Judicious selection of intelligent completions will increase oil recovery andconstrain unwanted water and gas production from the reservoir. Field experience, and results from extensive simulation studies, shows that thewater, oil and gas production are highly dependent on the type of inflowcontrol devices (ICDs) and the ICD configuration. Therefore, choosing the bestICD configuration is a key point to maximize the total oil production. In along horizontal well the primary objective is to maximize oil production fromthe whole completion interval and avoiding early breakthrough of gas or waterin parts of the well. In particular there is a need for compensating for thefriction induced pressure drop in the well which unchecked will result insignificantly higher production and early break through from the heel sectionsthan from the toe. The secondary objective is to limit flow from sections that, due to for instance heterogeneities, still suffer from early break through. Devices with autonomous valves should be especially suited for meeting thissecondary objective. Traditionally, optimal ICD configurations have been selected using a trial anderror approach simulating different configurations. This time consumingapproach may result in non-optimal configurations since it is impossible totest all of the possibilities. We have developed a semi-analytical mathematical model for calculating theoptimal ICD strengths for long horizontal wells in high permeable oil rimreservoirs. The mathematical model is utilized in a computer program whichfinds the optimal ICD configuration selecting ICDs from a pre-defined set ofoff-the shelf devices. The optimal configuration can be found in a fraction ofa second, and no flow simulations are needed. Optimized ICDs with RCP valves (autonomous control devises) is compared withoptimized spiral control devices, and benefits and restrictions arepresented. Introduction The nature of oil rim reservoirs makes long horizontal wells an attractiveoption for increasing well-reservoir contact and reducing drawdown. However, increasing the horizontal wellbore length leads to some production challenges. In a long horizontal well with open hole completion, the drawdown in the heelsection of a well is much higher than the drawdown in the toe section. This isbecause of the higher cumulative frictional pressure loss in the heel sectionthan the toe. Thus, higher production in the heel than the toe section isexpected. Consequently, the inflow from the reservoir to the well and water/gasbreakthrough are non-uniform. This phenomenon gives partial water or gasbreakthrough and lower oil recovery and sweep efficiency. Advanced completions in horizontal wells have been applied in recent years toavoid mentioned problems. Utilizing advanced completion gives the chance ofcontrolling unwanted fluid and optimizing of the oil production from a longhorizontal well. Inflow Control Devices (ICDs) as a type of advanced completionhave been used in producing wells to enhance oil production and restrictunwanted fluid from the reservoir to well. ICDs are passive control valveswhich are not adjustable or retrievable. Therefore, it is crucial to choose the best type andconfiguration for a single well before starting the production.

Summary Important challenges remain in the development of optimized control strategies for intelligent wells, particularly with respect to incorporating the impact of reservoir uncertainty. Most optimization methods are model-based and are effective only if the model or ensemble of models used in the optimization captures all possible reservoir behaviors at the individual-well and -completion level. This is rarely the case. Moreover, reservoir models are rarely predictive at the spatial and temporal scales required to identify control actions. We evaluate the benefit of the use of closed-loop control strategies, on the basis of direct feedback between reservoir monitoring and inflow-valve settings, within a geologically heterogeneous, thin oil-rim reservoir. This approach does not omit model predictions completely; rather, model predictions are used to optimize a number of adjustable parameters within a general direct feedback relationship between measured data and inflow-control settings. A high-resolution sector model is used to capture reservoir heterogeneity, which incorporates a locally refined horizontal grid in the oil zone, to accurately represent the horizontal-well geometry and fluid contacts, and capture water and gas flow. Two inflow-control strategies are tested. The first is an open-loop approach, using fixed inflow-control devices to balance the pressure drawdown along the well, sized before installation. The second is a closed-loop, feedback-control strategy, using variable inflow-control valves that can be controlled from the surface in response to multiphase-flow data obtained downhole. The closed-loop strategy is optimized with a base-case model, and then tested against unexpected reservoir behavior by adjusting a number of uncertain parameters in the model but not reoptimizing. We find that closed-loop feedback control yields positive gains in net-present value (NPV) for the majority of reservoir behaviors investigated, and higher gains than the open-loop strategy. Closed-loop control also can yield positive gains in NPV even when the reservoir does not behave as expected, and in tested scenarios returned a near optimal NPV. However, inflow control can be risky, because unpredicted reservoir behavior also leads to negative returns. Moreover, assessing the benefits of inflow control over an arbitrarily fixed well life can be misleading, because observed gains depend on when the calculation is made.

We study the shape and motion of gas bubbles in a liquid flowing through a horizontal or slightly inclined thin annulus. Experimental data show that in the horizontal annulus, bubbles develop a unique ‘tadpole-like’ shape with a semi-circular cap and a highly stretched tail. As the annulus is inclined, the bubble tail tends to vanish, resulting in a significant decrease of bubble length. To model the bubble evolution, the thin annulus is conceptualised as a ‘Hele-Shaw’ cell in a curvilinear space. The three-dimensional flow within the cell is represented by a gap-averaged, two-dimensional model, which achieved a close match to the experimental data. The numerical model is further used to investigate the effects of gap thickness and pipe diameter on the bubble behaviour. The mechanism for the semi-circular cap formation is interpreted based on an analogous irrotational flow field around a circular cylinder, based on which a theoretical solution to the bubble velocity is derived. The bubble motion and cap geometry is mainly controlled by the gravitational component perpendicular to the flow direction. The bubble elongation in the horizontal annulus is caused by the buoyancy that moves the bubble to the top of the annulus. However, as the annulus is inclined, the gravitational component parallel to the flow direction becomes important, causing bubble separation at the tail and reduction in bubble length.

Important challenges remain in the development of optimized control strategies for intelligent wells, particularly with respect to incorporating the impact of reservoir uncertainty. Most optimization methods are model-based and are effective only if the model or ensemble of models used in the optimization capture all possible reservoir behaviors at the individual well and completion level. This is rarely the case. Moreover, reservoir models are rarely predictive at the spatial and temporal scales required to identify control actions. We evaluate the benefit of using closed-loop control strategies, based on direct feedback between reservoir monitoring and inflow valve settings, within a geologically heterogeneous, thin oil-rim reservoir. This approach does not omit model predictions completely; rather, model predictions are used to optimise a number of adjustable parameters within a general direct feedback relationship between measured data and inflow control settings. A high-resolution sector model is used to capture reservoir heterogeneity, which incorporates a locally refined horizontal grid in the oil zone, to accurately represent the horizontal well geometry and fluid contacts, and capture water and gas flow. Two inflow control strategies are tested. The first is an open-loop approach, using fixed inflow control devices to balance the pressure drawdown along the well, sized prior to installation. The second is a closed-loop, feedback control strategy, employing variable inflow control valves that can be controlled from the surface in response to multiphase flow data obtained downhole. The closed-loop strategy is optimized using a base case model, and then tested against unexpected reservoir behavior by adjusting a number of uncertain parameters in the model but not re-optimising. We find that closed-loop feedback control yields positive gains in NPV for the majority of reservoir behaviours investigated, and higher gains than the open-loop strategy. Closed-loop control can also yield positive gains in NPV even when the reservoir does not behave as expected. However, inflow control can be risky, because unpredicted reservoir behavior also leads to negative returns. Moreover, assessing the benefits of inflow control over an arbitrarily fixed well life can be misleading, as observed gains depend on when the calculation is made.