Rock Quality Designation (RQD) Weighted Joint Density (wJd) method

This document details Datarock’s product RQD - Weighted Joint Density method.

Background

It is commonly known that datasets logged from drill core are influenced by the direction of the drilling with respect to the features of interest.

For example, if drilling is conducted parallel to the common direction of jointing, an RQD measurement will tend to be higher than if it is perpendicular to the direction of jointing.

Datarock has developed it’s Weighted Joint Density product (see wJd product description for details) to assist with this, and is furthering this work by developing this RQD Weighted Joint Density (RQDwJd) method.

This example shown below by Palmström et al shows an example of the increased consistency of RQDwJd over the traditional RQD in the situation where drilling direction is changed.

rqdw-1

Comparison of RQD,
WJD, and RQDWJD
measurements in the same
domain (Palmstrom 2005)

These comparisons show that RQDwJd provides a different perspective on rock quality, which could be more representative of the actual conditions, especially in complex geological settings where joint orientation plays a significant role.

Literature

This product is based on the following literature: 

Title

Author 

Year 

The weighted joint density method leads to improved characterization of jointing

A Palmström et al

1996

Measurements of and Correlations between Block Size and Rock Quality Designation (RQD)

A Palmström et al

2005

Practical Investigations on Use of Weighted Joint Density to Decrease the Limitations of RQD measurements

M. Haftani, & A. Mehinrad

2015

 

Dependent Models

The outputs of the following models are used:

Model Name

Model Type

Fracture Detection and Classification

Object Detection

Drillers Break

Object Detection

Fracture Mask

Instance Segmentation

 

Data Processing

To compute the Rock Quality Designation based on Weighted Joint Density (RQDwJd), as detailed in "Practical Investigations on Use of Weighted Joint Density to Decrease the Limitations of RQD Measurement" by M. Haftani & A. Mehinrad (2015), the following procedure is outlined:

Measure the Weighted Joint Density

Take the results of Datarock’s Weighted Joint Density (see product description for details).

A summary of the steps are here:

  • Take the output of the fracture detection model, “simple” classes only

  • Run a segmentation model to extract the fracture profile

rqdw-2

  • Measure the angle of each fracture with respect to the core axis

rqdw-3

  • For a given interval, calculate the Weighted Joint Density

rqdw-4

where

L = length of the measured section along the core

δ = the intersection angle, the angle between the drill core and the individual joint

This formula accounts for the frequency and orientation of joints within the rock mass. The summation () runs over all the joints you have in your core section.

rqdw-5

Calculate RQD_wJd

Use the formula:

rqdw-6

This formula calculates the Rock Quality Designation (RQD) value derived from the Weighted Joint Density (wJd), offering a numerical assessment of rock quality. Generally, higher RQDwJd values signify superior rock quality.

rqdw-7

Product Configuration Options

There are no configuration aspects to this product.

Output Data

Default interval length: 1.0m

Customisable interval available: Yes

User Data

User data may be provided to the Datarock team via csv in the following format:

·       HoleID_sampling_intervals_rqd_wjd.csv

CSV file to contain the following headers:

File Header 

Description

depth_from

Start of interval

depth_to

End of interval

 
Data Output

Results from this product is delivered in a batch nature.

Integration of the required technologies into Datarock production is ongoing.

The available CSV files include the following:

  • ProjectID_HoleID_rqd_wjd_1m.csv

  • ProjectID_HoleID_rqd_wjd_by_user_intervals.csv

Both CSVs contain the following headers:

File Header 

Description

hole_id

Customer’s Hole ID

depth_from_m

Start of interval (metres)

depth_to_m

End of interval (metres)

depth_from_ft*

Start of interval (feet)

depth_to_ft*

End of interval (feet)

wjd

Weighted joint density

wjd_measurable

Weighted joint density using only “measurable” fractures

 

rqd_wjd

RQD based on weighted joint density

rqd_wjd_measurable

RQD based on weighted joint density using only “measurable” fractures

 

*Only included if project depths are in feet.

Product Limitations

Limitations

Comments 

Interpreting Fracture Complexity

Machine learning models may have difficulty accurately interpreting the complexity of fractures in highly fractured zones, particularly fractures that a parallel to the core axis. Missing detections will impact the results.

Difficulty in Small Block Identification

The presence of numerous intersecting joints that create very small rock blocks may not be easily discernible in images, potentially leading to inaccuracies in the machine learning model's interpretation.

 
Document Version

Version

Date 

Author

Rationale 

1.0

23 Jan 2024

L Yanez

Initial release