Deoxygenation Prevents Arsenic Mobilization during Deepwell

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Environmental Modeling

Deoxygenation prevents arsenic mobilization during deepwell injection into sulfide-bearing aquifers Henning Prommer, Jing Sun, Lauren Helm, Bhasker Rathi, Adam Siade, and Ryan Morris Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b05015 • Publication Date (Web): 01 Nov 2018 Downloaded from http://pubs.acs.org on November 2, 2018

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Environmental Science & Technology

1

Deoxygenation prevents arsenic mobilization during deepwell injection into

2

sulfide-bearing aquifers

3 4

Henning Prommer*,1,2,3, Jing Sun*,1,2, Lauren Helm4, Bhasker Rathi1,5,

5

Adam J. Siade1,2,3, and Ryan Morris4

6 7 8 9

1 CSIRO 2 School

of Earth Sciences, University of Western Australia, 35 Stirling Hwy, Perth, WA 6009, Australia

3 National

Centre for Groundwater Research and Training (NCGRT), Adelaide, SA 5001, Australia

10 11

Land and Water, Private Bag No. 5, Wembley, WA 6913, Australia

4 Origin 5 Center

Energy, 339 Coronation Drive, Milton, QLD, Australia

for Applied Geosciences, University of Tuebingen, Tuebingen, Germany

12 13 14

Corresponding Authors*

15

Prommer: Phone: (+61) 8 9333 6272; Fax: (+61) 8 9333 6499; E-mail: [email protected]

16

Sun: Phone: (+61) 8 9333 6011; Fax: (+61) 8 9333 6499; E-mail: [email protected]

17 18 19 20 21

To be submitted to Environmental Science and Technology

1 ACS Paragon Plus Environment


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ABSTRACT

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Coal Seam Gas (CSG) extraction generates large volumes of co-produced water. Injection of the excess

25

water into deep aquifers is often the most sustainable management option. However, such injection

26

risks undesired sediment-water interactions that mobilize metal(loid)s in the receiving aquifer. This

27

risk can be mitigated through pre-treatment of the injectant. Here, we conducted a sequence of three

28

push-pull tests (PPTs) where the injectant was pre-treated using acid amendment and/or deoxygenation

29

to identify the processes controlling the fate of metal(loid)s and to understand the treatment

30

requirements for large-scale CSG water injection. The injection and recovery cycles were closely

31

monitored, followed by analysis of the observations through reactive transport modeling. While

32

arsenic was mobilized in all three PPTs, significantly lower arsenic concentrations were observed in

33

the recovered water when the injectant was deoxygenated, regardless of pH adjustment. The

34

breakthrough of arsenic was commensurate with molybdenum, but distinct from phosphate. This

35

allowed for the observed and modeled arsenic and molybdenum mobilization to be attributed to a

36

stoichiometric co-dissolution process during pyrite oxidation, whereas phosphate mobility was

37

governed by sorption. Understanding the nature of these hydrochemical processes explained the

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greater efficiency of pre-treatment by deoxygenation on minimizing metal(loid) mobilization

39

compared to the acid amendment.


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TOC ABSTRACT

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INTRODUCTION

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Over the next four decades, coal seam gas (CSG) production in Queensland, Australia will require the

45

management of large quantities of co-produced water extracted from coal seam horizons, and peaking

46

to ~70-80 GL year-1 over the ten-year peak production period.1 For some production sites, the most

47

economically feasible management option is to treat the CSG co-produced water to high quality

48

standards and to re-inject the treated water into either the depleted coal producing formations or deeper

49

aquifers.2 However, depending on the reactivity of the target aquifer, the geochemical dis-equilibrium

50

between the injectant and the composition of the receiving aquifer matrix can drive a wide range of

51

interactions between water and sediments, potentially contaminating the receiving aquifer. One of the

52

major concerns is the risk of mobilizing toxic metal(loid)s, in particular arsenic. Several previous

53

studies of managed aquifer recharge (MAR) have demonstrated that concerns of groundwater arsenic

54

contamination are even warranted in cases where neither the ambient groundwater nor the injectant

55

showed any elevated arsenic concentrations.3-10 In situ mobilization of geogenic arsenic during MAR

56

has been attributed to several different geochemical mechanisms.3 For example, Jones and Pichler

57

(2007)4 and Wallis et al. (2011) and (2018)5, 6 linked the temporary release of arsenic in the Suwannee

58

Limestone at aquifer storage and recovery (ASR) sites in southwest Florida to pyrite oxidation. On the

59

other hand, McNab et al. (2011)7 attributed elevated arsenic concentrations at a MAR site in the Central

60

Valley, California to the more alkaline character of the recharge water and arsenic desorption under

61

pH changes. Fakhreddine et al. (2015)8 explained arsenic mobilization at a MAR site in Orange

62

County, California to result from the lack of divalent cations in the purified injectant and subsequent

63

arsenate desorption from negatively charged clays. Also, both Appelo and de Vet (2003)9 and

64

Vanderzalm et al. (2011)10 discussed cases where competition from phosphate for sorption sites caused

65

arsenic displacement during aquifer recharge.

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Arsenic mobilization was also recently observed during a push-pull test in which deoxygenated CSG

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co-produced water was injected into the Precipice Sandstone, the lowermost aquifer (~1,250 m deep

69

at that site) in the Surat Basin, Australia.11 Complimentary laboratory sorption experiments showed

70

that arsenite was more soluble under the more alkaline conditions that were locally generated by the

71

injectant. However, none of the conceptual and numerical models of the reactive transport processes

72

that attempted to ascribe arsenic mobilization to desorption of arsenic from the aquifer matrix were

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able to even semi-quantitatively explain the field observations. Therefore, it was hypothesized that

74

either the deoxygenated injectant still contained low levels of oxidation capacity or that some (slow)

75

dissolution of arsenopyrite occurred in the absence of oxidants. Under this hypothesis, it was possible

76

to reproduce the observed dissolved arsenic concentrations for the recovery phase, while

77

simultaneously reproducing the observed concentrations of other related species such as sulfate. It was

78

therefore concluded that the presence of oxidants such as (residual) dissolved oxygen (DO) exerts an

79

important control on the long-term fate of arsenic in artificially recharged aquifers.

