No abstract
Smart wells combine monitoring and control technology. These smart completions are equipped with sensors to monitor well and reservoir conditions, and valves to control the inflow of fluids from the reservoir to the well. However, this intelligent equipment is located only in the downhole. The idea of this paper is to create virtual sensors located in different parts of the reservoir, which will be used to intelligent production control. This work explores a new algorithm for smart control of the gas-condensate reservoir. The proposed algorithm uses the analogies to a greedy algorithm as well as known natural intelligent structures like biological systems. The aim of this paper is to find an optimal well management strategy over the assumed life of reservoir. Gas-condensate reservoirs have a reservoir temperature located between the critical point and the cricondentherm on the reservoir fluid's pressure-temperature diagram. If bottomhole flowing pressure is less than dew point pressure, then vapour and liquid phases drop out in the porous media around the production well restricting the flow of gas. The near-well chocking can reduce the productivity of a well. Moreover, one of the goals of this paper is developing intelligent production scenarios for gas-condensate reservoir. The computer simulation has been carried out in the ECLIPSE reservoir simulator and the virtual sensors have been located within the reservoir. The actions programmed according to virtual sensors measurements yield self-modifying strategy of reservoir control. The proposed algorithm analyses reservoir behavior and changes production strategy in each time step to control the condensate bank range and, as a result maximizes the net present value (NPV). This paper presents a brief description of the created reservoir model and detailed explanation of the proposed algorithm. The next part focuses on the obtained results which indicate that this algorithm ensures intelligent control of gas-condensate reservoir and therefore the most profitable production scenario.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.