Production optimisation under uncertainty in fractured reservoirs

Uncertainty in the distribution of fractures has a high impact on the fluid flow in oil reservoirs. The challenge is to propagate the uncertainty in the fracture distribution patterns into the reservoir flow response. Optimisation reservoir production under this geological uncertainty would result in to more robust operational decisions to maximise recovery and minimise production costs. Commonly the uncertainty in fracture distribution is described by multiple discrete fracture network realisations (DFN) that represent a range of geologically plausible scenarios. The range of fracture distribution scenarios is captured by spatially varying properties such as facture density distribution, orientation, length etc. Fracture characteristics depend on both geomechanical factors and rock properties, which, therefore, have a high impact on the flow response. The corresponding flow response is also subject to upscaling errors introduced by the choice of the upscaling approach. Therefore, production optimisation (well placements, perforation etc.) becomes a computationally challenging task to perform over a range of possible realisations, modelling choices and upscaling methods required to account for the associated uncertainties. We propose an approach that performs well placement optimisation over a selected sub-set of the reservoir realisations, which would represent the range of uncertainties introduced by geological and upscaling factors. The sub-set of the DFN scenarios is obtained through clustering the exhaustive set of flow response realisations in a flow metric space using a multi-dimensional scaling. The obtained clusters define a limited set of flow scenarios that can be represented by a much smaller number of selected realisations, which still adequately characterise the spread of uncertainty associated with the exhaustive set. Optimisation over a limited set of selected realisations corresponding to the range of the flow response scenarios provides a set of well configurations that maximise oil recovery and minimise the costs (produced water and the number of wells). Optimisation over multiple geological scenarios with respect to the geological uncertainty identifies the most robust development decisions than the one based on the optimisation over a single scenario. Use of multi-objective optimisations provides a greater potential variability of possible solutions, which increases the confidence in the uncertainty prediction.