国家冰川冻土沙漠科学数据中心

元数据

名称	Deep Learning Approach of Drilling Decision for Subhorizontal Drain Geosteering Based on APC-LSTM Model（基于 APC-LSTM 模型的次水平排水地质导向钻井决策深度学习方法）
科技资源标识	CSTR:11738.14.NCDC.XDA14.PP6132.2024
DOI	10.2118/210605-PA
数据共享方式	开放下载
摘要	Steering drilling is used for exploring oil, natural gas, and other liquid and gaseous minerals. Steering drilling consists of high-efficiency drill bits, steering power drilling tools, and logging while drilling (LWD) and is used in petroleum drilling engineering. This paper mainly discusses subhorizontal drain geosteering, one of the methods of guided subhorizontal drilling. We use the currently popular deep learning method to conduct intelligent guided drilling. Geosteering is a sequential drilling decision process under uncertain stratum environment. However, the current geosteering drilling process relies heavily on manual work and has no use of temporal context. This paper aims to solve decision-making of geosteering in deep well (between 4500 and 6000 km) or ultradeep well (between 6000 and 9000 km). To this end, we make three contributions: (1) a wide-angle eye mechanism to obtain more geological information; (2) an asymmetric peephole convolutional long short-term memory (APC-LSTM) approach for geosteering drilling decision, whose input data were assembled with the wide-angle eye mechanism; and (3) use of the deep convolution generative adversarial networks (DCGAN) model to generate simulated logging data and conduct experiments in the simulation environment to verify our proposed method. APC-LSTM can capture the spatial-temporal correlation better between different strata for decision-making. Meanwhile, the APC-LSTM drilling decision model achieved better performance than other advanced methods in two drilling data sets. Tested in a simulative drilling environment, our proposed model achieves excellent application effect. Moreover, our method has been applied to the wells of oil field in practice
学科分类	170.20
关键词	upstream oil & gas neural network deep learning
作者	朱丹丹,戴欣萍,刘溢,王菲,罗晓婷,陈冬,叶智慧
数据量	3.6 MiB
论文类型：	journal
期刊名称：	SPE Drilling & Completion
出版时间：	2022-09-01

引用和标注

数据引用

朱丹丹,戴欣萍,刘溢,王菲,罗晓婷,陈冬,叶智慧. Deep Learning Approach of Drilling Decision for Subhorizontal Drain Geosteering Based on APC-LSTM Model（基于 APC-LSTM 模型的次水平排水地质导向钻井决策深度学习方法）. 国家冰川冻土沙漠科学数据中心(http://www.ncdc.ac.cn), 2024. https://cstr.cn/CSTR:11738.14.NCDC.XDA14.PP6132.2024.

许可协议

本作品采用知识共享署名 4.0 国际许可协议进行许可。

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项目信息详情

项目名称：超深层导向钻井关键技术
项目编号：XDA14040000
项目类别：中国科学院战略性先导科技专项（A类）