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Modified Kalman filter model-acceleration correction method based on sticking recognition detection
LAI Fu-qiang1, SUN Jian-meng2, LI Guang-yun1
(1.Chongqing Key Laboratory of Complex Oil & Gas Fields Exploration and Development, Chongqing University of Science and Technology, Chongqing 401331, China;2.School of Geosciences in China University of Petroleum, Qingdao 266580, China)
Abstract:
The complicated non-uniform movement of the down-hole formation microscanner image (FMI) equipment causes severe imaging distortion and therefore conceals important formation features in the obtained borehole images. Acceleration correction is therefore applied in order to recover the true depth of the sampled data. The traditional Kalman filter model-acceleration correction method can not adapt well to the sticking region, so a modified Kalman filter model-acceleration correction method based on sticking detection is proposed. In the method, the movement of the down-hole equipment is analyzed to detect the sticking region, and the overlapping region between two adjacent sticking events is taken into consideration to improve the Kalman filter model parameters. The test results show that not only the saw tooth phenomenon of slipping region is effectively eliminated, but also the compressed stretch image of sticking region is successfully corrected to recover the real features of formation. This method applies to not only the FMI logging tool, but also other imaging logging series, and illustrates great improvement in the image quality of the domestic imaging equipment.
Key words:  borehole image logging  sticking recognition  Kalman filter model  acceleration correction