Optimization of Commingled Production Using Infinitely Variable Inflow Control Valves

Abstract

Summary We developed an operational strategy for commingled production with infinitely variable inflow control valves (ICVs) using sequential linear programming (SLP). The optimization algorithm requires instantaneous and derivative information. We propose a workflow in which the production engineer relies on measurements to determine the flow rate and pressure values and on models to determine the derivative information (i.e., the changes in flow rates as a result of a change in an ICV setting). Such a model typically would be a steady-state wellbore simulator including choke models to represent the ICVs and inflow models to represent the near-well reservoir flow in the various zones. The parameters of the model need to be updated regularly using real-time measurements and production tests, and we discuss the impact of different smart-well instrumentation levels on the updating process. We simulated the performance of this production-optimization strategy in a reservoir simulator. Some numerical aspects of the algorithm and problems encountered during implementation are discussed. The performance of the algorithm was tested in two reservoir settings. In both cases, the optimization resulted in accelerated oil production compared to conventional, surface-controlled production. However, accelerated production did not always result in higher ultimate recovery compared to the conventional case. In such situations, the benefits of either short-term production optimization (accelerating production) or long-term reservoir management (maximizing recovery) should be weighed.