人工智能培训

搜索

人工智能论文:钻孔电阻率测量反演的深度学习方法(A Deep Learning Approach to the Inversion of Borehole Re

[复制链接]
comceo 发表于 2018-10-11 09:01:34 | 显示全部楼层 |阅读模式
comceo 2018-10-11 09:01:34 453 0 显示全部楼层
人工智能论文:钻孔电阻率测量反演的深度学习方法(A Deep Learning Approach to the Inversion of Borehole Resistivity  Measurements)我们使用井眼电阻率测量来绘制地下的电性质并提高储层的生产率。当用于地面导航目的时,必须实时反转它们。在这项工作中,我们探索了使用深度神经网络(DNN)执行井下电阻率测量的快速反演的可能性。在这里,我们构建了一个近似于以下反问题的DNN:给定一组钻孔电阻率测量,DNN旨在提供周围地下的物理上有意义且数据一致的分段一维分层模型。一旦建立了DNN,我们就可以实时地进行现场测量的实际反演。我们说明了DNN在高角度井上获得的随钻测井测量的性能。
We use borehole resistivity measurements to map the electrical properties ofthe subsurface and to increase the productivity of a reservoir.When used forgeosteering purposes, it becomes essential to invert them in real time.In thiswork, we explore the possibility of using Deep Neural Network (DNN) to performa rapid inversion of borehole resistivity measurements.Herein, we build a DNNthat approximates the following inverse problem: given a set of boreholeresistivity measurements, the DNN is designed to deliver a physicallymeaningful and data-consistent piecewise one-dimensional layered model of thesurrounding subsurface.Once the DNN is built, we can perform the actualinversion of the field measurements in real time.We illustrate the performanceof DNN of logging-while-drilling measurements acquired on high-angle wells viasynthetic data.人工智能论文:钻孔电阻率测量反演的深度学习方法(A Deep Learning Approach to the Inversion of Borehole Resistivity  Measurements) UksZInNpoknKkPsc.jpg
URL地址:https://arxiv.org/abs/1810.04522     ----pdf下载地址:https://arxiv.org/pdf/1810.04522    ----人工智能论文:钻孔电阻率测量反演的深度学习方法(A Deep Learning Approach to the Inversion of Borehole Resistivity  Measurements)
回复

使用道具 举报

您需要登录后才可以回帖 登录 | 立即注册

本版积分规则 返回列表 发新帖

comceo当前离线
新手上路

查看:453 | 回复:0

快速回复 返回顶部 返回列表