Abstract
In this chapter, we propose the idea of using supercritical carbon dioxide (SC-CO2) as a cleanout fluid in the water-sensitive formation due to its liquid-like density and gas-like viscosity. A back-propagation artificial neural network was successfully employed to match operation parameters and SC-CO2 flow velocity. A comprehensive model was developed to optimize the operation parameters according to two strategies: the cost-saving strategy and the local optimal strategy. This chapter is helpful to understand the distinct characteristics of SC-CO2 flow. It is the first time the back-propagation artificial neural network model is introduced to analyze the flow field during solids cleanout in horizontal wells.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
