Ya-Mei Yang1 , Robert Dilmore1 , Kayyum Mansoor2 , Susan Carroll2 , Grant Bromhal1 , Mitchell Small3 1NETL, 2LLNL, 3CMU

June 10, 2015. Berkeley CA

Groundwater Monitoring Network Design for Geologic Carbon Sequestration

Risk-based monitoring strategy development combining multiple monitoring technologies for CO2 leakage detection

Monitoring Techniques

Depth of Monitor

Depth of interval being monitored

Soil flux Shallow Shallow

Deformation Surface, wellbore

Surface, multiple intervals

Tracer Shallow / intermediate

Shallow / intermediate

Groundwater monitoring (pH, TDS, As, Cd…)

Shallow Shallow

Above zone interval (P, sat)

Deep Deep

Storage reservoir (P, sat)

Storage interval

Storage interval

Seismic Surface, wellbore

Surface, multiple intervals

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• Step 1: Simulation of natural system S k, l, m(n)(Xij, t)

• Step 2: Obtain or assume the prior probability distribution (i.e., weight) of each type of leakage pathway P0(l)

• Step 3: Decide the threshold βk(Xij, t) for each monitoring technique k

• Step 4: Estimate the probability of detection PD, i.e. P[Dk, l, m(n)(Xij, t+lag)] , for each leakage pathway

• Step 5: Decide monitoring network and estimate the decision criteria – max PD

Procedure

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Case Study: High Plains Aquifer

Stochastic leakage events at High Plain aquifer were simulated using NUFT, including the variation of permeabilities in sand and clay, sodium molality, trace metal molality and organic molality and CO2 and brine leakage rates. The resulting changes were reflected in groundwater monitoring parameters: pH, TDS and benzene concentration.

Single Leak Model Domain

Leak point

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Prior probability of potential leakage pathway P0(l)

Prior probability of potential leakage pathway

Prior I (equal)

Prior II (prefer random)

Prior III (prefer known well)

P0(known well) 0.5 0.3

0.8

P0(random unknown well)

0.5 0.7 0.2

P0(both known and unknown wells)

0 0 0

P0(no leak) 0 0 0

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PD(tD) = ∑ Pl,D(tD)∗ P0(l) 𝑳𝑳𝒍𝒍=𝟏𝟏

• Two threshold:

- 95% percentile of the background (initial) data - 99.5% percentile of the background (initial) data

• Two evaluation bases: - mean of the estimated PD of all realizations - median of the estimated PD of all realizations

P[D] for known leakage

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Threshold values - 95%

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Threshold values – 99.5%

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PD basis for known leakage

Calculate PD for all realizations (n=1, 2, …, 100) of each monitoring parameter (pH, TDS, benzene)

Then summarize them in mean PD, median PD layers, etc., as the basis for the following calculations

…

n=1

n=2

n=100

…

mean

median

pH

Benz

TDS

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pH – mean PD

Year 1

Year 5

Year 10

95% threshold 99.5% threshold

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pH – median PD

Year 1

Year 5

Year 10

95% threshold 99.5% threshold

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TDS – median PD

Year 1

Year 5

Year 10

95% threshold 99.5% threshold

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Benzene – median PD

Year 1

Year 5

Year 10

95% threshold 99.5% threshold

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PD for unknown leakage

100 m spacing

200 m spacing

500 m spacing

1000 m spacing

2000 m spacing

Random leak location (n=1000) and size estimated from simulations

Different monitoring density grids +

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Median Time 100 m 200 m 500 m 1 km 2 km

Year 1 1 0.55 0.094 0.0199 0.002 Year 2 1 1 0.34 0.084 0.016 Year 3 1 1 0.59 0.15 0.035 Year 4 1 1 0.74 0.22 0.053 Year 5 1 1 0.74 0.22 0.053

Year 10 1 1 1 1 1

Mean Time 100 m 200 m 500 m 1 km 2 km

Year 1 0.002 0 0 0 0 Year 2 0.002 0 0 0 0 Year 3 0.002 0 0 0 0 Year 4 0.002 0 0 0 0 Year 5 0.999 0.33 0.048 0.01 0.001

Year 10 1 0.55 0.097 0.02 0.002

Combined diagnosis of pH, TDS and benzene (99.5 background threshold, 99% PD)

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Detection capacity for different monitoring grid sizes (median: B99.5% , D99%)

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Detection capacity for different monitoring grid sizes (median: B99.5% , D99%)

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Detection capacity for different monitoring grid sizes (median: B99.5% , D99%)

Detection capacity for different monitoring grid sizes (median: B99.5% , D99%) combined diagnosis is better than individual

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An illustration using a naïve 4 point design

• P.ave vs. P.max?

One design case P.ave.ran P.ave.known

(4 points & well) pH 0.005 0.20 TDS 0.005 0.28 Bz 0 0.20 Combined (pH, TDS & Bz) 0.010 0.54 Prior I (equal) 0.5 0.5 Prior II (prefer random) 0.7 0.3 Prior III (prefer known well) 0.2 0.8 PD total for prior I 0.27 PD total for prior II 0.17 PD total for prior III 0.43

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• Information from multiple observed monitoring signals can be used to develop a better informed decision about leakage diagnosis given uncertainty.

• Groundwater leakage case used as example to evaluate probability of leak detection from known sources, and generalized to estimate monitoring density for leakage from unknown sources

• Probabilistic design allows the capacity of full risk assessment including not only true leakage events, but also false positive and false negative events.

• Future work: optimization of given condition (monitoring design, monitor numbers) using multiple criteria (max PD(ave or max), max spreadness, max utility…). Use of field background data and simulations of more leakage locations and pathways. Applications of this methodology to deeper subsurface monitoring technologies.

Summary and Next Steps

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Thank you!