Production Optimization under Uncertainty with Constraint Handling

To maximize the daily production from an oil and gas field, mathematical optimization may be used to find the optimal operating point. When optimizing, a model of the system is used to predict the outcome for different operating points. The model is, however, subject to uncertainty, e.g., the gas oil ratio estimates may be imprecise. The uncertainty is often ignored, and what is known as the expected value problem is solved. Because of inherent uncertainties, there is a great chance that constraints will be violated when implemented. In this paper, we formulate the production optimization problem as a stochastic programming problem, and use Conditional Value at Risk to handle the constraints. This allows us to control the conservativeness of the solution in an efficient manner.