential to ensuring a profit but reducing risk. Minimizing risk entails moving the distribution of lower tail returns closer to the expected return. Thus, there is a risk-reward tradeoff during optimization. Real-time risk-return optimization with first-principle models is computationally demanding. Sibaweihi et al. (2019) presented a real-time steam-assisted gravity drainage (SAGD) recovery optimization with varying steam availability workflow. The workflow cannot handle uncertainty, and the data-driven model may forecast out of the physical range of the model output parameters. As a result, data-driven process modeling incorporating physical or operational constraints and an optimization problem formulation that references a decision-makers' metrics to a benchmark is crucial. This study proposes data-driven input-output normalization to incorporate operating constraints based on their physical range. The workflow includes model training updating based on the concept of forgetting factor to adapt the data-driven model to the current state of the reservoir. A robust optimization (RO) problem scheme in which economic risk is mitigated by formulating the objective as a tradeoff of expected returns and risk is managed in real time. A modified Modigliani’s risk-adjusted performance has been implemented to minimize the possibility of selecting the wrong optimal risk-return tradeoff of nonsymmetric return realizations in this work. In this work, the risk is quantified through variance, minimum, semivariance (down side risk), and conditional-value-at-risk of the returns realizations because of oil price volatility. Application of the proposed workflow on a synthetic reservoir with steam NCG co-injection showed the data-driven calibrated model forecast performance shows a reasonable agreement with the synthetic reservoir throughout the optimization period. In addition, the optimization study with the proposed workflow also showed a net present value (NPV) increase of approximately 25–77% and a decrease in the cumulative steam-oil-ratio (cSOR) from 4.5 to 6.7% compared with the continuous steam injection base case. The reduction in cSOR indicates a lower steam requirement. An increase in methane sequestered demonstrates workflow ability to reduce greenhouse gas emissions while improving SAGD NCG co-injection key performance indicators.

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