Methods and systems for processing borehole dispersive waves with a physics-based machine learning analysis

Patent metadata

Field	Value
Publication number	US-11693141-B2
Application number	US-201816606690-A
Country	US
Kind code	B2
Filing date	Dec 6, 2018
Priority date	Dec 6, 2018
Publication date	Jul 4, 2023
Grant date	Jul 4, 2023

Abstract

Systems and methods are provided for determining a formation body wave slowness from an acoustic wave. Waveform data is determined by logging tool measuring the acoustic wave. Wave features are determined from the waveform data and a model is applied to the wave features to determine data-driven scale factors The data-driven scale factors can be used to determine a body wave slowness within a surrounding borehole environment and the body wave slowness can be used to determine formation characteristics of the borehole environment.

First claim

What is claimed is:

1. A method for processing borehole wave modes, the method comprising:
measuring, by an acoustic logging tool within a borehole passing through a formation, an acoustic wave to generate waveform data of an acoustic wave;
determining wave features from the waveform data, wherein the wave features are associated with a quality of the waveform data;
generating one or more data-driven scale factors by applying a trained model to the wave features, wherein the one or more data-driven scale factors adaptively balance a data-driven process and a model-based process based on the quality of the waveform data;
determining data-driven scale parameters from different wave features by comparing wave features generated by the trained model to directly measured wave features;
determining final parameters of the data-driven scale factor by combining the different wave features;
generating a formation body wave slowness by performing a hybrid process based on the one or more data-driven scale factors, wherein the hybrid process is a combination of the data-driven process and the model-based process, such that the hybrid process is more reliant on the data-driven model when a larger measured frequency length is present;
adjusting a downhole operational parameter based at least in part on the formation body wave slowness, wherein the downhole operational parameter comprises one of a drilling parameter, a logging parameter, a completion parameter, and a production parameter; and
providing the formation body wave slowness to a user interface and/or saving the formation body wave slowness to a log, wherein the formation body wave slowness provides wellbore and/or formation characteristics throughout drilling.

2. The method of claim 1 , further comprising: determining formation characteristics of a borehole environment; and modeling a fluid-filled borehole based on the determined formation characteristics of the borehole environment.

3. The method of claim 2 , further comprising calculating one of a full waveform response of the modeled fluid-filled borehole environment, an Airy-phase frequency, and a cut-off frequency.

4. The method of claim 1 , further comprising calculating one of a semblance value of target waves, an effective data length in frequency, and a minimum effective frequency.

5. The method of claim 1 , further comprising: training a model with synthetic data to generate the trained model; and applying the trained model to field data.

6. The method of claim 5 , further comprising: generating the synthetic data, wherein the synthetic data includes waveform data; adding noise to the generated synthetic data; determining wave features of the synthetic waveform data; determining data-driven scales by inverting the wave features of the synthetic waveform data; and training the model with the wave features of the synthetic waveform data and the data-driven scales.

7. The method of claim 1 , further comprising: determining data-driven scale parameters from different wave features by comparing wave features generated by the model to directly measured wave features; and determining final parameters of the data-driven scale factor by combining the different wave features.

8. The method of claim 1 , wherein the hybrid process comprises performing one of a fully modeling-based process, a fully data-driven process, and a limited data-driven process.

9. The method of claim 8 , wherein the limited data-driven process comprises: generating a set of parameter ranges of the data-driven process from the one or more data-driven scale factors; and performing an inversion processing comprising limiting adjustable parameters in a fixed range.

10. The method of claim 1 , further comprising generating a visualization of one of a waveform of the received acoustic wave, a time-domain semblance map, a misfit, and a fitting quality of the curve.

11. A system for processing borehole wave modes, the system comprising: a processor; and a memory comprising instructions which, when executed, cause the processor to:
measure, by an acoustic logging tool within a borehole passing through a formation, an acoustic wave to generate waveform data of the acoustic wave;
determine wave features from the waveform data, wherein the wave features are associated with a quality of the waveform data; generate one or more data-driven scale factors by applying a trained model to the wave features, wherein the one or more data-driven scale factors adaptively balance a data-driven process and a model-based process based on the quality of the waveform data;
determine data-driven scale parameters from different wave features by comparing wave features generated by the trained model to directly measured wave features;
determine final parameters of the data-driven scale factor by combining the different wave features;
generate a formation body wave slowness by performing a hybrid process based on the one or more data driven-scale factors, wherein the hybrid process is a combination of the data-driven process and the model-based process, such that the hybrid process is more reliant on the data-driven model when a larger measured frequency length is present;
adjusting a downhole operational parameter based at least in part on the formation body wave slowness, wherein the downhole operational parameter comprises one of a drilling parameter, a logging parameter, a completion parameter, and a production parameter; and
providing the formation body wave slowness to a user interface and/or saving the formation body wave slowness to a log, wherein the formation body wave slowness provides wellbore and/or formation characteristics throughout drilling.

12. The system of claim 11 , wherein the memory further comprises instructions which, when executed, cause the processor to: determine formation characteristics of a borehole environment; and model a fluid-filled borehole environment based on the borehole environment.

13. The system of claim 11 , wherein the memory further comprises instructions which, when executed, cause the processor to: calculate one of a semblance value of target waves, an effective data length in frequency, a minimum effective frequency, a full waveform response of a modeled fluid-filled borehole environment, an Airy-phase frequency, and a cut-off frequency.

14. The system of claim 11 , wherein the memory further comprises instructions which, when executed, cause the processor to: generate synthetic data including waveform data; add noise to the generated synthetic data; determine wave features of the synthetic waveform data; determine data-driven scales by inverting the wave features of the synthetic waveform data; train a model with the wave features of the synthetic waveform data and the data-driven scales to generate the trained model; and apply the trained model to field data.

15. The system of claim 11 , wherein the memory further comprises instructions which, when executed, cause the processor to: determine data-driven scale parameters from different wave features by comparing wave features generated by the model to directly measured wave features; and determine final parameters of the data-driven scale factor by combining the different wave features.

16. The system of claim 11 , wherein the hybrid process comprises performing one of a full modeling-based process, a fully data-driven process, and a limited data-driven process, and wherein the memory further comprises instructions which, when executed, cause the processor to: generate a set of parameter ranges of the data-driven processing from the one or more data-driven sca

Assignees

• Halliburton Energy Services Inc

Inventors

• Wang Ruijia
• Granville John

Classifications

• G01V2210/66

  Subsurface modeling · CPC title

• G01V1/282

  Application of seismic models, synthetic seismograms · CPC title

• G06N20/00

  Machine learning · CPC title

• G01V2210/47

  Slowness, e.g. tau-pi · CPC title

• G01V1/345

  Visualisation of seismic data or attributes, e.g. in 3D cubes · CPC title