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Nitrogen lifting through coil tubing is widely accepted as an efficient and reliable method for reviving a dead well. In offshore fields, it becomes more challenging to conduct a nitrogen lifting by coil tubing operation given the additional cost of securing a barge, cost of rigless well interventions, logistics and continuously changing weather conditions—all of which create a very limited window of opportunity to conduct a successful nitrogen lifting operation. This paper proposes an advanced method of conducting nitrogen lifting using coil tubing, optimizing for injection rate and depth of injection. The objective of this optimization is to reduce both operation time and consumed volume of nitrogen. By leveraging emerging technologies, an advanced production modeling algorithm using well digital twins was created to maximize lifting of liquid for each unit of gas injected. The method identified several parameters which can be used for nitrogen lifting by coil tubing—such as injection rate, depth of injection, and injection methodology. Ultimately, given the high levels of computing power needed to conduct this analysis, this method is optimal in offshore rigless activities where there is a limited window of opportunity to conduct rigless operations including nitrogen lifting by using coil tubing. Well digital twin was used to model well and fluid behavior under coil tubing conditions—ultimately calculating Gaslift Utilization Factor (GUF), which is a measure of how much liquid can be lifted for each unit of gas injected. Then, in an iterative process, all the possible parameters that can be changed would be altered to reach the highest GUF factor. As a pilot to see the outcomes of the advanced modeling algorithm, this method was deployed at an offshore well to revive a well back into normal production. The results were remarkable. There was a 40% reduction in the consumed nitrogen relative to traditional methods, and the job was completed 12 hours ahead of schedule. The tool provides two scenarios, one for most optimal scenario and another option in case the first scenario does not lift the expected volumes of fluid as anticipated. In the trial test, there was no need to resort to the second scenario. There are plenty of publications related to offshore rigless activities or the use of advanced modeling techniques. None, however, bridge the gap between the two areas combining advanced modeling with rigless operations to increase the efficiency and optimize to reduce cost and time.

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