Using VSP for Groundwater Monitoring Program Optimization

ASP Workshop

Charleston, SC

September 16, 2015

Alex Mikszewski, PEAmec Foster Wheeler

Introduction

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Long-Term Groundwater Monitoring

Increasing percentage of sites at this stage

Can be significant annual spend >$100k

VSP 7: Identify Sampling Redundancy Module

Temporal: Iterative Thinning, Variogram Analysis

Spatial: Kriging Analysis

VSP Input

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Time-series data

>10 observations

Time gaps/outliers

Well coordinates

Discrete time period analysis for spatial optimization

Vertical layering considerations

VSP Temporal Optimization

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Iterative Thinning

Identify sampling frequency to reproduce temporal trend

Iterative process: remove points, re-evaluate trend compliance

Single-well Variogram

Identify minimum temporal spacing for event independence

Sample no more frequently than range of correlation

PNNL, 2014

VSP Iterative Thinning

5 PNNL, 2014

VSP Iterative Thinning Example

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10 wells

Semi-annual sampling frequency

VSP Iterative Thinning Example

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Approved reduction to annual monitoring

Combined with spatial optimization leads to annual savings of >$30k

Optimal Monitoring Spacing (Days)

Well Arsenic Manganese Well Screened ZoneMW-1 367 367 OverburdenMW-2 457 610 OverburdenMW-3 457 457 Overburden/BedrockMW-4 608 456 OverburdenMW-5 374 374 OverburdenMW-6 455 364 OverburdenMW-7 457 610 OverburdenMW-8 366 366 Overburden/BedrockMW-9 457 332 Bedrock

MW-10 537 537 Overburden

Minimum years 1.0 0.9Maximum years 1.7 1.7Average years 1.2 1.2

VSP Variogram Analysis

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VSP Spatial Optimization

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Based on kriging root-mean-square-error (RMSE)

Wells ranked in value based on RMSE contribution

Optimization concept: leave enough wells to maintain

accurate kriging of the plume

PNNL, 2014

VSP Spatial Optimization

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VSP Spatial Optimization

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VSP Spatial Optimization

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Monitoring & Remediation Optimization System (MAROS)

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Individual Well Concentration Trends Mann-Kendall Regression

Spatial Moment Analysis Total plume mass Center & spread of plume mass Trends

Sampling Network Optimization Well redundancy/sufficiency Monitoring frequency analysis

MAROS Input

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Time series data

Coordinates

Time consolidation

Cleanup Goals

Cumulative Trend Analysis Results

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Spatial Moment Analysis: Total Plume Mass

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How MAROS Spatial Optimization Works

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Calculate Slope Factor (Measured vs. Estimated)

Delaunay Triangulation

0 to 1, 0 = no information provided by well

Calculate Average Concentration & Triangulation Area Ratios

Iterative Optimization

Remove low slope factor wells

Check ratios

MAROS thresholds

How MAROS Temporal Optimization Works

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Individual Well Frequency

Cost Effective Sampling (CES) Method

Rate of Change of Constituents at Wells (Trends, Variability)

Network-Level Frequency

Based on Zeroeth Moment (Total Plume Mass) COV

Higher Plume Mass Variability = More Sampling

User thresholds

Example MAROS Optimization Output

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VSP vs. MAROS

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VSP Pros

Transparency & ease of use

Geostatistical foundation for spatial optimization

Intuitive iterative thinning algorithm

MAROS Pros

Spatial moment quantification & trending

Network-level optimization based on zeroeth moment

Option to evaluate new well locations

Takeaways

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LTM Optimization means Spatial & Temporal

Efficient Statistical Tools Now Available in VSP

“Multiple lines of evidence” approach

Professional judgment remains important

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