Title: Research on dynamic parameter identification method of shallow reservoir based on Kalman filter

Authors: Shaowei Zhang; Rongwang Yin

Addresses: School of Computer Engineering, AnHui Wenda University of Information Engineering, HeFei 230032, China ' Department of Basic Teaching and Experiment, Hefei University, Hefei 230601, China

Abstract: The identification method of reservoir parameters has the problems of low recognition accuracy and timeliness. A dynamic parameter identification method of shallow reservoir based on Kalman filter is proposed. The history fitting method is used to establish and adjust the shallow reservoir model, and the parameters and range of the reservoir model are continuously adjusted according to the actual observation data of the shallow reservoir. Kalman filter is used to filter the data of shallow reservoir and to filter out the noise and interference information. Then the dynamic parameters of shallow reservoir are identified by the method of water resistivity shale content discrimination, and the state of shallow reservoir is reflected by the shallow water resistivity. The comparison shows that the average recognition accuracy of the method can reach 95.2%, the recognition process takes only 22 seconds at most, and its recall precision value level is always high.

Keywords: historical fitting; least squares objective function; reservoir model; Kalman filtering; parameter identification.

DOI: 10.1504/IJICT.2023.127682

International Journal of Information and Communication Technology, 2023 Vol.22 No.1, pp.32 - 44

Received: 16 Nov 2020
Accepted: 24 Dec 2020
Published online: 14 Dec 2022 *