Insurance Value of Intelligent Well Technology Against Reservoir Uncertainty

Abstract

Abstract Significant challenges remain in the development of optimized control techniques for intelligent wells, particularly with respect to properly incorporating the impact of reservoir uncertainty. Most optimization methods are model-based and are effective only if the model can be used to predict future reservoir behavior with no uncertainty. Recently developed schemes, which update models with data acquired during the optimization process, are computationally very expensive. We suggest that simple reactive control techniques, triggered by permanently installed downhole sensors, can enhance production and mitigate reservoir uncertainty across a range of production scenarios. We assess the implementation of an intelligent horizontal well in a thin oil rim reservoir in the presence of reservoir uncertainty, and evaluate the benefit of using two completions in conjunction with surface and downhole monitoring. Three control strategies are tested. The first is a simple, passive approach using a fixed control device to balance inflow along the well, sized prior to installation. The second and third control strategies are reactive, employing intelligent completions that can be controlled from the surface. The second strategy opens or closes the completions according to well water cut and flow rate and individual downhole rate and phase measurements obtained from a surface multiphase flowmeter and alternating zonal well tests. The third strategy proportionally chokes the completions as increased completion water cut is measured using downhole multiphase flowmeters. A cost-benefit analysis demonstrates that reactive control strategies always yield a neutral or positive return, whereas a passive, model-based strategy can yield negative returns if the reservoir behavior is poorly understood. While reactive control strategies enhance production and mitigate reservoir uncertainty, they may not deliver the optimum possible solution. Proactive control techniques, which additionally incorporate data from downhole reservoir-imaging sensors, may yield near-optimal gains. Introduction Intelligent (or smart) wells are equipped with downhole sensors to monitor well and reservoir conditions and with valves to control the inflow of fluids from the reservoir to the well (Robison 1997). This combination of monitoring and control technology has the potential to significantly improve oil recovery (Algeroy et al. 1999; Glandt 2005). However, considerable challenges remain in the formulation of control strategies to operate the valves during production, particularly when there is uncertainty associated with the reservoir description. Inflow control to a well can be "passive" or "active" (Jansen et al. 2002; Kharghoria 2002). Passive control may be effective if the reservoir geology and drive mechanisms are well understood so that inflow can be predicted with confidence using reservoir and well models, and if the predicted inflow does not change significantly with time during production. The well can then be configured so that hydrocarbon production (or some other objective function) is maximized, by optimizing the inflow profile along the well using fixed control devices sized prior to installation (e.g., Brekke and Lien 1994; Permadi et al. 1997). Active control is facilitated by the adjustable inflow control valves (ICVs) installed in intelligent wells. The settings of these valves can be varied to optimize the inflow profile along the well in response to monitoring data obtained from downhole sensors and to the predictions of reservoir and well models. Active control can be either "reactive" or "proactive" (Kharghoria et al. 2002; Yeten et al. 2004; Aitokhuehi and Durlofsky 2005; Ebadi and Davies 2006). Reactive strategies change the settings of ICVs in response to adverse changes in flow—such as the arrival of unwanted fluids—measured within the well or the adjacent reservoir.