Tailings and Mine Waste TMW2018 Short Course: Tailings 2.0

Sep 4th, 2018

During the one day Tailings and Mine Waste TMW2018 Short Course: Tailings 2.0 we will discuss a synergistic approach for Tailings dams risk assessment and long term monitoring. Tailings 2.0 encompasses an updatable, scalable, drillable and convergent Quantitative Risk Assessment (QRA) platform (see ORE2-Tailings ) and Space Observation monitoring as main or complementary to extant classic monitoring programs.

ORE2-Tailings

ORE2-Tailings uses multiple data sources to deliver initial estimates regarding probability of occurrence of dams’ failures, benchmarks them with the world-wide portfolio and the latest major failures opening the way to preliminary alert thresholds bearing on dams’ portfolios. They also provide results that assist in the setup of emergency procedures for the portfolio.

Thanks to Space Observation technologies, it is then possible to confirm and gradually calibrate extant data, as well as validate old reports and their assumptions.

The core of the analyses is the Optimum Risk Estimates (ORE, ©Oboni Riskope Associates Inc.) developed and tested on mining operations and restoration projects all over the world.

A few important characteristics of a modern risk assessment deployment

In order to avoid any misunderstanding we will start by defining a few characteristics a modern risk assessment deployment should have:

Updatable, as soon as implementation or design selections are made risks can be updated quickly and affordably.
Scalable, whether your project is at pre-feasibility or at reclamation the same data base and model is progressively scaled-up.
Drillable, you get exactly the data you are looking for — quantified and prioritized.
Convergent, you get all the risks relative one to another. No more silo with H&S risks separated from Community risks or Strategic risks, etc.

The cause of dams failures

Dam failures generally are the result of a series of “original sins” and “normalized” deviances. Recent forensic studies conclusions (Mount Polley, Samarco, Oroville) made this very clear. Thus Tailings 2.0 examines thirty diagnostic nodes (from inception to the date of analysis) to deliver the estimated annual probability of failure pf of dams.

Experience has shown that many failures are actually brewing since project’s inception. Thus it is paramount to include investigations, testing, etc. in the diagnostic of a dam. This wide spectrum approach allows to deliver a balanced view of the expected performances. Furthermore it allows Tailings 2.0 to benchmark each dam against the world-wide portfolio and well known failures.

Furthermore, as inter-dependencies can cause amplification effects within a dam portfolio, Tailings 2.0 offers solutions to this problem. It also delivers a meaningful vision by evaluating the probability of dominoes effects and their amplified consequences.