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PNNL SFA: Role of Microenvironments and Transition Zones in Subsurface Reactive Contaminant Transport
PNNL SFA: Role of Microenvironments and Transition Zones in Subsurface Reactive Contaminant Transport
Facies-based Characterization of Facies-based Characterization of Hydrogeologic Structures and Reactive
Transport PropertiesHydrogeologic Structures and Reactive
Transport PropertiesScientific Staff:
Andy Ward (PNNL)
Scientific Staff:
Andy Ward (PNNL)Andy Ward (PNNL)Chris Murray (PNNL)Charlotte Sullivan (PNNL)
Andy Ward (PNNL)Chris Murray (PNNL)Charlotte Sullivan (PNNL)Charlotte Sullivan (PNNL)Steve Yabusaki (PNNL)Roelof Versteeg (INL)
Charlotte Sullivan (PNNL)Steve Yabusaki (PNNL)Roelof Versteeg (INL)
November 4, 2008 PNNL-SA-65828
Objective
Project Objectives and ScopeProject Objectives and Scope
ObjectiveDevelop 3-D facies models that reflect variations in the depositional environment and the associated subsurface depos t o a e o e t a d t e assoc ated subsu aceheterogeneity
ScopeEstablish quantitative links between sedimentary facies and primary hydrophysical properties
requires higher level of sediment characterization whole sediments and size fractionslithocomponents of size fractions
IFRC Linkage
Field Data:Surface Geophysics
Borehole Geophysics Site-Specific Property Transfer/ SFA
IFRC
p yPetrophysical Models
a3-D Inversion
SFA
FY-09
Electrofacies DistributionsBest Estimates of
Facies Distribution
YesYes
and UncertaintyFacies Distribution
Flow/Transport Inversion with PSD Correlation
Structures
NoNo Improved Facies Model
(PSD moments) Criteria met?
Primary sediment properties are controlled by facies distributions, which in turn are controlled by grain size distributions resulting from the depositional environment
Project PremiseProject Premise
Hydraulic PropertiesBulk density
controlled by grain size distributions resulting from the depositional environment
Natural Isotope Abundance 40K, 238U, 232ThGross -ray responseElectrical Properties Bulk density
PorosityResidual water contentWater retentionRelative Permeability
Gross ray responsepDielectric SpectroscopySurface conductivityFormation FactorChargeability Relative Permeability
Particle Size Distribution
Chargeability
Reactive PropertiesSpecific surface areaCEC
Transport PropertiesTortuosityIntrinsic PermeabilityCECpor
SporReaction Kinetics
Thermal PropertiesHeat Capacity
Intrinsic PermeabilityDispersivityFormation Factor
Heat CapacityThermal conductivity
Sediment CharacterizationEstablishing such linkages requires
Particle Size Distribution
4
6
ume
(%)
Establishing such linkages requires higher level of characterization
whole sediments 0.01 0.1 1 10 100 1000 3000
Particle Size (µm)
0
2 Vol
u
IFC 3-30 6.5-8' <2mm (Pre Sonic) - Average, Tuesday, January 13, 2009 11:37:22 AMIFC 3-30 6.5-8' <2mm (10min Sonic) - Average, Tuesday, January 13, 2009 12:05:00 PMIFC 3-30 6.5-8' <2mm (Post Sonic) - Average, Tuesday, January 13, 2009 12:10:58 PM
size fractionsSize analysis
Dry/wet sieveH d t
Particle Size Distribution
1
2
3
4
5
Vol
ume
(%)
HydrometerLaser particle sizeMineralogy of size fractions
Continuous size distributions
0.01 0.1 1 10 100 1000 3000 Particle Size (µm)
0
C6216/2-29 39'-41' 32mm (Pre sonic) - Average, Thursday, January 08, 2009 3:19:48 PMC6216/2-29 39'-41' 32mm (10min sonic) - Average, Thursday, January 08, 2009 3:46:33 PMC6216/2-29 39'-41' 32mm (Post sonic) - Average, Thursday, January 08, 2009 3:50:32 PM
Particle Size of Coatings on 32 mm GravelContinuous size distributionsDifferences in coatings on different size gravel Differences in coatings on same size from different depths 70%
80%
90%
100%
ng
C5213 3-30 6.5'-8'C6216 2-29 39'-41'C6213 2.5'-5'C6212 2-28 47'-49'C6216 2-29 30-33
siltclay
size from different depthsCould impact variability in sorption
20%
30%
40%
50%
60%
70%
Perc
ent P
assi
n
0%
10%
20%
Size (mm)
2 µm 50 µm
G l
-ray Dependence on Grain Size-ray Dependence on Grain Size6
8
)
(a)
GoalEstablish basis for using 40K, 238U, 232Th logs to quantify grain size distributions and spatial variations 0
2
4
40K
(%)
and spatial variationsMeasurements
GEA Analysis on samples from C57086
8(b)
0.01 0.1 1 10 100 1000dg (mm)
0
strong relation between GEA response and grain size statistics
Observed on samples from Hanford and 2
4
6
238 U
(ppm
)
RiflePredictions
MCNP model of -ray spectrometer
0.01 0.1 1 10 100 1000dg (mm)
0
8(c) y p
MCNP model of borehole logNeural network to predict size moments from KUT 2
4
6
232 T
h (p
pm)
0.01 0.1 1 10 100 1000dg.mm.