80 81

These and other incidences suggest that a good understanding of the geochemical responses to the

82

injection of water is required to predict and manage water quality evolution and risks for the receiving

83

aquifers at both the local and regional scale. An obvious management option for minimizing arsenic

84

mobilization is to manipulate the injectant composition such that potential arsenic desorption and co-

85

dissolution processes are suppressed. However, to date only sparse information is available that

86

unambiguously links observed groundwater arsenic concentrations after water injection with specific

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pre-treatment methods.8 In particular, as far as we are aware, there is no evidence in the scientific

88

literature that deoxygenation of the injectant can successfully prevent arsenic mobilization under field-

89

scale conditions.

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In this study, we conducted and analyzed a series of push-pull tests with different injectant pre-

92

treatment techniques at a pristine site in the Surat Basin, Australia where large-scale CSG water re-

93

injection would be implemented. The objectives were to (i) identify and quantify the key

94

hydrogeological and geochemical processes controlling the evolution of water composition; and (ii)

95

determine injectant pre-treatment requirements for limiting or preventing the mobilization of geogenic

96

arsenic. We collected detailed hydrochemical data from the field trial and used a reactive transport

97

modeling approach to integrate and quantitatively evaluate the observations. The results from this

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study have not only implications for this and other CSG operations but also for many other MAR

99

schemes in pyrite-bearing aquifers that are potentially under the risk of arsenic mobilization.

100 101

MATERIALS AND METHODS

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Study Site Information.

103

The Condabri study site is located in southeast Queensland, Australia. No water injection had

104

previously occurred at this site, therefore it provided undisturbed aquifer conditions. Within the

105

Condabri Development Area, CSG and associated water are produced from the Jurassic age Walloon

106

Coal Measures between 450 and 800 m below ground level (mBGL). These coal measures are

107

separated from the Precipice Sandstone, the injection target aquifer, by the Eurombah Formation

108

(aquitard), Hutton Sandstone (aquifer), and Evergreen Formation (aquitard), in total ~325 m thick,

109

with the Evergreen Formation ~230 m thick. The lowermost portion of the Precipice Sandstone is

110

known as the Braided Stream Facies (BSF), which is considered to be the most permeable section of

111

the overall formation, comprising relatively coarse-grained material representative of a high energy

112

fluvial depositional environment.12 The screened interval of the trial injection/recovery bore (CON-

113

INJ2-P) was 1189-1278 mBGL, which included 24 m of the upper Precipice Sandstone, the entire 36

114

m of the BSF, and 29 m of the underlying Moolayember Formation, a shale.

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Portions of five sediment samples from the BSF sub-unit were ground to fine powder using a tungsten

117

carbide ring-grinder mill and analyzed by X-ray powder diffraction (XRD), using a Philips PW-1830

118

diffractometer with Cu-Kα radiation. The crystalline mineral phases in each sample were quantified

119

using Siroquant™, a commercial software written based on the Rietveld technique.13 Quartz was found

120

to be the predominant mineral (> 92%), with minor amounts of clay, feldspar, and carbonate minerals.

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Siderite was detected in three of the five samples, with concentration up to 11%. Pyrite was not

122

detected via powder XRD, which typically has a detection limit of 1-5%. The concentration of reduced

123

inorganic sulfur species (e.g., pyrite) in the sediment samples was determined by the chromium-

124

reducible sulfur method14 and found to be between 0.01% and 0.02%. The five samples from the BSF

125

sub-unit were also fused with lithium metaborate and cast into a disc, following the method of Norrish

126

and Hutton.15 The disc was analyzed by X-ray fluorescence (XRF) spectrometry using a Philips PW

127

2400 spectrometer, and SuperQ and Pro-trace interpretation software. International mineral standards

128

were used to calibrate the XRF system, and detection limit was found to be 0.5 mg kg-1. Based on

129

XRF, sediment iron concentration was between 52 and 834 mmol kg-1 (i.e., between 0.29% and

130

4.67%), arsenic concentration was between 47 and 73 µmol kg-1 (i.e., between 3.5 and 5.5 mg kg-1),

131

and molybdenum was mostly undetectable except for one sample in which the concentration was 7.3

132

µmol kg-1 (0.7 mg kg-1). Baseline water chemistry was established by multiple groundwater samples

133

collected during a 15 day, ~1,000 m3 day-1, pumping test conducted on the trial bore prior to water

134

injection. Based on field pH measurements, the ambient groundwater had a pH of ~6.5, whereas the

135

raw CSG water had a pH of ~9.2. Other than the trial bore, the closest water supply bore to the study

136

site accessing the Precipice Sandstone was 7 km distant. There are no identified springs or potentially

137

baseflow-connected watercourses associated with the Precipice Sandstone in the vicinity of the study

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site. More details about hydrostratigraphy at Condabri and the sediment characteristics are provided

139

elsewhere.16

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CSG Water Injection Trial, Sampling and Analysis.

