摘要：The quality of cement in cased boreholes is related to the production and life of wells. At present, the most commonly used method is to use CBL-VDL to evaluate, but the interpretation process is complicated, and decisions associated with significant risks may be taken based on the interpretation results. Therefore, cementing quality evaluation must be interpreted by experienced experts, which is time-consuming and labor-intensive. To improve the efficiency of cementing interpretation, this paper used VGG, ResNet, and other convolutional neural networks to automatically evaluate the cementing quality, but the accuracy is insufficient. Therefore, this paper proposes a multi-scale perceptual convolutional neural network with kernels of different sizes that can extract and fuse information of different scales in VDL logging. In total, 5500 datasets in Tarim Oilfield were used for training and validation. Compared with other convolutional neural network algorithms, the multi-scale perceptual convolutional neural network algorithm proposed in this paper can evaluate cementing quality more accurately by identifying VDL logging. At the same time, this model's time and space complexity are lower, and the operation efficiency is higher. To verify the anti-interference of the model, this paper added 3%, 6%, and 9% of white noise to the VDL data set for cementing evaluation. The results show that, compared with other convolutional neural networks, the multi-scale perceptual convolutional neural network model is more stable and more suitable for the identification of cementing quality.

关键字：cementing quality evaluation; artificial intelligence; convolutional neural network; image feature extraction

ISSN号：2076-3417

卷、期、页：卷: 12期: 21

发表日期：2022-11-01

影响因子：2.679000

期刊分区（SCI为中科院分区）：三区

收录情况：SCIE（科学引文索引网络版）

发表期刊名称：APPLIED SCIENCES-BASEL

参与作者：方春飞

通讯作者：王正,杨东晗,刘慕臣

第一作者：宋先知,祝兆鹏

论文类型：期刊论文

论文概要：方春飞,王正,宋先知,祝兆鹏,杨东晗,刘慕臣，A Novel Cementing Quality Evaluation Method Based on Convolutional Neural Network，APPLIED SCIENCES-BASEL，2022，卷: 12期: 21

论文题目：A Novel Cementing Quality Evaluation Method Based on Convolutional Neural Network