Use of Tracers To Evaluate and Optimize Scale-Squeeze-Treatment Design in the Norne Field

When squeezing scale inhibitors (SIs) into oil-production wells, the inhibitor should usually be uniformly placed in the open intervals to optimize squeeze lifetime. In wells with varying reservoir quality and/or significant crossflow, however, uniform placement is difficult to obtain. Flow diverters are frequently used to improve the chemical placement. In many cases, it is of great interest to evaluate the squeeze performance and assess the actual placement and back production of inhibitor to gather well information and thereby optimize future squeeze designs. This can be particularly interesting in subsea wells in which other types of data collection, such as production logging, are not feasible because of high intervention costs and high operational risk. This study suggests the use of tracers during squeeze treatments to evaluate the placement as an alternative to running production-logging tools (PLTs).

The main purpose of this paper is to demonstrate the applicability of tracers [in this particular study, the injection of a potassium chloride (KCl) slug in a producer well in the Norne field] to evaluate the layer flow-rate profile along the completion interval, which depends on the pressure and geological properties of each layer. The study consists of verifying the layer flow-rate profile predicted by a history-matched reservoir model. On the basis of this layer flow-rate profile, a tracer-injection program is designed, which includes two production stages at different rates. Finally, on the basis of the reservoir-model predictions, it is identified that each layer is at different pressures, which leads to a distinctive return profile.

To evaluate the match between the observed data and the simulation data, the layer flow-rate profile from the reservoir model was used to populate a specialized near-wellbore model for scale-squeeze treatments. The match between the observed data and the simulated data was good. However, the near-wellbore model, in particular the layer flow-rate profile, was fine-tuned further. Finally, the fine-tuned near-wellbore model was used to optimize future treatments more accurately with the fine-tuned layer flow-rate profile.