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The field trial of CSG water injection was performed as a sequence of three separate push-pull tests

143

(PPT1-PPT3). The first two PPTs were performed to demonstrate the feasibility of acid amendment to

144

the injectant to prevent arsenic mobilization, while the third PPT was performed to test the necessity

145

of deoxygenating the injectant. The injection surface facilities at Condabri included a 200,000 m3 lined

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feed pond, located ~200 m north of the trial bore and tied-in to the surrounding network gathering

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produced water from active CSG wells. From the feed pond, the raw CSG co-produced water was fed

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directly into the portable water treatment plant via a temporary inlet and suction line. The facilities had

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a maximum design capacity of ~3,000 m3 day-1. Treatment prior to injection included (i) removing

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suspended solids by pre-filtration with 200 µm disk filters; (ii) membrane filtration to 0.04 µm; (iii)

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dosing with 33% hydrochloric acid towards neutral pH (PPT1 only); (iv) disinfection by ultra-violet

152

irradiation; and (v) removal of DO by membrane contactors (PPT1 and PPT2 only). The DO of treated

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water was continuously monitored by the programmable logic controller via electrodes in flow-through

154

unit. Based on the monitoring data, the DO concentration in the treated water was 0.63 ± 0.20 µmol

155

L-1 (0.010 ± 0.003 mg L-1) during PPT1 and PPT2, and 538 ± 20 µmol L-1 (8.62 ± 0.32 mg L-1) during

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PPT3. Samples of the treated water were also regularly collected, and electrical conductivity, pH and

157

temperature were recorded using a calibrated YSI Pro Plus immediately following collection. pH of

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the treated water was 7.48 ± 0.23 during PPT1, and 9.19 ± 0.09 during PPT2 and PPT3. Water samples

159

were filtered as needed to 0.45 µm, preserved in accordance with laboratory requirements, and sent to

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Australian Laboratory Services for water composition analysis. These samples were analyzed for

161

alkalinity, major ions, heavy metals/metalloids, nutrients, and organic substances, using PC Titrator,

162

inductively coupled plasma mass spectrometry, ion chromatography, and total organic carbon

163

analyzer. The typical water compositions are listed in Table 1.

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The PPTs commenced on October 28th, 2014 and continued intermittently through to July 6th, 2015.

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During this 250 day period, the injection and recovery phases occurred during day 0-38 and 47-71,

167

respectively, for PPT1; day 79-125 and 128-176, respectively, for PPT2; and day 178-198 and 208-

168

250, respectively, for PPT3 (Figure 1). The injection phases were characterized by several flow

169

interruptions that resulted from operational problems. During PPT1, the maximum injection rate was

170

limited to ~1,000 m3 day-1 and the total volume of water injected was ~9,000 m3, while during PPT2

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and PPT3, rates of 1,800-2,200 m3 day-1 were reached during injection and the total volumes of water

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injected were ~43,000 and 26,000 m3, respectively. Temporary amendment of a bromide tracer (added

173

as NaBr) was undertaken as part of the trial to (i) assess the physical transport characteristics of the

174

Precipice Sandstone formation in terms of mixing and dispersion/dilution effects and (ii) identify the

175

potential contribution of fracturing to the flow and transport characteristics of the aquifer. The tracer

176

amendment was prepared by mixing 25 kg of laboratory grade NaBr into a 30,000 L blending tank and

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injected 2 days before the end of the injection phase in each PPT (i.e., on day 36, 123 and 196,

178

respectively). The blending tank was located between the microfiltration and deoxygenation systems

179

in the water treatment plant. Following each injection phase, a positive displace pump (Mono 820)

180

was installed into the bore to recover the water. An InSitu© RuggedTroll temperature logger was also

181

installed during recovery phases in PPT2 and PPT3. Compared to the injection rates, extraction rates

182

were more stable, while remaining below 1,000 m3 day-1 (Figure 1). The recovered water was sampled,

183

preserved and analyzed in similar fashion to the injectant water. No additional bores were available

184

on-site within the radius of influence of the trial bore to provide additional water quality monitoring

185

data.

186 187

Numerical Modeling Approach and Tools.

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A coupled flow, solute/heat and reactive transport model was developed to assist the interpretation of

189

the hydrochemical data collected during the CSG water injection trial. In a first step, a local-scale, 9 ACS Paragon Plus Environment


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radial-symmetric numerical flow model was constructed with MODFLOW17 to simulate the

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groundwater flow condition during PPT1-PPT3. The model domain was radially limited to 400 m. The

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lateral discretization varied between 1 m near the trial bore, and ~20 m for the grid cell most distant

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from the bore. The vertical model extent was limited to the depth zone between 1,213 and 1,249

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mBGL, corresponding to the depth of the BSF sub-unit, neglecting the substantially less permeable

195

remainder of the Precipice Sandstone. Based on the hydrogeological logs and geophysical data,16 the

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BSF sub-unit was relatively homogeneous and could be represented by a single layer with

197

homogeneous hydrogeological properties. During the field trial, the hydraulic gradients were

198

dominated by the injection and extraction fluxes and ambient groundwater flow was negligible. The

199

injection and extraction rates logged during the field trial were discretized into daily time steps to

200

describe the sometimes highly transient flow conditions.

201 202

On the basis of the computed flow field, subsequent transport simulations for conservative species,

203

heat transport and reactive species were performed with the reactive multi-component transport code

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PHT3D.18 The pre-trial water and sediment data were used to define the initial ambient conditions

205

(Table 1), while the regularly collected injectant measurements were used to discretize the time-

206

varying injectant composition. The known, amended bromide mass was added in the model over a 1

207

hour long period during each PPT in accordance with the experimental procedure of the tracer tests.

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Measured bromide concentrations comprised the primary calibration dataset for the physical transport

209

parameters that impact flow and conservative transport behavior, such as the longitudinal dispersivity.

