Dynamic data reconciliation, Wired drillpipe, Industrial data, Moving horizon estimation, Kalman filter
Measurement technology is advancing in the oil and gas industry. Factors such as wireless transmitters, reduced cost of measurement technology, and increased regulations that require active monitoring tend to increase the number of available measurements. There is a clear opportunity to distill the recent flood of measurements into relevant and actionable information. Common methods to do this include a filtered bias update, implicit dynamic feedback, Kalman filtering, and moving horizon estimation. The purpose of these techniques is to validate measurements and align imperfect mathematical models to the actual process. Additionally, they can determine a best-estimate of the current state of the process and any potential disturbances. These methods allow potential improvements in earlier detection of disturbances, process equipment faults, and improved state estimates for optimization and control.
Original Publication Citation
Hedengren, John D., and Ammon N. Eaton. "Overview of estimation methods for industrial dynamic systems." Optimization and Engineering (2015): 1-24.
BYU ScholarsArchive Citation
Hedengren, John and Eaton, Ammon, "Overview of Estimation Methods for Industrial Dynamic Systems" (2015). All Faculty Publications. 1668.
Ira A. Fulton College of Engineering and Technology
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