We present a stochastic optimal control framework to optimize gas network inventories in the face of system uncertainties. The model captures detailed network dynamics and operational constraints and uses a weighted risk-mean objective. We perform a degrees-of-freedom analysis to assess operational flexibility and to determine conditions for model consistency. We compare the control policies obtained with the stochastic model against those of deterministic and robust counterparts. In addition, we demonstrate that the use of risk metrics can help operators to systematically mitigate system volatility. Moreover,we discuss computational scalability issues and effects of discretization resolution on economic performance.