US 11,353,615 B2Granted

Method of and apparatus for carrying out acoustic well logging

AssigneeReeves Wireline Technologies Limited

InventorsSaid Assous, Peter Adrian Spencer Elkington, Joanne Tudge, James Anthony Whetton

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Abstract

In acoustic well logging, for each inversion depths of a well at which logging of data occurs, acoustic log signals representative of waveforms received at acoustic receivers are processed in a frequency domain to derive field dispersion curve(s). A neural net is operated to generate formation shear slowness value(s) from the curve(s), and resulting signal(s) indicative of shear slowness values are saved, transmitted, plotted, printed or processed. An apparatus for carrying out the method includes a logging tool having at least one activatable acoustic wave source; spaced and acoustically isolated therefrom in the logging tool an array of acoustic detectors that on the detection of acoustic wave energy generate electrical or electronic log signal(s) characteristic of acoustic energy waves detected by the acoustic detector(s); and at least one processing device associated with or forming part of the logging tool for processing the log signal(s).

Claims30

Dependent claims

An apparatus for carrying out acoustic logging to prospect for minerals in a subterranean formation intersected by a well, the apparatus comprising:

a logging tool having at least one activatable source of acoustic waves;
spaced and acoustically isolated therefrom in the logging tool, a plurality of acoustic well logging receivers configured to detect acoustic wave energy that on the detection of acoustic wave energy is capable of generating one or more electrical or electronic log signals that are characteristic of acoustic energy waves detected;
and
at least one processing device associated with or forming part of the logging tool, wherein for each of a plurality of inversion depths of the well at which logging of data occurs or has occurred in generally anisotropic rock formations, the at least one processing device is configured to:
obtain a plurality of acoustic log signals received at the plurality of acoustic well logging receivers;
process in the frequency domain the plurality of acoustic log signals representative of waveforms received at the plurality of acoustic well logging receivers to give rise to one or more flexural or Stoneley mode field dispersion curves, the at least one processing device being configured to:
extract dispersion information from the transformed acoustic log signals using one or more techniques in order to derive a field dispersion curve and to generate a field frequency semblance map containing a frequency semblance curve;
and
apply a shape language model (SLM) algorithm during generation of the field frequency semblance map whereby to reduce or eliminate one or more artefacts selected from the list including noise in the field slowness-frequency semblance map values;
ambiguity in aspects of the slowness values in the field slowness-frequency semblance map at low frequencies;
the absence and/or distortion of an asymptote of the slowness-frequency semblance in the field slowness-frequency semblance map;
and/or discontinuities in the field frequency semblance curve;
operate a neural net to generate one or more formation shear slowness values from either the flexural or Stoneley mode field dispersion curves;
and
process one or more resulting signals that are indicative of the one or more formation shear slowness values to image at least a portion of the subterranean formation.

12 more elements

An apparatus for carrying out acoustic logging to prospect for minerals in a subterranean formation intersected by a well, the apparatus comprising:

a logging tool having at least one activatable source of acoustic waves;
spaced and acoustically isolated therefrom in the logging tool, a plurality of acoustic well logging receivers configured to detect acoustic wave energy that on the detection of acoustic wave energy is capable of generating one or more electrical or electronic log signals that are characteristic of acoustic energy waves detected by the one or more detector of acoustic wave energy;
and
at least one processing device associated with or forming part of the logging tool, wherein for each of a plurality of inversion depths of the well at which logging of data occurs or has occurred in generally anisotropic rock formations, the at least one processing device is configured to:
obtain a plurality of acoustic log signals received at the plurality of acoustic well logging receivers;
process in the frequency domain the plurality of acoustic log signals representative of waveforms received at the plurality of acoustic well logging receivers to give rise to one or more flexural or Stoneley mode field dispersion curves, wherein the at least one processing device is configured to:
extract dispersion information from the transformed acoustic log signals using one or more techniques in order to derive a field dispersion curve and to generate a field frequency sem blance map containing a frequency semblance curve including using a mask to isolate a region of the field frequency semblance map of interest in order to eliminate the risk of picking of coherence peaks derived from aliases or other unwanted modes in the field frequency semblance map;
select the position of the mask based on a preceding field dispersion curve;
define the mask as a pair of frequencies and a pair of slownesses;
for the first depth only, identify the mode of interest and defining the frequency range of the observed signals for that mode;
compute a slowness range as the estimated slowness+/−delta μs/ft, where the slowness is estimated from the formation properties at that depth;
and
for all depths after the first, take frequency range as the range associated with coherence values above a pre-defined threshold, and the slowness range as the estimated slowness+/−delta;
operate a neural net to generate one or more formation shear slowness values from either the flexural or Stoneley mode field dispersion curves;
and
process one or more resulting signals that are indicative of the one or more formation shear slowness values to image at least a portion of the subterranean formation.