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Owing to differences in chloride and boron concentrations between the ambient groundwater and the

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injectant, measured chloride and boron concentrations also provided valuable calibration data. Because

212

of the substantial temperature differences between the ambient groundwater (~67ºC) and the injectant

213

(18-33ºC), heat transport was considered in the model (i) to appropriately describe the temperature-

214

dependency of geochemical reactions and (ii) as an additional environmental tracer. Heat transport 10 ACS Paragon Plus Environment

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was included in the model following the previously published approach,11,

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parameters were selected based on the similarities between solute and thermal energy transport.

19

and heat transport

217 218

After a good understanding of groundwater flow and solute transport processes was developed, the

219

model was extended to analyze the reactive transport behavior. Based on experimental observations,

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the defined reaction network consisted of equilibrium-based solution speciation of all relevant ions

221

including arsenic, cation exchange, surface complexation, and several kinetically controlled redox

222

reactions. Even though pyrite was not detected in the BSF sediments by XRD, due to significant sulfate

223

mobilization under aerated injectant in PPT3, oxidative dissolution of pyrite (FeS2) was included in

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the reaction network:

225

𝐹𝑒𝑆2(𝑠) + 3.75𝑂2 + 3.5𝐻2𝑂 = 𝐹𝑒(𝑂𝐻)3(𝑠) + 2𝑆𝑂24 ― + 4𝐻 +

(1)

226

Based on the concentration of reduced inorganic sulfur in the BSF sediments quantified by the

227

chromium-reducible sulfur method, the initial concentration of pyrite was set to 2.5 mmol kg-1

228

(0.03%), which is substantially lower than the detection limit of XRD, and allowed to deviate during

229

the model calibration process. Consistent with previously published studies,5, 11, 20-22 the rate of pyrite

230

oxidation 𝑟𝑝𝑦𝑟 was modeled as:

231

(

0.5 ―0.11 𝑟𝑝𝑦𝑟 = (𝐶0.5 × 10 ―10.19 × 𝑜2 + 𝑓2𝐶𝑁𝑂3― )𝐶𝐻 +

𝐴𝑝𝑦𝑟 𝑉

𝐶 0.67 𝑓(𝑇) 𝐶0 𝑓(𝑇𝑟𝑒𝑓)

)( )𝑝𝑦𝑟

(2) 𝐴𝑝𝑦𝑟

( ) is the ratio

232

where 𝐶𝑂2, 𝐶𝑁𝑂3― and 𝐶𝐻 + are the DO, nitrate and proton concentrations, respectively,

233

of pyrite surface area to solution volume, 115 dm-1 mol-1,11, 21 and

234

changes in 𝐴𝑝𝑦𝑟 resulting from the progressing reaction. 𝑓2 is a constant, which was assumed to be

235

unity.21, 23 T is the groundwater temperature in ºC, and 𝑇𝑟𝑒𝑓 is simply a reference temperature, for

236

example, the average groundwater temperature during the trial. 𝑓(𝑇) represents a function accounting

237

for the temperature dependency of the pyrite oxidation reaction:21, 24


𝑉

( )𝑝𝑦𝑟 is a factor that accounts for 𝐶 𝐶0


238

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𝑓(𝑇) = 𝑒

―(𝑇 + 273.15𝑎1 + 𝑎2)

(3)

239

where 𝑎1 and 𝑎2 are empirical constants that mimic the reaction mechanisms of the Arrhenius equation,

240

which were adopted from literature data,21 and set to −6758.1 K and 16.1, respectively. The ferric iron

241

produced from pyrite oxidation was assumed to precipitate as ferrihydrite (simplified as Fe(OH)3,

242

Reaction 1). The evolution (precipitation and dissolution) of ferrihydrite was modeled as a

243

thermodynamically controlled equilibrium reaction.22,

244

from an earlier performed injection trial in the same Precipice formation,11 the deoxygenated injectant

245

in PPT1 and PPT2 in the model was assumed to contain a residual oxidant concentration represented

246

by a DO concentration of 15 µmol L-1 (0.24 mg L-1), which was then subject to model calibration. The

247

inclusion of DO in this case did not imply that molecular oxygen was present at that exact

248

concentration; rather it represented oxidants more generally. Additionally, siderite (FeCO3) was found

249

necessary in the reaction network based on observed iron concentration and allowed to precipitate

250

and/or dissolve following the standard rate formulation:22, 26

251

25

Consistent with the hypothesis developed

𝑟𝑠𝑖𝑑𝑒𝑟𝑖𝑡𝑒 = 𝑘𝑠𝑖𝑑𝑒𝑟𝑖𝑡𝑒 × (1 ― 𝑆𝑅𝑠𝑖𝑑𝑒𝑟𝑖𝑡𝑒)

(4)

252

where 𝑘𝑠𝑖𝑑𝑒𝑟𝑖𝑡𝑒 is the rate coefficient, and 𝑆𝑅𝑠𝑖𝑑𝑒𝑟𝑖𝑡𝑒 is the saturation ratio of siderite, which determines

253

the reaction direction and accounts for the impacts from the dynamically changing geochemical

254

conditions and temperatures on reaction rate. The stoichiometries and thermodynamic constants of the

255

considered Fe minerals are listed in Table S1.

