ADVANCES IN DRILL RIG DEPLOYED RADARS

SAGA, September 2009 1

ADVANCES IN DRILL RIG DEPLOYED RADARS

Mr Tim Sindle, ARCO/CRC Mining Imaging Lab, The University of SydneyDr Carina Kemp, Business Development Manager, GEOMOLE

11th SAGA Biennial Conference and Exhibition, 16-18 September 2009

SAGA, September 2009

2

Outline Introduction Method and Results

Survey Gear Minimisation Analyzing Drill Deployed Data Automatic Algorithm Development

Conclusions

3


Introduction – In-mine geophysics

Anticipate problems ahead of mining

Improve efficiency of mining operations

Bulky gear Time consuming

surveys cause delays in production

The Good The Bad

No matter how good the results, if any technique cannot be easily and reliably implemented in the mining environment, it will not be used mainstream.

4


Introduction – Borehole Radar (BHR)? Ground penetrating radar

(GPR) in a drillhole Reflections indicate a

contrast in the electrical properties of the rock.

BHR provides high detailed continuous reflections from lithology contacts and structures.

GeoMole BHR 10 – 124 MHz Bandwidth Resolution: less than1m Range: up to 50m or more

(depending on rock type)

Probe diameter: 32 mm BHR Profiling at ~10 m/min

Omnidirectional antenna

5


BHR then…. Survey trials of

BHR showed very promising results, but the gear let us down. 50 kg optical fibre

winch 20 kg push rods 10 kg probes

6


BHR Now - Minimal Gear Radar Tool

1.6m 3kg

Non-conductive spacers 1.5m 2kg each

Drill attachment PDA

Radar SpacersDrill

Attachment

+IQ

+PDA


7

BHR Now – Drill rig deployed

IQCore barrel

drill bit

spacers

Drill Rig Deployed Borehole Radar- Pumpdown Radar Tool

Spacers

The radar tool continuously records data.

The motion of the rods is discontinuous as the rods pulled and removed.

Depth Depth

Mea

sure

men

t (s

tati

on)

Mea

sure

men

t (S

tati

onar

y)Winch Survey OTR Survey

Stationary

Moving

Moving

Deployment Motion…

Winch Survey OTR Survey

Same 40m section of a horizontal borehole

Radar Data…

StationaryMoving

11


Raw Data Aim:

To understand the motion in order to work out how to recompress it.

Different motion for each type of drill-rig

Boart LM75 Diamond

Raw Data

Recompressed Data

Recompressing Radar Data..

13


Movement Log Logging procedure

tracks accurately the motion of the drill rig.

User records ‘MOVE’, ‘STOP’ and ‘ROD-CHANGE’ following the motion of the drill.

These events are time stamped and recorded for data processing

S SS MM MMSM

Rada

r Dat

a

Trace Number

Acce

lero

met

er D

ataR R

Ampl

itude

Accelerometers were installed in the radars to assist with movement logging

14


Time Log processed Data Vulnerable

to human error


15

Automatic Algorithm DevelopmentUsing the accelerometer data for

automatic processing: Statistical deviation measurement Fourier Spectrum Analysis Velocity integration calculations


16

Statistical Processed Data

700 705 710 715 720 725 730 735 740 745-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2Am

plitu

de

Traces

Standard DeviationThreshold

17


Accelerometer Processed Data

Suffers from random accelerometer events

18


Fourier Spectrum Analysis Examine the power in

various regions of motion

Difference observed between some moving and stopped traces by examining the higher frequency content.

However, drill vibrations cause wide band energy gains.

METHOD ABANDONED 0 5 10 15 20 25 30 35 400

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04Single-Sided Amplitude Spectrum of y(t)

Frequency (Hz)

Am

plitu

de

*Stopped with drill shockStart of moveStopped Constant velocity move

Frequency Spectrum

19


Velocity Processed Data Noisy environment causes

spurious accelerations and accurate velocity is hard to gather.

A high pass filter distributes the velocities aroundzero.

Then the mean representation of the velocity is calculated

700 705 710 715 720 725 730 735 740 745 750

-1

-0.5

0

0.5

1

Trace

Ampl

itude

velocityonaccelerati

Positive = Moving, Negative = Stopped

20


Velocity Processed Data Copes well with

the sharp drill shocks and vibrations as they often have equal positive and negative direction.

Captures the start and stop of the movement well.

Particularly violent jerks can cause a trace to be lost.


21

Comparison…

Raw DataTime LogAccelerometerVelocity

22


Conclusions… Drill deployed radars

can be run with minimal disruption to normal work flow.

Using the time log alone can be vulnerable to human error

Yet all automated methods investigated so far are vulnerable to sharp spurious drill movements.

A combination of a time log together with statistical and velocity methods will result in smooth “winch quality” images being produced.

Development in this

area continues


23

Conclusions The ultimate aim of a tool knowing its own

position automatically is theoretically possible, but only within well defined constraints, and there will always be the unknown events on the drill rig that can cause inaccuracies.

The above progress makes it possible for quick data turnaround from survey to seamless integration of BHR data into mine planning packages, to enable day to day mining decisions to be made using such tools.


24

Acknowledgements… The authors would like to thank DeBeers

Canada in particular Kevin Smith, for their ongoing feedback and use of the tool.

The funding contributions of ARCO, CRC Mining, and GeoMole are gratefully acknowledged.

Many thanks to the tireless work by Sydney University ARCO Lab members including; Andrew Bray, Steven Owens, and Phillip Manning.

Documents

ADVANCES IN DRILL RIG DEPLOYED RADARS