Automatic borehole sonic classification method and apparatus
A general-purpose workflow for automatic borehole sonic data classification to identify data into different physical categories and logging conditions, which are traditionally manually evaluated. The workflow uses machine learning techniques and physical knowledge for data classification, including pre-processing the high-dimensional high-quality dispersion modes extracted using a recently developed physical-driven ML enabled approach.
1 . A method of automatically classifying sonic data comprising:
obtaining a set of sonic data from a borehole measurement of a borehole;
using a dispersion analysis method, processing the set of sonic data from the borehole measurement;
determining scattered dispersion points from the processing;
extracting the scattered dispersion points as a smooth curve through a machine-learning automatic dipole interpretation approach, wherein a fast curve, a slow curve, a fast equivalent isotropic and homogeneous curve, and a slow equivalent isotropic and homogeneous curve are used in the machine-learning automatic dipole interpretation approach;
obtaining an equivalent isotropic and homogeneous curve for the set of sonic data from the processed borehole measurement;
inputting the smooth curve and the equivalent isotropic and homogeneous curve into a classifier; and
outputting dispersion types of the sonic data from the classifier.
2 . The method according to claim 1 , wherein the set of sonic data includes fast and slow dipole waveforms.
3 . The method according to claim 1 , wherein the processing of the set of sonic data uses Prony's method or other dispersion analysis method.
4 . The method according to claim 1 , wherein the classifier is an artificial intelligence based classifier.
5 . The method according to claim 1 , wherein the classifier can output different classification types including borehole ovality, stress induced anisotropy, vertical transverse isotropy, titled transverse isotropy, overlay with alteration or gas, and isotropic.
6 . The method according to claim 4 , wherein the artificial intelligence-based classifier is a machine learning artificial intelligence based classifier.
7 . The method according to claim 1 , further comprising:
performing a synthetic dispersion analysis prior to the outputting dispersion types of the sonic data from the classifier.
8 . The method according to claim 7 , wherein the synthetic dispersion analysis is used by the classifier as training data.
9 . The method according to claim 1 , wherein outputting dispersion types of the data from the classifier comprises outputting a strength of each dispersion type.
10 . The method according to claim 1 , comprising determining a logging tool is centered within the borehole based on the dispersion types.
11 . A method of automatically classifying data comprising:
obtaining a set of data from a borehole measurement;
using a dispersion analysis method, processing the set of data from the borehole measurement;
determining scattered dispersion points from the processing of the set of data from the borehole measurement;
extracting the scattered dispersion points as a smooth curve through a machine-learning automatic dipole interpretation approach, wherein a fast curve, a slow curve, a fast equivalent isotropic and homogeneous curve, and a slow equivalent isotropic and homogeneous curve are used in the machine-learning automatic dipole interpretation approach;
obtaining an equivalent isotropic and homogeneous curve for the set of data from the processed borehole measurement;
inputting the smooth curve and the equivalent isotropic and homogeneous curve into a classifier;
performing a synthetic dispersion analysis and feeding results to the classifier as training data; and
outputting dispersion types of the data from the classifier.
12 . The method according to claim 11 , wherein the classifier is an artificial intelligence-based classifier.
13 . The method according to claim 12 , wherein the artificial intelligence-based classifier is a machine learning artificial intelligence-based classifier.
14 . The method according to claim 11 , wherein the set of data includes fast and slow dipole waveforms.
15 . The method according to claim 11 , wherein the processing of the set of data uses Prony's method or other dispersion analysis methods.
16 . The method according to claim 11 , wherein the classifier outputs a classification type comprising borehole ovality, stress-induced anisotropy, vertical transverse isotropy, titled transverse isotropy, overlay with alteration or gas, or isotropic.
17 . A non-transitory computer readable storage medium having data stored therein representing software executable by a computer, the software including instructions for classifying data per a method, the method comprising:
obtaining a set of data from a borehole analysis of a borehole;
using a dispersion analysis method, processing the set of data from the borehole analysis;
determining scattered dispersion points from the processing of the set of data from the borehole analysis;
extracting the scattered dispersion points as a smooth curve through a machine-learning automatic dipole interpretation approach, wherein a fast curve, a slow curve, a fast equivalent isotropic and homogeneous curve, and a slow equivalent isotropic and homogeneous curve are used in the machine-learning automatic dipole interpretation approach;
obtaining an equivalent isotropic and homogeneous curve for the set of data from the borehole analysis;
inputting the smooth curve and the equivalent isotropic and homogeneous curve into a classifier;
performing a synthetic dispersion analysis and feeding results to the classifier; and
outputting dispersion types of the data from the classifier.
18 . The non-transitory computer readable storage medium according to claim 17 , wherein the set of data includes fast and slow dipole waveforms.
19 . The non-transitory computer readable storage medium according to claim 17 , wherein the processing of the set of data uses Prony's method or other dispersion analysis method.
20 . The non-transitory computer readable storage medium according to claim 17 , wherein outputting dispersion types of the data from the classifier comprises outputting a strength of each dispersion type.
21 . The non-transitory computer readable storage medium according to claim 17 , wherein the method determining a logging tool is centered within the borehole based on the dispersion types.