256 257

Similar to sulfate, dissolved arsenic and molybdenum concentrations were also substantially elevated

258

during PPT3. Trace metals including arsenic and molybdenum are often incorporated in the structure

259

of pyrite and have been shown to co-dissolve stoichiometrically during pyrite oxidation.4, 11, 27-29 This

260

process was considered in the reaction network and the rate of trace metal co-dissolution (𝑟𝑎𝑠𝑝𝑦 for

261

arsenic or 𝑟𝑚𝑜𝑝𝑦 for molybdenum) was modeled by scaling 𝑟𝑝𝑦𝑟with a proportionality term (aspy or

262

mopy), which represents the molar ratio of trace metal within pyrite: 12 ACS Paragon Plus Environment

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𝑟𝑎𝑠𝑝𝑦/𝑚𝑜𝑝𝑦 = (𝑎𝑠𝑝𝑦 𝑜𝑟 𝑚𝑜𝑝𝑦) × 𝑟𝑝𝑦𝑟

(5)

264 265

To account for the potential for cation exchange reactions to affect groundwater quality evolution

266

during the trial, an exchanger site (X) was implemented in the model. The stoichiometries and

267

thermodynamic constants of the cation exchange reactions are listed in Table S1. Based on relevant

268

observations, adsorption and desorption of phosphate were also considered and assumed to occur as

269

surface complexation reactions. A phosphate surface complexation model (SCM) was previously

270

established for the sediments from the BSF sub-unit of the Precipice Sandstone formation, based on

271

laboratory sorption experiments.11 This phosphate SCM and a surface site (Con_w) were implemented

272

in the model in this study. Arsenic or molybdenum sorption, on the other hand, was found to be

273

negligible.

274 275

The parameters used in the numerical model were initially selected based on literature values, where

276

available, and then refined such that the sum of the squared residuals between observations and

277

simulation equivalents was minimized. Following an initial manual trial-and-error calibration, the

278

parameters were further refined as needed by an automatic calibration step using a heuristic particle

279

swarm optimization (PSO) algorithm. The PSO code was written within the PEST++ platform using

280

the YAMR run manager 30, 31 and linked with PHT3D. The parameters to be automatically calibrated

281

included reaction rate coefficients, initial pyrite concentration, DO concentration in PPT1/PPT2 (as

282

proxy for oxidants), cation exchange capacity, surface site density, and SCM reaction constants (Table

283

2). All other reactions, stoichiometries, and thermodynamic constants remained consistent with the

284

standard WATEQ4F database.32 The observation data used to constrain the automatic calibration

285

consisted of measurements of pH, alkalinity, arsenic, molybdenum, iron, calcium, sulfate, and

286

phosphate in the recovered water. The procedure of observation weight assignment was adopted from

287

Sun et al., (2018).22 Using the PSO Monte Carlo algorithm described in Rathi et al., (2017),11 a formal 13 ACS Paragon Plus Environment


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nonlinear uncertainty analysis of the parameter estimates was conducted, resulting in 1,000 realizations

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of parameter sets from the posterior distribution.

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RESULTS AND DISCUSSION

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Analysis of Flow, Conservative Transport and Heat Transport Behavior.

293

Overall, most of the monitored groundwater constituents displayed conservative transport behavior.

294

During the recovery phase of each PPT, while the initial recovered water mostly showed a similar

295

composition to the corresponding injectant, upon continued extraction the observed aqueous

296

concentrations reversed towards the concentrations found under ambient conditions (Figures 2, 4 and

297

S1). The physical transport behavior during the field trial, as deduced from the observations during the

298

recovery phases of the PPTs, was accurately described with a relatively simple conceptual/numerical

299

hydrogeological model. During model development and calibration, no evidence was found to suggest

300

a high level of aquifer heterogeneity or a flow/transport behavior that would be characteristic for a

301

fractured rock aquifer.

302 303

To prevent the bromide concentrations from diluting to below the limits of practical detection, each of

304

the bromide tracer injections during the field trial was conducted towards the end of the injection phase.

305

As a result, bromide was recovered relatively fast during extraction and indeed without excessive

306

dilution (Figure 2). The observed bromide concentrations provided valuable information for the

307

calibration of the conservative transport model. Good agreements between simulated and observed

308

data were also achieved for chloride and boron. Nevertheless, compared to bromide, chloride and

309

boron results were less sensitive to the longitudinal (macro-)dispersivity of 1.5 m that was selected as

310

a parameter value in the employed advective-dispersive transport model (Table 2). This might be

311

explained by the fact that the travel distance of bromide prior to extraction was less than that of the

312

other injected solutes, especially during the larger injection cycles PPT2 and PPT3 (Figure 3). Given 14 ACS Paragon Plus Environment

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313

the well-known scale-dependency of longitudinal dispersion, it is possible that the calibrated value,

314

based on bromide, underestimates the dispersion that the other solutes have undergone during

315

subsurface transport.33 Based on simulated concentration profiles (Figure 3), the penetration distance

316

of the injectant (corresponding to 50% of Δboron) was ∼20 m during PPT1, while it was ∼60 m and

317

∼50 m during PPT2 and PPT3, respectively. These differences in penetration distance between each

318

PPT were a result of differences in the volumes of water injected (Figure 1). As evident from the still

319

elevated boron concentrations at the end of the recovery phases, the injected solutes were not

320

completely recovered, even though the extracted volume exceeded the injected volume in each PPT.

321 322

The subsurface water temperatures around the trial bore changed dynamically during the field trial

323

(Figure 2). Compared to the InSitu© temperature logger data, manually measured temperatures during

324

water sampling were similar for the low temperatures but consistently lower for the higher

325

temperatures, indicating that some cooling had occurred for the manual measurements at the ground

326

surface. Therefore, the calibration of the heat transport parameters in the model (Table 2) was largely

327

based on the temperatures recorded in situ by the logger. Some small discrepancies between simulated

328

and observed temperatures, however, exist for the initial period of each recovery phase. The most

329

likely explanation for the small discrepancy is that (i) the model does not account for potential vertical

330

heat transfer from under- or overlying aquitards, especially during the storage (no-flow) phases before

331

extraction and/or (ii) that the heat transfer between the injectant and the aquifer matrix was

332

incomplete.19 Nevertheless, the results of the heat transport simulations agree reasonably well with the

333

temperature measurements (Figure 2). These simulated temperatures were internally used in the

334

reactive transport simulations to define the reaction temperature.

