Waterflood Optimization and its impact using Intelligent Digital Oil Field (iDOF) Smart Workflow Processes: A Pilot Study in Sabriyah Mauddud, North Kuwait

Abstract The Sabriyah Mauddud (SAMA) limestone reservoir of North Kuwait is a large oil reservoir currently under waterflooding to meet production targets, and optimization of water injection is critical. The engineers have begun monitoring, diagnosing, and optimizing this waterflood through the implementation of an intelligent digital oil field (iDOF) program called KwIDF (Kuwait Integrated Digital Field). Various workflows have been developed for the visualization and analysis of waterflooding performance. Such workflows use real-time production data to derive and update daily water saturation and reservoir pressure from numerical simulation. The ultimate goal of this workflow is to identify and define actions to improve water injection and production over the short and medium terms. The transformation of raw data into information is achieved through intelligent, automated work processes, which are referred to in this paper as "smart workflows." These smart workflows assist engineers with daily well surveillance activities, aid productivity, and help improve the speed and quality of their decision-making process. The workflow presented is related to waterflood management; however, it is part of a family of smart workflows (e.g., electrical submersible pump [ESP], subsurface waterflood optimization [SWFO], and smart production surveillance [SPS] workflows), which have been discussed in other work. These smart workflows and a commercial software, Oil Field Manager (OFM) for Decline Curve Analysis (DCA), have also been used in waterflood optimization for the optimum production allowable selection, with the ultimate goal of improving asset performance. This paper shares how these smart workflows are used for waterflood optimization and illustrates a case study of an injection pattern optimization and its impact on production. Comprehensive monitoring of the waterflood has been performed using real-time data through iDOF smart workflow processes, illustrating the analysis of real-time data, which includes pump intake pressure, flowline pressure, water cut (WC), and flowing bottomhole pressures. Other data used include static bottomhole pressure, production log results, and production flow tests. Monitoring is planned in such a way to understand waterflood movement within the reservoir from injector to producers and its impact on the production behavior of the surrounding producers. A positive response in terms of pressure maintenance and production increase has been observed and confirmed using various analytical tools.