Intelligent/smart completions are widely used to maximize the value of production wells through higher ultimate hydrocarbon recoveries, to promote better cleanup of unconventional wells during “flowback,” and to improve sweep efficiency in case of injector wells. To maximize the economic value of these applications, especially in the presence of uncertainties (geological, reservoir, and long-term tool reliability), and minimize the economic risk it's vital to optimize the placement and operational settings of the Interval Control Devices (ICDs)/Autonomous Inflow Control Devices (AICDs)/Interval Control Valves (ICVs). The requirement for optimization could also arise from the limitation imposed by present technology on the maximum number of valves deployable in a single completion string.

In this paper an optimization routine for determining the optimal placement of ICVs and their inflow settings is presented. The overall optimization scheme uses the simulated annealing algorithm in conjunction with a commercial reservoir simulator to maximize an objective function that captures the mean and variance in the well's estimated value. Multiple geostatistical realizations are used to incorporate the element of geological/reservoir uncertainty in the optimization process. The workflow also accounts for the risk of flow control valve failure. A brief description of the screening methodology (to choose the appropriate inflow control technology) and a decision analysis framework for deploying intelligent completion technology, based on utility theory, is also presented herein.

The optimization technique was applied to cooptimize the positions and flow cross-section areas of the ICVs in a horizontal well completed in an oil reservoir, using a composite objective function. Geologic/reservoir and valve-life uncertainties were incorporated in the routine. The improvement in the well's Net Present Value (which is between 55–70% for the cases investigated) obtained through the employment of this technique, is also illustrated. An instance of the decision analysis is carried out to exhibit the suitability of intelligent completions deployment in the given well.