The method of continuous gas lift has been commonly used in the oil industry to enhance production. Existing optimization models consider an approximate performance curve anchored by production test data, often disregarding reservoir uncertainty. We propose a robust optimization model that jointly considers the most recent data and an uncertainty set for the reservoir pressure, a critical parameter that is usually not measured precisely. As a result, we obtain what we call a “bow-tie” uncertainty set for the performance curves, in which the performance uncertainty increases when we move away from the production test’s operational point. We test our model with real data from an offshore oil platform and compare it against a fully deterministic model. We show superior out-of-sample performance for the robust model under different probability distributions of the reservoir pressure.

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