Closed Loop Reservoir Management (CLRM) aims to automate the classic reservoir management process by means of using artificial intelligence, control theory and optimization workflows. One of the tasks performed during a CLRM is the optimization process which deals with defining the best allocation of resources so as to maximize, for instance, Net Present Value. This resource allocation may be wells location, injection (or production) rate for each well, etc. In this context, the goal of this paper is to introduce an innovative approach of CLRM for optimization of waterflooding process by using Voidage Replacement Ratio (VRR) as a decision variable to generate optimum NPV of a given field. It is argued that this decision should be taken prior to any definition on well injection rates or either zone injection rate for Intelligent Wells. A simplified oil-water reservoir model is used to simulate real field behavior under a number different scenarios of VRR values defined by an optimization algorithm designed to reach maximum value of a simplified NPV function. From that initial model, three case studies are performed to answer three questions: How can different reservoir and economic scenarios impact decision of the optimum value of VRR to be pursued by reservoir managers?;How to adapt the proposed method to different development project drivers?Is it possible to improve field's performance by defining an optimum sequence of variation for VRR through time? This paper demonstrates that, although being the most common practice for waterflooding projects, a strategy of keeping VRR ~ 1 do not always lead to optimal profitability in terms of NPV. They are also discussed the many aspects to be considered when deciding on the pressure management strategy for developing a reservoir safely and economically while limited by technical and operational constraints. The studies performed demonstrate how an active VRR management during production lifetime might generate improved efficiency of company's assets aligned to its development strategy. Also, a reliable rule of thumbs guideline is presented to give operators a qualitative insight on how different geological and economic scenarios might impact on the definition of optimum strategy for VRR management.

A lean approach for qualification of inflow control is presented, involving full-scale experimental tests. The experimental data are converted into optimized well design by a simple and robust workflow, incorporating internally developed software tools for pressure-drop modelling and for well modelling with inflow control. Production data from several Equinor operated fields are analyzed, and the increased oil recovery, as well as the impact on CO2 intensity, is discussed. Results from a full-scale qualification test campaign including five different autonomous inflow control devices (AICD) are presented. The intended application is a special case with high expected production and high pressure, in nearly vertical wells, which is unlike any of the more than 175 wells with AICDs in Equinor. The ability to choke gas and allow high oil production, as well as other selected qualification criteria, were examined for the five, already commercially available, technologies in a benchmarking study.