Throughout the previous few years, substantial attention has been paid to the usage of smart well technologies to help improve recovery, particularly with technological improvements and an increasing expanse of opportunities in more challenging and rewarding assets. The fundamental focus has been to propose and develop workflows that integrate several surface/subsurface subprocesses and automate the entire workflow. In cases where significant investment is made to complete smart wells with remotely controlled inflow control valves (ICV), reservoir sweep becomes decisive when evaluating the efficient recovery.

Application of this technology has been challenging because it is a modern concept. This study showcases the effective application of ICVs within intelligently completed fields to satisfy the objective function by augmenting reservoir sweep and oil recovery. In this study, a commercial full-physics numerical reservoir simulator has been used to evaluate a synthetic simulation model mimicking a realistic reservoir with waterflood. The wells are installed with smart well completions using ICVs that are controlled by conditional statements called procedures. The decision parameters varied to determine if the level of ICV opening within producer wells is water-cut and well-injection rates. Then, the cumulative oil recovery is used as an objective function to increase the maximum oil recovery.

The ultimate goal is to reach the highest net present value (NPV) through having higher cumulative oil production values with the lower water injection and water production rates. The relatively high expenditure linked with installing intelligent completions within wells drive further the importance to apply and study the advantages of this technology on multiple, diverse cases coupled with specifically planned workflows.

Recent studies have shown that a robust reservoir management plan along with an effective application of ICVs within intelligently completed fields can augment reservoir sweep and oil recovery. The results of the study demonstrated the positive impact when using ICVs on NPVs calculated compared to the base case where traditional completions have been used. It is also shown that, without a robust reservoir management plan, the use of intelligent completions might not always be successful. Augmenting the performance of the reservoirs, in addition to looking at the individual well performance, forms the crux of a sound reservoir management plan.

This study, therefore, examines the big picture by following a field-wide approach rather than focusing solely on individual or near-well performance. The core of this study is to provide a framework of effective integration of data from leading performance indicators attributing to intelligent well completions, with the ultimate goal of optimizing the reservoir recovery.

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