335



Page 16 of 31

336

Observed Hydrochemical Patterns.

337

Prior to PPT1, the ambient groundwater in the receiving aquifer was anoxic, as indicated by relatively

338

high methane and ammonium concentrations, and mildly acidic (Table 1). The raw CSG co-produced

339

water that was collected and stored in the feed pond on-site, on the other hand, contained molecular

340

oxygen due to aeration, and had a significantly higher, alkaline pH due to degassing and potentially

341

some redox transformations. The CSG water used in the injection of the PPTs was treated with

342

different combinations of pre-treatment methods. During PPT1, the injection of pH-adjusted,

343

deoxygenated CSG water led to elevated dissolved iron concentrations and subtle arsenic mobilization

344

(Figure 4). The sulfate concentration in the recovered water in PPT1 was also relatively high, which

345

is a result of the high sulfate concentration in the injectant rather than geochemical reactions. The

346

sulfate in the injectant originated from a residual volume of surface water, which was stored in the

347

same feed pond and used for a preceding injection trial within another formation at the Condabri study

348

site.34 During PPT2, the injection of pH-unadjusted, deoxygenated CSG water led to phosphate

349

mobilization and also still subtle, but slightly more pronounced mobilization of arsenic and

350

molybdenum. The peak phosphate, arsenic and molybdenum concentrations in the recovered water

351

were on the order of 10-6, 10-7 and 10-8 mol L-1, respectively, identical with the results obtained during

352

the PPT at the Reedy Creek study site, which was conducted in a similar fashion to the Condabri

353

PPT2.11,

354

significant geochemical response among the three conducted PPTs. Induced by the oxidant-abundant

355

injectant, the oxidative dissolution of pyrite occurred affecting water quality. The occurrence of pyrite

356

oxidation is clearly evidenced by a substantial increase in dissolved sulfate concentrations. Coinciding

357

with the increase in sulfate concentrations, peak concentrations of dissolved arsenic and molybdenum,

358

two trace elements that often substitute within pyrite and can also accumulate as sulfide minerals by

359

themselves such as orpiment and molybdenite,4, 11, 27-29 were both one order of magnitude higher in

360

PPT3 than those in PPT2. The magnitude of phosphate mobilization during PPT3, on the other hand,

35

The injection of pH-unadjusted, aerobic CSG water during PPT3 caused the most


Page 17 of 31


361

remained at the same level as what was observed during PPT2. Additionally, elevated dissolved iron

362

concentrations were observed during the late recovery phase of PPT3, when the water volume

363

extracted exceeded the water volume injected (Figures 1 and 4).

364 365

Model-based Interpretation of Observed Metal Mobilization.

366

The observed hydrochemical responses to the injection and recovery of treated CSG water could be

367

closely replicated by the developed reactive transport model, illustrating that the conceptual

368

hydrogeochemical model and its numerical implementation provide a good representation of the key

369

processes occurring in the field trial (Figures 2, 4 and S1). The breakthrough behavior of sodium,

370

calcium, alkalinity, and many other groundwater constituents could be reproduced by conservative

371

transport simulations (i.e., without inclusion of any geochemical reaction). This provides evidence that

372

the injection target Precipice Sandstone formation has a limited reactivity, at least over the investigated

373

time-scale. Nevertheless, the substantial increases in dissolved sulfate, arsenic and molybdenum

374

concentrations observed during the recovery phase of PPT3 were reaction-driven. By considering

375

pyrite oxidation and an associated stoichiometric co-dissolution of arsenic and molybdenum, the

376

observed concentration changes could be reproduced by the model. As illustrated by the simulated

377

concentration profiles, arsenic was mobilized during aquifer passage, with a distinct leading front of

378

elevated dissolved arsenic coinciding with the position of the front of the aerobic injectant (Figure 3C).

379

Moreover, the model simulations suggest that ferrihydrite precipitated in conjunction with pyrite

380

oxidation, which explains why the sulfate release in PPT3 was not accompanied by a simultaneous

381

equivalent iron release. The model simulations also suggest that freshly accumulated ferrihydrite re-

382

dissolved when anoxic native groundwater migrated back towards the injection/recovery bore. It can

383

be seen that once the extraction volume approached the injection volume, iron release occurred

384

(Figures 1 and 4). Similar to ferrihydrite, siderite could also both dissolve and precipitate during the

385

field trial due to the dynamically changing water composition in the subsurface, which could affect the 17 ACS Paragon Plus Environment


Page 18 of 31

386

breakthrough pattern of iron (the poorly calibrated results from a model variant that excluded the

387

transformation of siderite are shown in Figure S2).

388 389

Based on the analysis of calibration results, dissolved arsenic concentrations in the field trial were

390

highly sensitive to the parameter aspy, which represented the magnitude of arsenic incorporation in

391

pyrite. As aspy increases, more arsenic is released to the groundwater. This is also illustrated with

392

molybdenum; with an order of magnitude lower abundance (mopy is an order of magnitude lower than

393

aspy, Table 2), molybdenum exhibited a much less significant release compared to arsenic (Figure 4).