13 more elements

A non-transitory computer-readable storage medium storing computer-readable instructions for estimating the shear slowness of a subterranean formation which, when implemented by a processor, cause the processor to:

obtain a plurality of acoustic log signals received at a plurality of acoustic well logging receivers of an acoustic logging tool in a well of the subterranean formation;
process in the frequency domain the plurality of acoustic log signals representative of waveforms received at the plurality of acoustic well logging receivers to give rise to one or more flexural or Stoneley mode field dispersion curves, the processor being configured to:
extract dispersion information from the transformed acoustic log signals using one or more techniques in order to derive a field dispersion curve and to generate a field frequency semblance map containing a frequency semblance curve including using a mask to isolate a region of the field frequency semblance map of interest in order to eliminate the risk of picking of coherence peaks derived from aliases or other unwanted modes in the field frequency semblance map;
select the position of the mask based on a preceding field dispersion curve;
define the mask as a pair of frequencies and a pair of slownesses;
for the first depth only, identify the mode of interest and defining the frequency range of the observed signals for that mode;
compute a slowness range as the estimated slowness+/−delta μs/ft, where the slowness is estimated from the formation properties at that depth;
and
for all depths after the first, take frequency range as the range associated with coherence values above a pre-defined threshold, and the slowness range as the estimated slowness+/−delta;
operate a neural net to generate one or more formation shear slowness values from either the flexural or Stoneley mode field dispersion curves;
and
process one or more resulting signals that are indicative of the one or more formation shear slowness values to image at least a portion of the subterranean formation.

9 more elements

A method of improving acoustic well logging to prospect for minerals in a subterranean formation intersected by a well, the method comprising, for each of a plurality of inversion depths of the well at which logging of data occurs or has occurred in generally anisotropic rock formations, carrying out the steps of:

obtaining, at at least one processing device, a plurality of acoustic log signals received at a plurality of acoustic well logging receivers of an acoustic logging tool in the well of the subterranean formation;
processing in the frequency domain, at the at least one processing device, the plurality of acoustic log signals representative of waveforms received at the plurality of acoustic well logging receivers to give rise to one or more flexural or Stoneley mode field dispersion curves by:
deriving a field dispersion curve by extracting dispersion information from the transformed acoustic log signals using one or more techniques in order to generate a field frequency semblance map containing a frequency semblance curve including the step of using a mask to isolate a region of the field frequency semblance map of interest in order to eliminate the risk of picking of coherence peaks derived from aliases or other unwanted modes in the field frequency semblance map;
selecting the position of the mask based on a preceding field dispersion curve;
defining the mask as a pair of frequencies and a pair of slownesses;
for the first depth only, identifying the mode of interest and defining the frequency range of the observed signals for that mode;
computing a slowness range as the estimated slowness+/−delta μs/ft, where the slowness is estimated from the formation properties at that depth;
and
for all depths after the first, taking frequency range as the range associated with coherence values above a pre-defined threshold, and the slowness range as the estimated slowness+/−delta;
operating, at the at least one processing device, a neural net to generate one or more formation shear slowness values from either the flexural or Stoneley mode field dispersion curves;
and
imaging, at the at least one processing device, at least a portion of the subterranean formation by processing one or more resulting signals that are indicative of the one or more formation shear slowness values.

9 more elements

obtaining, at at least one processing device, a plurality of acoustic log signals received at a plurality of acoustic well logging receivers of an acoustic logging tool in the well of the subterranean formation;
processing in the frequency domain, at the at least one processing device, the plurality of acoustic log signals representative of waveforms received at the plurality of acoustic well logging receivers to give rise to one or more flexural or Stoneley mode field dispersion curves by:
deriving a field dispersion curve by extracting dispersion information from the transformed acoustic log signals using one or more techniques in order to generate a field frequency semblance map containing a frequency semblance curve;
and
applying a shape language model (SLM) algorithm during generation of the field frequency semblance map whereby to reduce or eliminate one or more artefacts selected from the list including noise in the field slowness-frequency semblance map values;
ambiguity in aspects of the slowness values in the field slowness-frequency semblance map at low frequencies;
the absence and/or distortion of an asymptote of the slowness-frequency semblance in the field slowness-frequency semblance map;
and/or discontinuities in the field frequency semblance curve;
operating, at the at least one processing device, a neural net to generate one or more formation shear slowness values from either the flexural or Stoneley mode field dispersion curves;
and
imaging, at the at least one processing device, at least a portion of the subterranean formation by processing one or more resulting signals that are indicative of the one or more formation shear slowness values.

8 more elements

Dependencies are inferred from the claim text.