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The research on the mechanism of repeated fracturing fractures shows that the pressure drop caused by the initial fracturing fractures and the production activities of the oil layer in the repeated fracturing wells leads to changes in the stress field near the well, which causes the fractures to re-fract during the repeated fracturing process. Combining with the low permeability characteristics of Tongxi fault block reservoir in North China Oilfield, optimization of repeated fracturing parameters and selection of fracturing materials were carried out, and the principle of well selection and layer selection was put forward. The repeated fracturing technology was successfully implemented in the Tong47-43x well and achieved a good oil-increasing effect, which provided important technical measures for the next development of the Tongxi fault block reservoir.

Scale variation is one of the key challenges in the object detection area, which limits the precision of detection methods like Single shot multibox detector (SSD). This paper proposes a detection method based on SSD, which focuses on handling scale variation and better detection performance of small objects. Our method, called TridentSSD, have an architecture with three branches, which are respectively responsible for detecting different scales of objects, solving the problem of scale variation while training. Then we augment small object branch with deconvolution module and feature fusion methods to improve precision, especially for small object detection. During training, we modify the original matching rules to generate training samples. Consequently, we can use objects of different scales to train the corresponding branches respectively. Finally, Experiments have done on both PASCAL VOC2007 and VOC2012 datasets. Results show that, with an input size of 300×300, our TridentSSD achieves better performance compared to the benchmark method SSD.

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