| 摘要: |
| 由于FMI成像测井仪器在井下做复杂的非匀速运动,导致成像图像出现严重的畸变现象,需要通过加速校正来恢复采样数据对应的真实深度。针对传统Kalman滤波器模型-加速度校正方法对“遇卡区域”适应性较差的现象,首先对仪器在井下的运动状态进行分析,识别出仪器的遇卡区域,然后利用遇卡识别结果对传统的Kalman滤波器数学模型进行改进,首次考虑了仪器相邻遇卡现象的叠加情况。结果表明:改进后的方法不仅有效地解决了“粘滞滑动区域”的锯齿现象,而且较好地消除了“遇卡区域”的压缩拉伸现象,恢复了地层的原貌。该方法不仅适用于FMI成像测井,同样适用于其他成像测井系列,尤其有助于改善国产成像仪器图像的质量,具有推广应用价值。 |
| 关键词: 成像测井 遇卡识别 卡尔曼滤波器模型 加速度校正 |
| DOI：10.3969/j.issn.1673-5005.2013.06.006 |
| 分类号:P 631.811 〖HTH〗 |
| 基金项目:“十二五”国家科技重大专项 (2011ZX05014-004);重庆市科委自然科学基金计划资助项目(cstc2012jjA90009);重庆市教委科学技术研究项目(KJ121420);重庆科技学院校内科研基金资助项目(CK20111312和CK2013Z04) |
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| Modified Kalman filter model-acceleration correction method based on sticking recognition detection |
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LAI Fu-qiang1, SUN Jian-meng2, LI Guang-yun1
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(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)
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| 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 |
