On the basis of re-recognizing the geological characteristics of the reservoir in Well A, using laboratory experiments, statistical analysis and other methods, systematically carry out injection and production dynamic analysis in Well A, analysis of damage mechanism of water injection wells, analysis of reservoir damage factors and reservoir Research on protection technology, put forward suggestions on reservoir protection technology and augmented injection technology for well An area. Mixed injection of water is likely to cause insoluble scaling and lead to blockage of water injection pipelines and reservoir pore throats. Working fluids that are not compatible with the formation enter the formation and also cause blockage of the reservoir. Therefore, oil reservoirs must not only pay attention to reservoir protection measures during water injection development It is also necessary to strictly control the quality of the injected water and strictly select additional injection fluids to effectively protect the reservoir and carry out long-term and efficient water injection operations.
In this paper, we present TEScalib, a novel extrinsic self-calibration approach of LiDAR and stereo camera using the geometric and photometric information of surrounding environments without any calibration targets for automated driving vehicles. Since LiDAR and stereo camera are widely used for sensor data fusion on automated driving vehicles, their extrinsic calibration is highly important. However, most of the LiDAR and stereo camera calibration approaches are mainly target-based and therefore time consuming. Even the newly developed targetless approaches in last years are either inaccurate or unsuitable for driving platforms.To address those problems, we introduce TEScalib. By applying a 3D mesh reconstruction-based point cloud registration, the geometric information is used to estimate the LiDAR to stereo camera extrinsic parameters accurately and robustly. To calibrate the stereo camera, a photometric error function is builded and the LiDAR depth is involved to transform key points from one camera to another. During driving, these two parts are processed iteratively. Besides that, we also propose an uncertainty analysis for reflecting the reliability of the estimated extrinsic parameters. Our TEScalib approach evaluated on the KITTI dataset achieves very promising results.
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