Abstract
We address the problem of designing a data-driven soft sensor to estimate the downhole pressure in gas-lifted oil wells. Such application is based on a two-step procedure. In the first step, black-box models are identified offline using experimental data. In the second step, recursive predictions of these models are combined with current measured data (of variables other than the downhole pressure) by means of an interacting bank of unscented Kalman filters. In doing so, a closed-loop model prediction is performed. Results indicate that such closed-loop scheme improves estimation accuracy compared to the free-run model prediction.
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.
