Scale inhibitor (SI) squeeze treatments are one of the most common techniques to prevent downhole scale formation. In this paper, we present the optimization of treatment design for multiple wells included in offshore campaigns. Two offshore fields with 8 and 12 production wells in west Africa were considered that are separately treated via yearly squeeze campaigns. The wells included in each campaign are treated in a single trip of the supply vessel. Based on the storage capacity of the vessel, the available volume of SI onboard should be optimally allocated to each of the wells (having different properties and water production rates), so that they are all protected from scaling for 1 year until the next campaign is carried out. A hybrid optimization methodology was applied to optimize the squeeze campaign design.

The gradient descent (GD) algorithm was first applied to derive the squeeze “isolifetime proxies” related to each well. Each proxy includes all the possible squeeze designs that result in 365 days of squeeze lifetime in the well. Using these proxies, any combination of wells’ squeeze designs could be nominated as the campaign design, because that would result in treating all wells until the next campaign. The multiobjective particle swarm optimization (MOPSO) technique was implemented to optimize the campaign design by simultaneously minimizing the total SI volume and the total injection time for the whole campaign. Minimizing the total pumping time would consequently minimize the deferred oil volume and the total cost of squeezes in the field.

Finally, the Pareto Front was identified for each field, showing the most optimum campaign designs. The Pareto Front was shown to be a valuable tool for the operator to make a trade-off between the size of the vessel and the injection time; that is, to use a bigger vessel to transport more inhibitor to the wells or to use a smaller one but for a longer time to inject more water during the squeeze treatments in the field. A cost analysis was performed to identify the most optimum deployment plan providing the most optimum inhibitor allocation strategy, including the optimum inhibitor volume and the optimum injection time for each campaign.

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