394

The magnitude of arsenic incorporation in pyrite varies considerably between environmental systems,

395

from no incorporation to up to about 4 mol%.36 In the final calibrated model, aspy in the BSF sub-unit

396

at Condabri was determined to be 3.61 ± 0.22 mol% (Table 2). While the estimated value is relatively

397

high, it agrees reasonably well with what was previously estimated for the same formation during the

398

Reedy Creek injection trial (2.65 ± 0.26 mol%) through a formal nonlinear uncertainty analysis.11

399 400

Because water temperature can impact not only thermodynamically controlled speciation/mineralogy

401

but also the kinetic reaction rates such as pyrite oxidation, it was originally expected that dissolved

402

arsenic concentrations would be also sensitive to the temperature difference between the injectant and

403

ambient groundwater. To illuminate the role of temperature on arsenic mobilization, a model variant

404

assuming instant heat transfer in the subsurface (i.e., reaction temperature set to ambient 67ºC) was

405

constructed on the basis of the final model (Figure S3). Surprisingly, little discrepancies were found

406

for dissolved arsenic in the recovered water. This is because while higher temperature would promote

407

faster pyrite oxidation, the ultimate amount of pyrite oxidation and arsenic co-dissolution nevertheless

408

largely depended on the availability of the oxidant, in this case, DO in the injectant.

409


Page 19 of 31


410

Contrasting Behavior between Phosphate and Arsenic.

411

An interesting observation, which underpins our conceptual model of arsenic release and was

412

successfully reproduced by the reactive transport model, is that while arsenic mobilization was

413

dramatically different between PPT2 and PPT3, the level of phosphate mobilization remained similar

414

(Figure 4). The contrast between the fates of dissolved arsenic and phosphate during the three PPTs

415

can be explained by their significant differences in reactivity with sulfide and with sediment surfaces.37

416

In contrast to arsenic, phosphate does not readily react with sulfide to form insoluble minerals. Instead,

417

the fate of phosphate during the field trial was governed by adsorption/desorption processes, especially

418

responding to the induced pH changes (Figures 4 and S4). The pH sorption edge for phosphate on the

419

Precipice Sandstone sediments sat between the injectant pH of 7.5 for PPT1 and the injectant pH of

420

9.2 in the other two PPTs (Table 1). This pH sorption edge is consistent with the results of phosphate

421

sorption studies on some clay minerals, and also on iron oxides which however are less likely to exist

422

under anoxic native condition in the Precipice Sandstone formation.38, 39

423 424

While the model’s ability to reproduce the high arsenic concentrations during PPT3 is important for

425

quantifying sulfidic arsenic co-dissolution as induced by an oxidant-abundant injectant, reproducing

426

the relatively subtle arsenic mobilization from deoxygenated injectant during PPT1 and PPT2 was

427

originally thought to hold the key for confirming and quantifying arsenic desorption from the sediment

428

surfaces. However, arsenic mobilization during PPT1 and PPT2 could have been explained solely by

429

the same hypothesis from the Reedy Creek study, i.e., either the deoxygenated injectant still contained

430

low levels of oxidation capacity or some (slow) dissolution of pyrite occurred in the absence of

431

oxidants.11 The modeling results suggest that instead of being a result of the acid dosing treatment, the

432

more pronounced arsenic mobilization in PPT2 compared to PPT1 was simply a result of the larger

433

injection volume and thus a larger total mass of exogenous oxidants reacting during PPT2. Therefore,

434

the arsenic breakthrough patterns for all three PPTs could be closely replicated by a reactive transport 19 ACS Paragon Plus Environment


Page 20 of 31

435

model relying solely on the stoichiometric co-dissolution process during pyrite oxidation. This

436

suggests that surface-bound arsenic in the BSF sub-unit at Condabri was possibly insignificant or, if

437

not, insensitive to the geochemical changes imposed by the CSG water injection. Consequently,

438

compared to phosphate, the overall fate of arsenic was much less pH dependent than anticipated prior

439

to our detailed analysis of the PPTs (Figure 4). In contrast to the studies reported by Wallis et al.,

440

(2010) and (2011),5, 20 where the fate of arsenic cannot be described by the numerical model without

441

arsenic attenuation by (re)adsorption, the model developed in this study did not require any additional

442

attenuation process to replicate the arsenic dynamics. This further supports the hypothesis that, in

443

contrast to the finely ground Precipice Sandstone material employed for dedicated, site-specific batch

444

sorption experiments,11 the in situ sediment surfaces possess a low affinity for arsenic sorption under

445

the investigated hydro-geochemical conditions.

446 447

IMPLICATIONS

448

Combining experimental observations with reactive transport modeling, this study demonstrates the

449

important role that deoxygenation can play in minimizing the risk of mobilizing arsenic and other trace

450

metals during the injection of oxygenated excess waters into deep aquifers. This study further reveals

451

that enhanced risks are mostly associated with the presence of pyrite and the variety and abundance of

452

heavy metals that are often incorporated in the pyrite structure. As illustrated by the present case, this

453

risk may also exist even without detectable pyrite, i.e., detection by commonly applied mineralogical

454

techniques. While this study mostly addressed the understanding and predictions of the geochemical

455

impacts of the injection of CSG co-produced water, it is also relevant for many other types of MAR

456

schemes where oxidized solutions are injected into pyrite-bearing aquifers. In the present case, acid

457

amendment showed to have a smaller than originally anticipated role on mitigating dissolved arsenic

458

concentrations. At other sites where pyrite oxidation is absent, preventing elevated injectant pHs might

459

still be critical for reducing arsenic mobility. Nevertheless, arsenic, in such a scenario, might less likely 20 ACS Paragon Plus Environment

Page 21 of 31


460

be subject to further migration of the contamination, because native sediments would have some

461

affinity for arsenic sorption and be able to attenuate the mobilized arsenic. Our analysis illustrates that

462

identifying the fundamental reaction mechanisms is an important step in designing an effective pre-

463

treatment.

