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Solutions for mine water management

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Page 1: Solutions for mine water management - dhigroup.com content/presences/emea... · Bin 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 210 220 230 240 250

Solutions for mine water management

Page 2: Solutions for mine water management - dhigroup.com content/presences/emea... · Bin 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 210 220 230 240 250

Solutions for mine water management

Sound mine water planning, information and forecasting minimizes disruption & yields increased production.

DHI applies its knowledge of water using, physics based process models, and monitoring expertise to find cost effective solutions.

Mine Dewatering

Mine Flooding

Stream Diversions

Water Surplus and Scarcity

Compliance Monitoring

Water Balance Forecasting

Acid Mine Drainage

Climate Change Impacts

Wetland & Stream Restoration

Mine Closure

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Mine Dewatering

#3

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Geological model using LEAPFROG Parameters

zone and Faults Representation

Open PIT

Tailing Pond Matterhorn

Hydrogeological model FEFLOW:

Groundwater flow modeling, transport and

density dependent flow, geochemistry,

Mine Hydrology using MIKE SHE :

Spatial and Dynamic water balance, distributed

Recharge , ground and surface water interactions

, stream flow, solutes an geochemistry

Pore Pressures

TSF Seepage

Water Balance

Dewatering

Mine Closure – Pit Rebound

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FEFLOW - Full unstructured mesh including pinch out layers

20 April, 2016 © DHI #5

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Mine Inflow Forecasts & Uncertainty

© DHI

• Parameter range instead single value • Multiple samples from parameter range • Explore range of predictions • Reliable estimate of average expected inflow

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e W

ate

r In

flo

w [

l/s]

Life of Mine [days]

Mine Water Inflow Forecast

base case sampled from range Poly. (sampled from range)

Red: base case prediction, green: 90% likeliness range of prediction

0%10%20%30%40%50%60%70%80%90%100%

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mean dewatering requirement [l/s]

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Data Worth Analysis

© DHI

• Linear sensitivities represent the response of predictions to small parameter variations

High-Resolution 3D linear sensitivity study with 10.000 pilot points: Highest Sensitivity found in areas close to desaturation of top aquifer

High Kxx - sensitivity

Low Kxx - sensitivity

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Data Worth Analysis

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Integrating Water in Mine Planning &

Operations

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Conceptualisation of MIKE IPO Integration

© DHI

Scada Historian (Assets)

Groundwater, surface water, rainfall database(s)

Borehole database

MIKE IPO

Data analysis

Measurements; head, discharge, TDS, pit level

Spatial; bore locations, pit floor & water surfaces, etc

Mine Development model

Configuration tool Import Script

Groundwater & Surface Water Models

Hydraulic/ water infrastructure Model

Operational plan; • Mine plans (5yr, 2yr, 3mth) • planned vs actual water levels at

key locations and spatially • Planned vs. actual abstraction &

quality • Planned vs actual network

pressures, flows, quality, etc • water use

Model calibration; • planned vs obs heads • water balance at sub domain level

Models (incl. inputs & outputs)

Operational data

Reconciliation

Expert user analysis

Compliance; • plan, actual and trigger water

levels at compliance monitoring locations

Asset performance; • pump availability, utilisation and

productivity • Pump health (referencing pump

curve)

Other

Real time forecast; flood levels

Custom views

Pump curves

Other

External Tools

Water Level & pit floor difference calculations

Interpreted water surfaces

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4/20/2016 11

Mine Water Reporting

Enhancing communication of business reporting via web enabled dashboards of forecasts and operations data

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© DHI

$ Optimum ? $

Traditional single model realization approach. Calibrated model only provides base investment cost, no risk level

Risk Based Decision Making Framework Using Uncertainty Analysis, data worth analysis

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© DHI

Multiple model realization approach. Suitable uncertainty analysis method (here: Monte-Carlo sampling) provides understanding of risk associated with different decisions

Risk Based Decision Making Framework Using Uncertainty Analysis, data worth analysis

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Uncertainty analysis, data worth and risk based planning: • Numerical uncertainty analysis • provide data worth input to optimizing study programs; and • informing risk based mine planning

Uncertainty analysis & Risk based planning:

95% CI ($$$$)

50% CI ($$)

Flow

Time

95% CI ($$$$)

50% CI ($$)

Con

sequ

ence

Likelihood

Uncertainty & Data worth Analysis Integrated planning risk assessment (production impact)

95% CI ($$$)

Risk Based Decision Making Framework Using Uncertainty Analysis, data worth analysis

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Tailings Dam Seepage

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TSF and Pit Inflow Seepage Analysis

4/20/2016 16

• Groundwater – Surface Interaction Modelling • Flow paths and travel times • Quantitative assessment • Evaluation of mitigation measure

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17

Open Pit

River Tailing Pond

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Mine Planning For Floods

Ensham Mine Queensland - Australia

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Mine planning for floods

Existing flooding With flood protection

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Mine planning for floods

Minor overtopping of levee Major overtopping of levee

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Mine Site Drainage

© DHI

Assess Contact and Non Contact Water 2m Resolution

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Forecasting for mine management SCADA system for real-time rainfall and flows

