Short term mine sequencing using geological uncertainty

7/23/2019 Short term mine sequencing using geological uncertainty

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Short term mine sequencing

using geological uncertainty

indexes - An application to the

Carajás Iron Ore Complex

Prof. Rodrigo de Lemos Peroni

Mining Engineer José Edson Aragão da Silva



IntroductionGoal

Objectives

Methodology Case Study

Results

Conclusions

Presentation Outline



Motivation

Different quality from planning, mainly in the weekly mineplanning.

Possible causes:

• Some mines are getting close to exhaustion, ore close to the

contact zones and/or, intrinsic variability of the deposits.;

Posing the problem

1 – Predicted quality detachment in the monthly plan when

compared to the executed grades.

2 – High variability in the weekly plans when compared to the

monthly plan.3 – Absence of a good forecast beyond a week.

4 – Lack of knowledge of geological uncertainties impacting the mine

planning.

Introduction

Introduction

Goal

Objectives

Methodology

Results

Conclusions



Goal:

Develop a short-term sequencingmethodology to improve the predictability of

quality in the weekly period and to assess the

geological uncertainty of the planned blocks;

Introduction

Goal

Objectives

Methodology

Results

Conclusions

Introduction



Objectives:

Implement a methodology of weekly sequencing;

Model the Fe grade uncertainties in the N5 mine;

Analyze the geological uncertainty during the mining

periods, aiming to know the risks associated to the

mining areas;

Compare the average quality given by the estimated

block model with the E-Type (Kriged) calculated from

the simulations.

Introduction

Introduction

Goal

Objectives

Methodology

Results

Conclusions



Estimated block model:

Very popular in the mining industry;

• Estimate mineral resources;

• In the last decades there were verified discrepancies when

compared to production results;

•

It is “accepted” that the main reasons to a poor reconciliationare related with the uncertainty in the geological interpretation

and with the estimates.

Factors that might affect the estimate uncertainty

1 – Change of support;

2 – Lack of sampling, bad sampling or protocols;

3 – Interpolation errors;

4 – Smooth effect.

Methodology

Introduction

Goal

Objectives

Methodology

Results

Conclusions



Geostatistical simulation

• Firstly proposed in the 70’s as a method to provide

a quantitative assessment of the local uncertainty.

sequential Gaussian simulation (sGs)

• Started in the 90´s;

•“Widely” used compared to other simulationalgorithms;

Introduction

Goal

Objectives

Methodology -

Simulation

Results

Conclusions

Methodology



General view

Unknown deposit Geological dataset

Equally probable

models

Introduction

Goal

Objectives

Methodology -

Simulation

Results

Conclusions

Methodology



Simulations generated using the sGsim implemented within SGEMs:

Dataset

EDA

VariogramHematites

sGs 100 Simulations

Introduction

Goal

Objectives

Methodology -

Simulation

Results

Conclusions

Methodology



Case Study



Case Study

N5W, N5E, N5EN e N5Sul



Case Study



N5W

N5E

N5S

Case study – N5 Mine

Main aspects:

• Hematites – HF, HC e HMN;

• Product: Sinter Feed;

• Main variable – Fegl;

• Period considered: 2011 – 2013;• Post processed block model

Localização das amostras da Mina de N5

Introduction

Goal

Objectives

Methodology -

Simulation

Results

Conclusions

Case Study

Final Block Dimensions:

25m x 25m x 15m



Validation

• Histograms

Histograma dos dados

Histograma das realizações

Introduction

Goal

Objectives

Methodology -

Simulation

Results

Conclusions

Methodology

Mean

66.86%

Variance

5.99

50 realizations



Validation

• Variogram Introduction

Goal

Objectives

Methodology -

Simulation

Results

Conclusions

Methodology



Export

Simulations(.CSV)

Calculation ofUncertainty Flag on model

The probability was post processed in

Excel considering the number of times

the criteria Fegl>= 65% was attended.

Introduction

Goal

Objectives

Methodology -

Simulation

Results

Conclusions

Methodology



Export

simulations(.csv)

Calculation of

uncertainty Flag on model

Introduction

Goal

Objectives

Methodology -

Simulation

Results

Conclusions

Methodology



Input parameters for sequencing.

Introduction

Goal

Objectives

Methodology -

Sequencing

Results

Conclusions

Technical parameters for sequencing

Model Name

Plant Production (per year)

Plant Recovery

Studied Variable

Reference content (Goal)

Period

65%

Weekly

N5 (Kriging)

50Mt

Global Iron grade (Fegl)

92%

Input data



The blue shades show the areas where the probability of the FEGL content presents

values above 65% are extremely low.

Areas in intense orange and red shades are areas of low uncertainty considering theypresent more than 80% probability to have FEGL grades above the defined cutoff of 65%.

Areas in green and yellow are areas of high uncertainty and or 40 to 60 % probability and

must be better assessed.

Week1 - 46

Week

47

ahead

Introduction

Goal

Objectives

Methodology -

Simulation

Results

Conclusions

Results

Probability map for the Fe content present grades above 65%.



To carry out the sequencing in weekly plans, the simulations were fed to the Minesched®

software. The implemented algorithm allows good operational flexibility.

Introduction

Goal

Objectives

Methodology -

Sequencing

Results

Conclusions

Results



Results

Areas

shown inthe map.

Graphs presenting the quality and performed geological uncertainty ofeach period.

Note that, although almost all periods have reached the goal of quality

(Kriging model), the associated risks, after week 46, are high, and

confirmed by the low probability of achieving the target grade (65%).

Introduction

Goal

Objectives

Methodology

Results

Conclusions



The methodology allowed the predictability of

qualities in periods of mining;

The application of geostatistical simulation

technique has proven to be a valuable tool toapproach the uncertainty of the variable FEGL;

Using the methodology suggested here, it was noted

that the results may assist short-term and mediumterm planning in the preparation of weekly and

monthly plans and in decision making, when periods

of operational difficulties are identified.

Conclusions

Introduction

Goal

Objectives

Methodology

Results

Conclusions



Apply the methodology in other iron ore

mines in the Carajás complex and other

mining complexes from VALE;

Spread the methodology in the short-term

and medium term iron ore planning areas and

other commodities in the company;

Next steps

Introduction

Goal

Objectives

Methodology

Results

Conclusions



Aknowledgements

• VALE

• UFRGS/PPGEM



Thank You!

Gracias!

Obrigado!

Rodrigo Peroni

[email protected]

Short term mine sequencing using geological uncertainty

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