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The development of new and existing oil fields is very expensive and technically challenging; this provides an important economic incentive to improve oil recovery via efficient computational optimisation. Decisions related to these activities may be significantly aided by sound and proven mathematical-oriented methods. The use of intuitive engineering judgement alone cannot guarantee sustainable profitability over long periods especially under geological (reservoir model) uncertainty. To capture the uncertainties in the subsurface geological/reservoir model in this work, geostatistical realisations of the model are obtained using available information (permeabilities and porosities). We also apply specialised algorithms within the MATLAB Reservoir Simulation Toolbox – MRST (interfaced with PETREL™) to optimally vary the well locations and production rates, thus maximising the field's oil recovery. A new modification of an existing pseudowell-based injection well placement algorithm is presented herein with the application of adjoint-computed gradients of an auxiliary objective function (the Lorenz coefficient). The difficulty of this problem is characterised by the presence of discrete variables, nonlinear and nonconvex objective function and constraints. The developed workflow is applied to a realistic case study, for which robust optimality is demonstrated using the worst case realisation for the determination of optimal well locations and controls. A comparative investigation of optimising injection well location vs. simultaneous optimisation of injection and production well placement is also presented. In our presented case study, we further discover that increasing the optimisation search space does not necessarily guarantee improved results.
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- 1 Finished
1/09/17 → 31/10/18