公开(公告)号：US11835674B1

公开(公告)日：2023-12-05

申请号：US18053143

申请日：2022-11-07

Applicant: Schlumberger Technology Corporation

Inventor： Bo Fan ,  Maja Skataric ,  Sandip Bose ,  Shuchin Aeron ,  Smaine Zeroug

IPC: G01V1/50 ,  E21B47/005

CPC classification number: G01V1/50 ,  E21B47/005 ,  E21B2200/22 ,  G01V2210/324 ,  G01V2210/60

Abstract: A sonic tool is activated in a well having multiple casings and annuli surrounding the casing. Detected data is preprocessed using slowness time coherence (STC) processing to obtain STC data. The STC data is provided to a machine learning module which has been trained on labeled STC data. The machine learning module provides an answer product regarding the states of the borehole annuli which may be used to make decision regarding remedial action with respect to the borehole casings. The machine learning module may implement a convolutional neural network (CNN), a support vector machine (SVM), or an auto-encoder.

公开(公告)号：US08755249B2

公开(公告)日：2014-06-17

申请号：US13723080

申请日：2012-12-20

Applicant: Schlumberger Technology Corporation

Inventor： Shuchin Aeron ,  Sandip Bose ,  Henri-Pierre Valero ,  Venkatesh Saligrama

IPC: G01V1/50 ,  G01V1/48 ,  G01V1/30

CPC classification number: G01V1/50 ,  G01V1/30 ,  G01V1/307 ,  G01V2210/00

Abstract: Slowness dispersion characteristics of multiple possibly interfering signals in broadband acoustic waves as received by an array of two or more sensors are extracted without using a physical model. The problem of dispersion extraction is mapped to the problem of reconstructing signals having a sparse representation in an appropriately chosen over-complete dictionary of basis elements. A sparsity penalized signal reconstruction algorithm is described where the sparsity constraints are implemented by imposing a l1 norm type penalty. The candidate modes that are extracted are consolidated by means of a clustering algorithm to extract phase and group slowness estimates at a number of frequencies which are then used to reconstruct the desired dispersion curves. These estimates can be further refined by building time domain propagators when signals are known to be time compact, such as by using the continuous wavelet transform.

Abstract translation: 在不使用物理模型的情况下提取由两个或更多个传感器的阵列接收的宽带声波中的多个可能干扰信号的慢度色散特性。 色散提取的问题被映射到在基本元素的适当选择的过完整字典中重建具有稀疏表示的信号的问题。 描述了稀疏惩罚信号重建算法，其中稀疏约束通过施加l1范数类型惩罚来实现。 提取的候选模式通过聚类算法进行合并，以提取多个频率的相位和组慢度估计，然后用于重建所需的色散曲线。 当已知信号是时间紧凑的，例如通过使用连续小波变换，可以通过构建时域传播器来进一步改进这些估计。

公开(公告)号：US11493659B2

公开(公告)日：2022-11-08

申请号：US16759667

申请日：2018-10-25

Applicant: SCHLUMBERGER TECHNOLOGY CORPORATION

Inventor： Bo Fan ,  Maja Skataric ,  Sandip Bose ,  Shuchin Aeron ,  Smaine Zeroug

IPC: G01V1/50 ,  G06N20/10 ,  E21B47/005 ,  G01V1/46 ,  G06N3/04 ,  G06N3/08

Abstract: A sonic tool is activated in a well having multiple casings and annuli surrounding the casing. Detected data is preprocessed using slowness time coherence (STC) processing to obtain STC data. The STC data is provided to a machine learning module which has been trained on labeled STC data. The machine learning module provides an answer product regarding the states of the borehole annuli which may be used to make decision regarding remedial action with respect to the borehole casings. The machine learning module may implement a convolutional neural network (CNN), a support vector machine (SVM), or an auto-encoder.

公开(公告)号：US20210181366A1

公开(公告)日：2021-06-17

申请号：US16759667

申请日：2018-10-25

Applicant: SCHLUMBERGER TECHNOLOGY CORPORATION

Inventor： Bo Fan ,  Maja Skataric ,  Sandip Bose ,  Shuchin Aeron ,  Smaine Zeroug

IPC: G01V1/50 ,  G01V1/46 ,  E21B47/005 ,  G06N3/08 ,  G06N3/04 ,  G06N20/10

Abstract: A sonic tool is activated in a well having multiple casings and annuli surrounding the casing. Detected data is preprocessed using slowness time coherence (STC) processing to obtain STC data. The STC data is provided to a machine learning module which has been trained on labeled STC data. The machine learning module provides an answer product regarding the states of the borehole annuli which may be used to make decision regarding remedial action with respect to the borehole casings. The machine learning module may implement a convolutional neural network (CNN), a support vector machine (SVM), or an auto-encoder.

公开(公告)号：US10317545B2

公开(公告)日：2019-06-11

申请号：US13712985

申请日：2012-12-13

Applicant: Schlumberger Technology Corporation

Inventor： Shuchin Aeron ,  Sandip Bose ,  Henri-Pierre Valero

IPC: G01V1/28 ,  G01V1/36

Abstract: Methods and apparatus for waveform processing are disclosed. An example method includes determining shrinkage estimators in a Discrete Radon transform domain based on semblance of waveform data and de-noising the waveform data using a processor and the shrinkage estimators to enable the identification of weak signals in the waveform data.

公开(公告)号：US20140169130A1

公开(公告)日：2014-06-19

申请号：US13712971

申请日：2012-12-13

Applicant: SCHLUMBERGER TECHNOLOGY CORPORATION

Inventor： Shuchin Aeron ,  Sandip Bose ,  Henri-Pierre Valero

IPC: G01V1/48

CPC classification number: G06F17/148 ,  G01V1/364 ,  G01V2210/46

Abstract: Methods and apparatus for waveform processing are disclosed. An example method includes representing waveform data using space time propagators in the Discrete Radon Transform Domain. The method also includes identifying signals within the represented waveform data using a Sparisty Penalized Transform.

Abstract translation: 公开了用于波形处理的方法和装置。 示例性方法包括使用离散氡变换域中的空间时间传播器来表示波形数据。 该方法还包括使用Sparisty Penalized变换来识别所表示的波形数据内的信号。

公开(公告)号：US20130238248A1

公开(公告)日：2013-09-12

申请号：US13712985

申请日：2012-12-13

Applicant: SCHLUMBERGER TECHNOLOGY CORPORATION

Inventor： Shuchin Aeron ,  Sandip Bose ,  Henri-Pierre Valero

IPC: G01V1/28

CPC classification number: G01V1/28 ,  G01V1/36 ,  G01V1/364 ,  G01V2210/32 ,  G01V2210/324

Abstract: Methods and apparatus for waveform processing are disclosed. An example method includes determining shrinkage estimators in a Discrete Radon transform domain based on semblance of waveform data and de-noising the waveform data using a processor and the shrinkage estimators to enable the identification of weak signals in the waveform data.

Abstract translation: 公开了用于波形处理的方法和装置。 示例性方法包括基于波形数据的相似性确定离散氡变换域中的收缩估计器，并使用处理器和收缩估计器对波形数据进行去噪，以便能够识别波形数据中的弱信号。