Meteorological forecasts

Regional radar and satellite images

Local rainfall radar

Dashboard

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Proactive flood management helps you:

• Minimize downtime due to flooding or potential flooding

• Improve safety and minimize damage during flooding

• Reduce the risk of catastrophic mine closure

The Ensham Mine cost of downtime was over $1 million per day

The haul trucks are valued up to $3 million The drag-line recovery cost ~$100 million

Including lost production, the damage for the Ensham Mine flood disaster was in excess of $350 million

Ensham Mine Queensland - Australia

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Sediments & Tailings

#24

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Tailings & Sediments

20 April, 2016 © DHI #25

• Wet Tailings • Dynamic morphological simulation. • Beach Slope Development • Prediction of distribution and grades. • Spigot Optimzation

25 25

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Tailings & Sediments

20 April, 2016 © DHI #26

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Tailings Dam Breach Impact Assessment

20 April, 2016 © DHI #27

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Mine Hydrology Reliable Water Balance Modelling

#28

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Channel flow

Overland surface

flow and flooding

Saturated

groundwater flow

Unsaturated

groundwater flow

Precipitation and

snowmelt

Vegetation based

evapotranspiration and

infiltration

Demand driven

water use

Integrated water

quality

One Water — One System — One Model MIKESHE - Integrated Mine Hydrology

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Groundwater discharge areas

Recharge is; • Spatially distributed

• Temporally distributed (dynamic),

• It cannot reliably be predefined

• Not a simple boundary condition.

Surface Water Hydrology & Groundwater Rechage

What does Recharge really look like?

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Mine Catchment and principle mine affected areas

Recently rehabilitated mine area

Old, rehabilitated mining area

On going mining, with concurrent rehabilitation

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Topography

Spatial water balance

Surface water

Vegetation

Soils

High groundwater recharge

Groundwater discharge to surface water

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Dynamic water balance

Response to stream flow peaks

Discharge delayed relative to rainfall

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Mine Closure and Reclamation

#34

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Mine Closure

Understanding mine hydrology is the key to mine closure

• Recharge sources

• Pit/Groundwater rebound

• Decant locations/volumes

• Mitigation measures

• Treatment design

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Mine Closure

Understanding mine hydrology is the key to mine closure

• Recharge sources

• Pit/Groundwater rebound

• Decant locations/volumes

• Mitigation measures

• Treatment design

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Case Study – Middleburg, South Africa

Mine Closure Plan – Water Treatment Plant ARD treatment volumes were reduce by 50% compared to the estimates from simple models • 50% lower expected decant volumes • 30% lower capital costs for treatment plant • 50% lower operational costs for 50+ years

Regulatory approvals had completely stalled • Loss of confidence in Lumped Box Models (GOLDSIM) • Physics based catchment modelling re-energized stakeholder

engagement • Reliability improved.

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Phytoremediation

© DHI

Conceptual model

Hydrologic model processes

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Land cover changes to reduce infiltration • Clay cap too expensive

• No easy source of clay • Surface areas too large

• Reduce infiltration by increasing

ET in the cover?

Current cover: grass Annually harvested for animal feed Thin, shallow rooted Deeper rooted, denser vegetation?

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Construction changes to reduce infiltration • Backfill compaction during emplacement?

• Lower conductivity, lower porosity

• Slope modifications? • Enhanced runoff

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Pit Lakes and Groundwater Rebound

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42

Pit Rebound

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Remote Sensing in Mining

#43

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Screening for surface minerals

20 April, 2016 © DHI #44

Airborne hyperspectral mapping: • detailed mapping of surface

materials • Vegetation proxy mapping for

surface minerals.

Copper Mineralisation

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Water quality mapping

20 April, 2016 © DHI #45

• Suspended Sediments • Biological

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Planning – topographic mapping

20 April, 2016 © DHI #46

Comparison of aerial LIDAR derived DEM with satellite derived.

Topographic mapping in remote locations. Accuracy of +/- 1 meter.

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Solutions for mine water management Physics based solutions

• Reliable forecasts.

• More efficient planning

• Reduced downtime

• Higher regulatory confidence

• Improved compliance

Cost effective solutions Mine Dewatering

Mine Flooding

Stream Diversions

Water Surplus and Scarcity

Compliance Monitoring

Water Balance Forecasting

Acid Mine Drainage

Climate Change Impacts

Wetland & Stream Restoration

Mine Closure

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Thank you

20 April, 2016 © DHI #48

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Additional information

20 April, 2016 © DHI #49

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Integrating Water in Mine Planning &

Operations

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DHI Solutions for planning optimization and risk management • Uncertainty analysis, data worth and risk based

planning • Regional integrated watershed modelling • Mine site water balance • Dewatering and water supply system design • Fluvial morphology • Surface water and groundwater impact assessment • Dam break analysis • etc DHI Solutions for planning & operations & reconciliation • MIKE IPO • real time reconciliation of planning and operational

data enabling targeted and timely response to emerging risks and opportunities

• Improving efficiency and QA for data integration in multi-function planning process

