A new optimisation procedure for uncertainty reduction by intelligent wells during field development planning

The uncertainty in the produced oil volume can be minimised by substituting intelligent wells (IWs) for conventional wells. A previous study showed that IWs reduce the impact of geological uncertainty on the production forecast (Birchenko, Demyanov et al. 2008). This investigation has now been extended to the “dynamic” parameters (fluid contacts, relative permeabilities, aquifer strength and zonal skin). The efficiency of the IWs in reducing the total production uncertainty due to the reservoir’s dynamic parameters was found to be comparable to that reported for the static parameters. However, this later study identified that the result was strongly dependent on the strategy employed to optimise the field’s performance. Experience has shown that challenges arise while using commercial software for optimisation of a typical, modern field with multiple reservoirs and a complex surface production network. Inclusion of the optimisation algorithm dramatically increases the calculation time in addition to showing stability and convergence problems. This thesis describes the development of a novel method of a reactive control strategy for ICVs that is both robust and computationally fast. The developed method identifies the critical water cut threshold at which a well will operate optimally when on/off valves are used. This method is not affected by the convergence problems which have lead to many of the difficulties associated with previous efforts to solve our non-linear optimisation problem. Run times similar to the (non-optimised) base case are now potentially possible and, equally importantly, the optimal value calculated is similar to the result from the various optimisation software referred to above. The approach is particularly valuable when analysing the impact of uncertainty on the reservoir’s dynamic and static parameters, the method being convergent and independent of the point used to initiate the optimization process. “Tuning” the algorithm’s optimisation parameters in the middle of the calculation is no longer required; thus ensuring the results from the many realisations are comparable