天然气工业2023，Vol.43Issue(12)：71-83,13.DOI:10.3787/j.issn.1000-0976.2023.12.008
基于岩屑录井图像的井壁稳定性智能预测方法
An intelligent prediction method for wellbore stability based on drilling cuttings logging images
摘要
Abstract
On drilling sites,the analysis results of rock mechanics ae usually applied to predict wellbore stability,but this method generally has low time efficiency.In this paper,an image sample library including 16 types of falling block shape and lithology is established using the real-time drilling cuttings logging image data.A wellbore instability type analysis model based on features of falling block images is established on the basis of the efficient feature extraction technology of deep leaning network.In addition,the falling block images in the images of the cuttings returned while drilling are analyzed for shape and lithology identification,so as to determine the types of encountered strata and wellbore instability.And the following research results are obtained.First,ShuffleNetV2 network is used as the intelligent system infrastructure,and XConv convolutional kernel parallel branch and SimAM attention mechanism module are introduced into the unit module,which enhances the network's attention to the landmark feature information of falling block images.Second,a multi-channel feature fusion algorithm is adopted in the design of Stage 2,Stage 3 and Stage 4 of the ShuffleNetV2 network,which retains the key profile features of the falling block.As a result,the improved ShuffleNetV2 network model achieves an accuracy of 90.56％ in identifying the shape and lithology of falling block.In conclusion,the on-site application results have verified the reliability of this method.The time from the input of returned cuttings images to the output of results is less than 1 second,and the recognition results are basically consistent with geological data and construction process conditions.This fully demonstrates that this method can meet the actual needs of rapid perception of wellbore stability on site.关键词
岩屑录井图像/轻量化网络/单元结构/SimAm注意力机制/多通道特征融合/井壁稳定性Key words
Drilling cuttings logging image/Lightweight network/Unit structure/SimAm attention mechanism/Multi-channel feature fusion/Wellbore stability引用本文复制引用
夏文鹤,唐印东,李皋,韩玉娇,林永学,吴雄军..基于岩屑录井图像的井壁稳定性智能预测方法[J].天然气工业,2023,43(12):71-83,13.基金项目
国家重点研发计划项目"井筒稳定性闭环响应机制与智能调控方法研究"(编号:2019YFA0708303)、中国石油-西南石油大学创新联合体科技合作项目"深井复杂地层钻井方式优选及提速工艺技术研究"(编号:2020CX040103). （编号:2019YFA0708303）
