Rock property measurements while drilling

Patent metadata

Field	Value
Publication number	US-9540928-B2
Application number	US-201113575599-A
Country	US
Kind code	B2
Filing date	Feb 4, 2011
Priority date	Feb 5, 2010
Publication date	Jan 10, 2017
Grant date	Jan 10, 2017

Abstract

Described herein is a method and system for characterizing in-ground rock types from measurement-while-drilling data in a mining environment. The method includes the steps of drilling holes at a plurality of selected locations within a region of interest; collecting measurements while drilling to obtain an array of data samples ( 162 ) indicative of rock hardness at various drilling depths in the drill hole locations; obtaining a characteristic measure ( 163 ) of the array of data samples; performing Gaussian Process regression ( 164 ) on the characteristic measure; and applying boundary detection ( 166 ) to the rock hardness output data obtained from the Gaussian process model to identify the distribution ( 280 ) of at least one cluster of rock type within the region of interest.

First claim

What is claimed is:

1. A method for generating a continuous rock type distribution map characterizing in-ground rock types from measurement-while-drilling data in a mining environment, comprising:
drilling holes at a plurality of selected locations within a region of interest;
collecting measurements-while-drilling, the measurements comprising penetration rate, and at least one of pull down pressure and rotation pressure;
determining, with a processor, from the collected measurements a discontinuous rock type distribution map comprising an array of characteristic measures indicative of rock hardness at various drilling depths in the drill hole locations;
generating a continuous rock type distribution map from the discontinuous rock type distribution map by:
applying the discontinuous rock type distribution map to a Gaussian process with a selected covariance function to generate a Gaussian process model with optimized hyperparameters;
sampling the Gaussian process model by performing Gaussian process regression at a selected spatial resolution within the region of interest; and
applying boundary detection to rock hardness output data obtained from the sampling of the Gaussian process model to identify the distribution of at least one cluster of rock type within the region of interest, the boundary detection based on received hardness data between at least a first rock type and a second rock type; and
including the detected boundaries in the continuous rock type distribution map.

2. A method as claimed in claim 1 wherein characteristic measures indicative of rock hardness at a given location are calculated according to a corresponding penetration rate divided by the product of a pull down pressure and a rotation pressure square-root.

3. A method as claimed in claim 1 wherein the selected covariance function is a rational quadratic kernel.

4. A method as claimed in claim 1 wherein the boundary detection to identify rock type distribution includes applying at least one predetermined threshold value to the rock hardness output data obtained from the Gaussian process model.

5. A method as claimed in claim 1 wherein the operation of applying boundary detection includes performing unsupervised classification of the rock hardness output data.

6. A system for generating a continuous rock type distribution map of in-ground rock types from measurement-while-drilling data in a mining environment, comprising:
a drill equipped with at least one sensor for generating measurement-while drilling-data, the measurements comprising penetration rate, and at least one of pull down pressure and rotation pressure;
a processor for determining, from the measurements, a discontinuous rock type distribution map comprising an array of characteristic measures indicative of the hardness of the rock being drilled, and a spatial position sensor for generating spatial position information corresponding to the array of characteristic measures within a region of interest;
a data storage for storing the discontinuous rock type distribution map, the array of characteristic measures and corresponding spatial position information;
a training processor adapted to apply the discontinuous rock type distribution map to a Gaussian process with a selected covariance function to generate and store a Gaussian process model with optimized hyperparameters;
an evaluation processor for sampling the Gaussian process model by performing Gaussian process regression at a selected spatial resolution within the region of interest; and
a boundary processor for discerning and including detected boundaries in the continuous rock type distribution map of at least one rock type in the region of interest by applying boundary detection to rock hardness output data obtained from the sampling of the Gaussian process model to identify the distribution of at least one cluster of rock type within the region of interest, the boundary detection based on received hardness data between at least a first rock type and a second rock type.

7. A system as claimed in claim 6 wherein the array of characteristic measures indicative of rock hardness is calculated from the measurement outputs of a plurality of drill sensors.

8. A system as claimed in claim 7 wherein the plurality of drill sensors include sensors for measurements of drill penetration rate, pull down pressure and rotation pressure.

9. A system as claimed in claim 8 wherein the characteristic measures indicative of rock hardness are calculated according to the corresponding measured penetration rate divided by the product of pull down pressure and rotation pressure square-root.

10. A system as claimed in claim 6 wherein the selected covariance function is a rational quadratic kernel.

11. A system as claimed in claim 6 wherein the boundary detection processor operates to identify rock type distribution by applying at least one predetermined threshold value to the rock hardness output data obtained from the Gaussian process model.

12. A system as claimed in claim 6 wherein the boundary detection processor performs unsupervised classification of the Gaussian process output sample data.

13. A method for generating a continuous rock type distribution map of in-ground rock types in a mining environment, comprising:
recording measurements from a plurality of drill sensors whilst drilling a plurality of holes through rock within a region of interest, the measurements comprising penetration rate, and at least one of pull down pressure and rotation pressure;
using the recorded sensor measurements to generate, with a processor, a discontinuous rock type distribution map comprising an array of characteristic measures indicative of rock hardness at various drilling depths in the plurality of drill hole locations;
applying the discontinuous rock type distribution map to a Gaussian process with a selected covariance function to generate a Gaussian process model with optimized hyperparameters;
sampling the Gaussian process model by performing Gaussian process regression at a selected spatial resolution within the region of interest; and
discerning the continuous rock type distribution map of in-ground rock types within the region of interest by applying boundary detection to the rock hardness sample data output from the Gaussian process model to identify the distribution of at least one cluster of rock type within the region of interest, the boundary detection based on received hardness data between at least a first rock type and a second rock type; and
including the detected boundaries in the continuous rock type distribution map.

14. A method as claimed in claim 13 wherein the characteristic measures indicative of rock hardness are generated according to the corresponding drill penetration rate divided by the product of pull down pressure and rotation pressure square-root.

15. A method as claimed in claim 13 wherein the selected covariance function is a rational quadratic kernel.

16. A method as claimed in claim 13 wherein the boundary detection to identify rock type distribution includes applying at least one predetermined threshold value to the rock hardness sample data output obtained from the Gaussian process model.

17. A method as claimed in claim 13 wherein the operation of applying boundary detection includes performing unsupervised classification of the rock hardness output data.

18. A method for generating a continuous rock type distribution map characterizing in-ground rock types from measurement-while-drilling (MWD) data in a region of interest, compri

Assignees

• Zhou Hang
• Hatherly Peter James
• Univ Sydney

Inventors

• Zhou Hang
• Hatherly Peter James

Classifications

• G06T17/05

  Geographic models · CPC title

• E21C39/00 Primary

  Devices for testing in situ the hardness or other properties of minerals, e.g. for giving information as to the selection of suitable mining tools · CPC title