Streamlines provide detailed information about dynamic fluid paths and connectivity between injectors and producers. Fuzzy logic can simulate human thinking and handle different categories of information including linguistic, imprecise, approximate, and overlapping to name a few. This paper presents a genuine approach for field injection optimization using a streamline-based fuzzy logic system.

In a previous paper, we presented an adaptive streamline based fuzzy logic system that uses three input parameters namely injection efficiency (IE), water cut (WC), and injection loss to aquifer to assign an injector ranking index (IRI) according to injector performance. In this paper, we enhanced the streamline based fuzzy logic workflow to smartly redistribute water injection by accounting for operational constraints and number of supported producers in a pattern in addition to the IRI. The new workflow examines the low performers (i.e., low and medium IRI categories) and assigns different injection reduction factors for each injector in these categories. Then, the total amount of reduced injection is assigned to high performers (i.e., high IRI) while ensuring no operational constraint is violated, such as BHP and capacity of pumps.

This approach has been tested on a large-scale DPDP simulation model. The area of interest in this field has two rows of injectors: downdip and updip. The updip injectors are the focus of the study. The results of this case study show noticeable improvements in injection efficiency for most wells in the area of interest ensuring better sweep, good pressure support, and improving cumulative oil production.

We believe combining both technologies, namely streamlines and fuzzy logic, can provide reservoir engineers with a robust decision-making tool to attain a more successful field-wide water injection strategy.

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