Sand & Gravel Case Study

Jim Reed
RockWare Incorporated
1/12/06

Creating A Borehole Database

A series of exploration boreholes were drilled.  Samples were taken every five feet and sieved in order to determine the relative percentages of sand, gravel and clay (or other non-sand/gravel material).  These samples were restricted to the interval below the base of the soil profile and the top of the bedrock.  The borehole locations, stratigraphy (see Table 1), and sieve analyses (see Table 2) were then entered into a relational database.

Table #1.  Information that was recorded for each borehole.

Name Unique borehole identifier (e.g. BH-01, BH-02, etc.)
Easting UTM easting from GPS (in feet)
Northing UTM northing from GPS (in feet)
Elevation Elevation from GPS (in feet)
Soil Depth Depth to base of soil (in feet).
Bedrock Depth Depth to top of bedrock (in feet).
Total Depth Total depth of borehole (in feet).


 

Table #2.  Information that was recorded for each sample interval.

Depth-1 Depth to top of sampled interval (feet).
Depth-2 Depth to base of sampled interval (feet).
Sand % Sand (0 to 100)
Gravel % Gravel (0 to 100)
Clay % Clay or other non-sand/gravel material(0 - 100)

Displaying The Boreholes

Two and three-dimensional striplogs were constructed for each borehole. 

 

 

Figure 1
Two-dimensional
striplog.

 

Figure 2
Three-dimensional
composite striplog

 

Figure 3
Three-dimensional
percentage log.

 

Figure 4
Three-dimensional depiction of all boreholes.

Generating The Initial Sand, Gravel & Clay Models

Solid “block” models for the sand, gravel, and clay data were created by using a modeling algorithm that estimates grade levels for a three dimensional matrix of imaginary blocks.

Figure 5
Sand Percentage Logs
Figure 6
Sand Model
Figure 7
Sand > 40%
     
Figure 8
Gravel Percentage Logs
Figure 9
Gravel Model
Figure 10
Gravel > 40%
     
Figure 11
Clay Percentage Logs
Figure 12
Clay Model
Figure 13
Clay < 20%

Computing Sand & Gravel Reserves

The sand and gravel models were combined by adding each of the block values (Figure 14).  This combined model was then filtered to show only those regions where the sand and/or gravel are greater than 80 percent (Figure 15).  Finally, a pit was generated (Figures 16, 17, & 18) by using a "floating cone" algorithm that removes material above the ore based on user-defined criteria (e.g. maximum slope, bench height, ore grade, etc.)

 
Figure 14
Sand + Gravel Model
  Figure 15
Sand+Gravel > 80%
     
 
Figure 16
Optimum Pit Design
Max Slope = 45 deg.
No Benches
  Figure 17
Optimum Pit Design
Max Slope = 45 deg.

Bench Height = 10 Feet
 
Figure 18
Optimum Pit Design
Max Slope = 45 deg.
Bench Height = 20 Feet
   

Appendix I

Classification Scheme
Minimum
Size (mm)
Maximum
Size (mm)
Classification
20 200 Pebbles
2 20 Gravel
0.06 2 Sand
0.002 0.06 Silt
0 0.002 Clay