Geological Risk Assessment Essentials (GRA)

Lexicon of terms:

Conditional Simulation: geostatistical methods of modeling mineral deposits that attempt to reproduce the range and spatial variability of grades in sample data. Instead of producing a single average case model, conditional simulation generates a number of equally likely models.
Simulated grades: grades in anyone of the models generated by conditional simulation.
Reference grades: point grade estimates used for building standard economic models, pit optimization and scheduling. Usually, reference grades are the averages of simulated grades or are obtained by other geostatistical methods like kriging.
Geological risk: the potential effects of statistical errors in reference grades estimation on life of the mine plans based on the reference grades. The two main concerns are:

1. The life of the mine plan may significantly overestimate the profits and NPV; therefore, the true return on investment may turn out lower than expected.
2. The mining strategies devised the plan may not be optimal.

Value models: block models with a single field representing block net value for a particular conditional simulation model and given economic settings.
Optimal Extraction Sequence (OES): thje solved sequence based on planning parameters.
Risk Rated k-Pits: see the description below.

Geological Risk Assessment (GRA)

To assess the geological risks of a life of the mine plan we need to relate conditional simulation models to economic and technological parameters; build the corresponding value models of the deposit and use these models to infer the probability distributions of vital planning statistics like profits and NPV. GRA allows you to do all that and in addition offers a risk based method of selecting the ultimate pit limits.

GRA Tools

Import simulation data into your application.
Build equally likely value models by applying economic and recovery parameters to the grade models.
Evaluate probability distributions of profits and NPV of any OES.
Generate Risk Rated k-Pits (see below)

OES and Risk

In your applicaton, various stages of mine planning are represented by optimal extraction sequences. The sequences are optimal with respect to the reference grades however the value models can be used to evaluate profits and NPV for the simulated grades. Suppose we have 50 value models built from 50 conditional simulation models. We can calculate 50 equally likely profit and NPV values representing their respective probability distributions and the standard distribution parameters like median, mean, standard deviation, average deviation etc.

The probability distribution parameters, in particular standard or average deviations give a measure of risk. Large deviations relative to the mean indicate that the true profit (NPV) is likely to differ significantly from the predicted (mean) profit (NPV) and therefore signal that the project may be risky.

Risk Rated k-Pits

Suppose we have N value models built from N conditional simulation models. For each value model we generate an LG ultimate pit giving us N pit shells each of which is equally likely to be the “true” profit maximizing optimal final pit shell. There may be blocks in the model that are included in all N shells; others may be included in N-1 shells, N-2 shells, and so on, finally some blocks will not be included in any shell at all.

Consider the pit shell (k-Pit) that consists of all blocks included in at least k LG ultimate pits, where k is an integer between 1 and N. It can be shown that any k-Pit adheres to the same slope rules as the LG ultimate pits so it can be used as the mine’s final pit shell. Like LG pits parameterized by product prices, k-Pits are nested, that is, the k-Pit is wholly contained in the (k-1)-Pit. The difference is that the k-Pits are ranked by geological risk whereas LG phases are ranked by economic risk stemming from price or costs uncertainties.

The risk rating of a k-Pit can be quantified as s = (100*k/n); the N-pit is rated as 100% safe, carries no risks, and the rating diminishes (the risk grows) for smaller values of k.

Like any other pit, a k-Pit can be evaluated for costs, revenues, NPV, tonnages and grades. Geological risk ratings in conjunction with these standard statistics may help you to choose final pit shell that strikes the right balance between risks and opportunities associated with any mining venture.

	Related Topics
	GRA Quick Start Capital Costs Import Essentials Economic Model Pit Optimization Pushback Essentials Scheduler Essentials MAO Essentials MFO Essentials

JMN 20065_01_EN