0
2
Predicted Grain size (C5708)Predicted Grain size (C5708)
20
Obs.Pred.
20 20
60
40
60
40 40
80
60
Dep
th (f
t)
80
60D
epth
(ft)
80
60
Dep
th (f
t)
100 100 100
140
120
140
120
140
120
grsic s
0.0 0.5 1.0IGR
0 2 4 6 840K (%)
0.001 0.1 10dg (mm)
140
Vertical variation in lithology
Integrating Multi-scale HeterogeneitiesIntegrating Multi-scale HeterogeneitiesVertical variation in lithology
Generation of fine-scale heterogeneity patterns Borehole logs for vertical transitionBorehole logs for vertical transition probabilities and conditioning
Horizontal continuity large-scale architectural structuresurface geophysics
Merge fine and large scaleMerge fine- and large-scale heterogeneities
TPROGS algorithm of Carle et al. (1998)(1998)Coupled Markov chain (Dekking et al. 1999)Sequential indicator simulation (SISIM) f GSLIB lib
From Elfeki et al. 2002, Petroleum Geoscience, 8:159-165
(SISIM) from GSLIB library
-ray Properties of Geologic UnitsAssume global histogram of totalAssume global histogram of total is sum of n normal populations
One normal distribution for each lithofaciesO ti i fit f lith f i t l b lOptimize fit of lithofacies to global histogram of data
Three lithofacies identifiedUse spectral 40K, 238U, 232ThUse spectral K, U, Th
Extend to all logged IFRC wellsNeural network approach for identification of faciesCalibrate with grain size dataCalibrate with grain size data
Extend lithofacies simulation to three dimensions
Algorithm employed will be based Facies Percent Mean g p yon comparison of 2-D results 1 32% 151.2
2 40% 161.53 29% 177.5
11
Electrical Properties of Geologic UnitsSelected 4 cores based on EBF-Ksat
Normalized Hydraulic ConductivityProfile, Well 399-2-12
30
21-22’, 43-44’ 52-53’, 58-59’Instrumented for electrical measurements
Resistivity, chargeability (IP)Three major electrofacies identified
32
34
36
38
40
42bgs)
Three major electrofacies identifiedHigh resistivity, low chargeabilityIntermediate resistivity, low chargeabilityLow resistivity, high chargeability
42
44
46
48
50
52
54
Dep
th (f
t b
Low resistivity, high chargeabilityLow fines = low chargeability (<10 mV/V)
mineral type, grain size, Spor, pore water chemistry, physics of interaction between
54
56
58
600.0 0.1 0.2
Normalized Kiy p ysurfaces and fluids
Facies a ( m) m (mV/V)Depth (ft)21-22 1 200043-44 2 90052-53 3 1500
1.04.62.0
12
58-59 4 110 13.0
PorescaleSmooth particle hydrodynamics
Aspherical particlesAspherical particles Packing based on IFRC sedimentsReactive chemistry
Impedance spectroscopy Solve 3-D Poisson equationMultiple phases
Important factorsParticle sizeParticle sizeParticle shapeSurface area/ surface charge
13
FY 2009 Science Deliverables
Property Transfer Models for IFRC SedimentsSpectral gamma logsSpectral gamma logs
3-D electrofacies model reflecting heterogeneity at IFRC well fieldwell field3-D lithofacies model reflecting heterogeneity at IFRC well field