Adaptive Workflows For Artifact Identification In Electromagnetic Pipe Inspection

Patent metadata

Field	Value
Publication number	US-2020309986-A1
Application number	US-201816479553-A
Country	US
Kind code	A1
Filing date	Jun 25, 2018
Priority date	Jun 25, 2018
Publication date	Oct 1, 2020
Grant date	—

Abstract

A method for identifying an artifacts disposed on concentric pipes may comprise disposing an electromagnetic logging tool into a first wellbore, broadcasting an electromagnetic field from a transmitter disposed on the electromagnetic logging tool, energizing a casing with the electromagnetic field, and recording a secondary electromagnetic field from the casing at a plurality of depths and at a plurality of frequencies. The method may further comprise picking a first plurality of artifacts in the first signal, constructing a target value matrix from the first plurality of artifacts, producing a first input matrix from the first signal and a first well plan, and constructing a predictor from the first input matrix and the target value matrix. Additionally, disposing the electromagnetic logging tool into a second wellbore and producing a second plurality of artifacts from the predictor and the second input matrix.

First claim

What is claimed is:

1 . A method for identifying an artifact disposed in a wellbore comprising:
disposing an electromagnetic logging tool into a first wellbore; broadcasting an electromagnetic field from a transmitter disposed on the electromagnetic logging tool; energizing a casing with the electromagnetic field; recording a secondary electromagnetic field from the casing at a plurality of depths and at a plurality of frequencies; picking a first plurality of artifacts in the first signal; constructing a target value matrix from the first plurality of artifacts; producing a first input matrix from the first signal and a first well plan; constructing a predictor from the first input matrix and the target value matrix; disposing the electromagnetic logging tool into a second wellbore; broadcasting a signal from a second transmitter disposed on the second electromagnetic logging tool; recording a second signal; producing a second input matrix from the second signal; producing a second plurality of artifacts from the predictor and the second input matrix; and displaying the second plurality of artifacts to an operator.

2 . The method of claim 1 , further comprising:
creating an input sample matrix of a pipe configuration as a function of a depth and a pipe index; creating an input sample matrix of a signal as a function of the depth and a data channel; and concatenating the input sample matrix of a pipe configuration and the input sample matrix of a signal into a concatenated matrix; and assigning the concatenated matrix to the target value matrix.

3 . The method of claim 1 , wherein the first plurality of artifacts are picked by the operator.

4 . The method of claim 1 , further comprising picking at least one reference artifact from an external information source, comparing at least one of the second plurality of artifacts to the at least one reference artifact, and producing a quality value from the comparison of the at least one of the second plurality of artifacts to the at least one reference artifact.

5 . The method of claim 4 , further comprising removing at least one artifact from the first plurality of artifacts, and designating it as reference artifact.

6 . The method of claim 1 , wherein the first plurality of artifacts in the first signal are deviations corresponding to a plurality of collars disposed on one or more concentric pipes.

7 . The method of claim 6 , further comprising:
creating an input sample matrix as a function of a depth and a pipe index; determining a proximity width; convolving the input sample matrix with a window of a selected shape with a width equal to the proximity width into a convolved matrix; and assigning the convolved matrix to the target value matrix.

8 . The method of claim 6 , further comprising picking a first plurality of collars with an automated collar locator.

9 . The method of claim 8 , further comprising adjusting the automated collar locator based on a second plurality of collars.

10 . The method of claim 6 , further comprising displaying to the operator a first plurality of collar picks and a second plurality of collar picks.

11 . The method of claim 6 , further comprising appraising a second plurality of collar picks.

12 . The method of claim 1 , wherein the first plurality of artifacts in the first signal are deviations corresponding to a plurality of defects on one or more concentric pipes.

13 . The method of claim 12 , wherein the plurality of defects comprise corrosion, abrasion, scratch, hole, cut, groove, scaling, manufacturing tolerance, parted casing, magnetic permeability variation, or conductivity variation.

14 . The method of claim 12 , further comprising
creating an input sample matrix as a function of depth and a pipe configuration; concatenating a plurality of input sample matrices corresponding to different types of defects in to a concatenated matrix; and assigning the concatenated matrix to the target value matrix.

15 . The method of claim 14 , further comprising appending an auxiliary input matrix to the input sample matrix.

16 . The method of claim 15 , further comprising estimating the auxiliary input matrix and the target value matrix with an inversion.

17 . The method of claim 16 , wherein the auxiliary input matrix is from a first forward modeling code and the target value matrix is from a second forward modeling code, where the first forward modeling code is more computationally efficient but less accurate than the second forward modeling code.

18 . The method of claim 16 , wherein the target value matrix is post-processed.

19 . The method of claim 16 , further comprising estimating at least one parameter of the inversion with the predictor.

20 . The method of claim 12 , further comprising:
creating an input sample matrix as a function of depth and a pipe index; creating an input sample matrix of a pipe configuration as a function of depth and the pipe index; concatenating the input sample matrix and the input sample matrix of a pipe configuration into a concatenated matrix; and assigning the concatenated matrix to the target value matrix.

21 . The method of claim 20 , wherein a pipe configuration parameter is a nominal thickness, a nominal inner diameter, a nominal outer diameter, a nominal magnetic permeability, or a nominal conductivity of pipe.

22 . The method of claim 20 , estimating at least one pipe configuration parameters by the predictor.

23 . A method for identifying an artifact disposed in a wellbore comprising:
producing a forward model; producing a first pipe configuration; simulating the forward model and the first pipe configuration to create a first signal; picking a first plurality of artifacts in the first signal; constructing a target value matrix from the first plurality of artifacts; producing a first input matrix from the first signal and a first well plan; constructing a predictor from the first input matrix and target value matrix; producing a second input matrix from a second signal created by the forward model with a second pipe configuration model; producing a second plurality of artifacts from the predictor and the second input matrix; and displaying the second plurality of artifacts to an operator.

24 . The method of claim 23 , further comprising adding variations to the second pipe configuration, wherein pipe configuration parameters are a magnetic permeability of pipes, a conductivity of pipes, a diameter of pipes, an inner diameter of pipes, an outer diameter of pipes, a eccentricity of pipes, a thickness of pipes, a core properties of EM pipe inspection tool transmitters, a core properties of EM pipe inspection tool receivers, a logging depth, a location of an artifact, or an overlap of artifacts on different pipes.

25 . The method of claim 23 , further comprising calibrating the forward model with real data.

Assignees

• Halliburton Energy Services Inc

Inventors

• Donderici Burkay
• Capoglu Ilker R
• Fouda Ahmed Elsayed

Classifications

• E21B47/006

  Detection of corrosion or deposition of substances · CPC title

• E21B47/00

  Survey of boreholes or wells (monitoring pressure or flow of drilling fluid E21B21/08) · CPC title

• E21B47/092

  by detecting magnetic anomalies · CPC title

• G01N27/85

  using magnetographic methods · CPC title

• G01V3/28

  using induction coils · CPC title