Groundwater Monitoring Network Design for Geologic Carbon...
Transcript of Groundwater Monitoring Network Design for Geologic Carbon...
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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
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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
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• 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%)
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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!