A Novel Optimisation Algorithm for Inflow Control Valve Management

Abstract

Intelligent Wells are distinguished from conventional wells by being equipped with downhole sensors to monitor the Inflow Control Valves (ICVs) to control the (multiple) zonal flow rates. The data from the downhole sensors monitors the properties of the fluid flowing into the well from the reservoir at a zonal or a well level. The sensor data is analysed to provide the necessary information for the ICVs to be operated in the optimum manner i.e. to increase the hydrocarbon recovery and prevent unwanted fluid production. This objective is simply stated, but the optimisation calculations required to identify the optimum ICV settings necessitates the repetitive solution of a complex, non-linear problem. Several commercial software providers have made such optimisation algorithms available to the industry to perform this task. However, experience has shown that challenges still arise when they are applied to large, complex models even though these algorithms work well on many simple cases. This is especially true when the optimisation algorithm is combined with a large, multi-well simulation model of multiple reservoirs with a complex, surface production network that is typical of those used today by operators to study real-field cases prior to field development. Inclusion of the optimisation algorithm not only dramatically increases the calculation time (up to 50 times when compared with the equivalent run without such optimisation); but also stability and convergence problems give additional increases in the running time. More importantly, the combined software will sometimes simply stop, due to erroneous control parameters being provided by the optimisation algorithm. The optimisation algorithm may also return unrealistic results at random time intervals, a problem that can lead to unnecessary complications as it may not be immediately recognised. Such problems are particularly acute if the software is performing multiple realisations, for example when it is being applied to analyse the impact of a multiple field development scenarios or when studying how uncertainty in the reservoir's dynamic and static properties affect the field's production performance. This paper will present a novel method based on the direct search algorithm for implementing an ICV control strategy. This method was chosen since it is not affected by the convergence problems which have caused many of the difficulties associated with previous efforts to solve our non-linear optimisation problem. Our control strategy will use the current, zonal inflow rate and water cut data to identify the optimal ICV choke positions. The availability of this data reduces the number of possible choke positions that have to be evaluated at each time step by the simulator. Run times similar to the base case are potentially possible while, equally importantly, the optimal value identified is similar to the value returned by the other published optimisation methods referred to above. This paper outlines the assumptions made and, after exploring the method's use in...

Role of Artificial Lift in Wells With a Downhole, Flow Control Completion

“…The pump performance was forecast using a linear function of oil flowrate with respect to water cut between the initial conditions to a water cut of 80%. ( Grebenkin and Davies, 2012) introduced the concept of a zonal "critical water cut". This work showed that, for reactive optimisation, an ICV should be closed once the zone produces at a water cut greater than the "critical water cut" value.…”

“…This will be aided by the calculating the "critical water cut" value at which each (ICV) zone should be closed (Grebenkin and Davies, 2012). Unfortunately, we do not have the necessary information to carry out the full economic analysis described above; though we were able to prepare the qualitative comparison of Workflow A and B (Figures 14 and 15) by assuming that the water cut increases linearly with time.…”

“…A reactive and (pseudo) proactive (essentially a predefined reactive) control strategy were compared by (Pinto et al, 2012) for a particular, rate-constraint case. Finally (Grebenkin and Davies, 2012) proposed a novel reactive, ICV control strategy providing instant optimization for wells producing under fixed wellhead pressure.…”

Proactive Optimization of Intelligent-Well Production Using Stochastic Gradient-Based Algorithms

“…Guyaguler and Byer (2008) used piecewise linear function with mixed-integer linear programming to provide an optimum reactive solution to the production-allocation problem in conventional wells. This approach improves the shortterm (current) objectives by optimizing this nonlinear equation subject to active constraints.…”

The Improvement of Production Profile While Managing Reservoir Uncertainties with Inflow Control Devices Completions

“…AICDs and AICVs are passive devices that are capable of actively control the unwanted fluid flow after breakthrough. ICVs are active devices with the capability of change the inflow area enabling the operator to control the well in a proactive and/or reactive manner (Kharghoria et al (2002), Yeten et al, 2004, Aitokhuehi and Durlofsky, 2005, Ebadi and Davies, 2006, Haghighat Sefat et al, 2013, Grebenkin and Davies, 2012, Addiego-Guevara et al, 2008.…”