Framework for Integrating Water in Mine Planning and Operations

Integrated mine planning, capital program and operations model

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Framework for Reconciliation of Water Budgets

Mine Plan

Operations

Dewatering plan

Mine Pit Elevation Reconciliation: Mine Plan vs Survey

Dewatering Forecast Reconciliation: forecast vs hind cast modelling

Operations Reconciliation: • Abstraction volumes • Pump mechanical availability • Pump utilization • Pump productivity (well column, rates)

Outcome Plan

• Data sources integrated into single platform • Enabling efficient and quality assured reconciliation of planning and operations data • Identify causal factors of impending interactions between mine advancement and watertable • Focused early response.

target

actual

Planned pit elevation

Surveyed pit elevation forecast

Hindcast

Mine plan Water level Mine plan

Water level

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Models and Scenarios

Scripts

Spreadsheets

© DHI

MIKE IPO: Component UI (Scientific View/Configurator View)

Time series

Data base

Maps (GIS)

Open architecture (API)

Add User tools

Platform for Mine Water Management - MIKE IPO

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Conceptualisation of MIKE IPO Integration

© DHI

Scada Historian (Assets)

Groundwater, surface water, rainfall database(s)

Borehole database

MIKE IPO

Data analysis

Measurements; head, discharge, TDS, pit level

Spatial; bore locations, pit floor & water surfaces, etc

Mine Development model

Configuration tool Import Script

Groundwater & Surface Water Models

Hydraulic/ water infrastructure Model

Operational plan; • Mine plans (5yr, 2yr, 3mth) • planned vs actual water levels at

key locations and spatially • Planned vs. actual abstraction &

quality • Planned vs actual network

pressures, flows, quality, etc • water use

Model calibration; • planned vs obs heads • water balance at sub domain level

Models (incl. inputs & outputs)

Operational data

Reconciliation

Expert user analysis

Compliance; • plan, actual and trigger water

levels at compliance monitoring locations

Asset performance; • pump availability, utilisation and

productivity • Pump health (referencing pump

curve)

Other

Real time forecast; flood levels

Custom views

Pump curves

Other

External Tools

Water Level & pit floor difference calculations

Interpreted water surfaces

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Example workflow Import Operational Data

• Active connection to data sources such as Envirosys, Aquire, SCADA Historian, pit survey

• Import interpreted datasets (ie. WL contours)

• Data stored in related Time series, worksheets and spatial formats

Import Mine Plan File

• Import ans store mine plan files (csv.)

• Create time series spatial features (rasters)

• Create timeseries for selected points

Compare current WL with mine plan • Compare spatial data sets • Compare timeseries data

Define dewatering asset array • Examine asset capacity; availability, productivity

• Define additional assets

Generate Forecast WL Publish plan Reconcile plans and asset performance

• Automatic connection or embedded dewatering simulator

• Run dewatering simulator and auto post process results

• Compare spatial and timeseries outputs with mine plan data

• Publish plans as forecasts and targets via dashboards

• Link operational data to dashboards for live updates

• Visual reconciliation of forecasts with operational data (ie. abstraction rates, WL’s, asset performance).

• Automate reconciliation of forecasting tool with operational data

• Reconcile mine plan with survey elevation

Decision making

• Make rapid and informed decisions to align mine planning, dewatering planning and operations

• Informed status for subsequent planning cycle

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4/20/2016 56

Mine Water Reconciliation

Enhancing planning, operations and reconciliation through integration of datasets and data forms (time series, spreadsheet and spatial)

Forecats WL

Forecats mine pit elevation

Measured WL WL

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4/20/2016 57

Mine Water Reporting

Enhancing communication of business reporting via web enabled dashboards of forecasts and operations data

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© DHI

$ Optimum ? $

Traditional single model realization approach. Calibrated model only provides base investment cost, no risk level

Risk Based Decision Making Framework Using Uncertainty Analysis, data worth analysis

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© DHI

Multiple model realization approach. Suitable uncertainty analysis method (here: Monte-Carlo sampling) provides understanding of risk associated with different decisions

Risk Based Decision Making Framework Using Uncertainty Analysis, data worth analysis

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Uncertainty analysis, data worth and risk based planning: • Numerical uncertainty analysis • provide data worth input to optimizing study programs; and • informing risk based mine planning

Uncertainty analysis & Risk based planning:

95% CI ($$$$)

50% CI ($$)

Flow

Time

95% CI ($$$$)

50% CI ($$)

Con

sequ

ence

Likelihood

Uncertainty & Data worth Analysis Integrated planning risk assessment (production impact)

95% CI ($$$)

Risk Based Decision Making Framework Using Uncertainty Analysis, data worth analysis

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Mine Planning For Floods

Ensham Mine Queensland - Australia

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Forecasting for mine management

Operate flood structures

Publish Flood Maps

forecast

Time of forecast

hindcast

Forecast flows and levels

48 hr

48 hr

Provide Flood Warnings

Dashboard