464 465

ACKNOWLEDGEMENTS

466

This study was financially supported by the Origin Energy and Gas Industry Social and Environmental

467

Research Alliance (GISERA) of Australia. The PSO calibration and uncertainty analysis were

468

conducted on CSIRO’s Pearcey high performance computer cluster. The authors would like to thank

469

O. Atteia for his continuous support with the PHT3D implementation into the ORTI GUI. The authors

470

also would like to thank I. Wallis, S. Fakhreddine, and D. Welter for their valuable input.

471 472

ASSOCIATED CONTENT

473

Supporting information available: Tables S1 – S2 and Figures S1 – S4, which contain additional data

474

from the field trial and additional details on the parameters and simulation outputs from the numerical

475

model. The Supporting Information is available free of charge on the ACS Publications website at

476

DOI: 10.1021/esxxxxx.

477 478

NOTES: The authors declare no competing financial interest.

479



480

Page 22 of 31

FIGURES AND TABLES

481 482

Table 1. Typical initial (ambient) groundwater and injectant composition during the trial. Individual

483

measurements of injectant composition are shown in Figures 2, 4 and S1 as symbols in red background. Species pH temperature TDS a Al As(+3) b As(+5) b B Ba Br a C(-4) C(+4) Ca Cl DOC F Fe(+2) K Mg Mn(+2) Mo N(-3) N(+5) Na O(0) c P S(-2) S(+6) Si Sr

unit -

ºC mg L-1 mol L-1 mol L-1 mol L-1 mol L-1 mol L-1 mol L-1 mol L-1 mol L-1 mol L-1 mol L-1 mol L-1 mol L-1 mol L-1 mol L-1 mol L-1 mol L-1 mol L-1 mol L-1 mol L-1 mol L-1 mol L-1 mol L-1 mol L-1 mol L-1 mol L-1 mol L-1

Ambient 6.50 67.0 3619 1.85×10-7 6.67×10-9 0 1.11×10-5 1.57×10-5 5.66×10-5 1.61×10-4 1.33×10-2 2.62×10-3 4.70×10-2 5.00×10-4 1.58×10-5 5.93×10-6 1.25×10-3 5.76×10-4 2.91×10-7 5.21×10-9 1.32×10-4 0 6.53×10-2 0 1.61×10-7 1.56×10-6 1.32×10-9 1.01×10-3 3.22×10-5

PPT1 7.48 26.7 2594 2.25×10-7 2.67×10-8 0 4.38×10-5 3.75×10-6 5.34×10-5 0 8.30×10-3 1.95×10-4 3.95×10-2 6.31×10-4 1.04×10-4 1.44×10-6 2.59×10-4 7.87×10-4 2.99×10-8 7.81×10-9 1.84×10-6 4.94×10-7 4.92×10-2 6.30×10-7 1.61×10-7 1.56×10-6 7.74×10-5 9.43×10-4 1.36×10-5

PPT2 9.17 27.4 2958 2.64×10-7 2.96×10-8 0 4.03×10-5 4.42×10-6 5.53×10-5 0 1.69×10-2 2.96×10-4 3.08×10-2 5.32×10-4 1.20×10-4 1.19×10-6 2.72×10-4 4.76×10-4 3.44×10-8 5.95×10-9 8.57×10-6 7.14×10-7 5.49×10-2 6.30×10-7 2.42×10-7 1.56×10-6 1.38×10-5 8.24×10-4 1.33×10-5

PPT3 9.20 19.7 3590 1.85×10-7 0 1.68×10-7 5.71×10-5 3.85×10-6 6.01×10-5 0 2.10×10-2 2.37×10-4 3.51×10-2 5.16×10-4 1.37×10-4 4.48×10-7 2.56×10-4 4.45×10-4 1.74×10-8 3.52×10-8 1.07×10-6 4.46×10-7 5.86×10-2 5.38×10-4 1.61×10-7 0 1.69×10-5 8.43×10-4 1.29×10-5

484 485

Note: a The reported number represents the measurement on the injectant without bromide amendment;

486

b

487

simulated assuming local redox equilibrium; and c the DO concentration in PPT1 and PPT2 used in

488

the numerical model is higher than the measured concentration, see Table 2 for model input.

the concentration of total dissolved arsenic was measured, while the redox status of arsenic was


Page 23 of 31


489

Table 2. Key parameters employed in the final calibrated model and associated statistics. Some

490

parameters were estimated by PEST/PSO, and basic statistics for the 1,000 Monte Carlo realizations

491

are reported.a Correlation coefficients between model parameters are given in Table S2. Estimated Parameter Value

Parameter

Physical Transport and Heat Transport Parameters [unit] Longitudinal dispersivity [m] 1.5 Thermal diffusion coefficient [m2 day-1] 1.5 Thermal distribution coefficient [m3 kg-1] 5×10-4

Prior Standard Deviation

Posterior Standard Deviation

% Variance Reduction

-

-

-

-

-

5.77×10-10

2.58×10-

80.0%

2.02×10-13

6.54×10-

Most Likely Range Lower

Upper

Chemical Reaction Related Parameters [unit] 1.05×10-12

Siderite evolution (𝒌𝒔𝒊𝒅𝒆𝒓𝒊𝒕𝒆) [mol L-1 s-1]

10

10

Arsenic molar fraction in pyrite (aspy) [unitless]

3.61×10-2

8.57×10-2

2.16×10-3

99.9%

2.98×10-2

3.39×10-2

Molybdenum molar fraction in pyrite (mopy) [unitless]

3.44×10-3

9.43×10-3

3.31×10-4

99.9%

3.18×10-3

3.79×10-3

Initial pyrite concentration (py) [mol kg-1]

2.30×10-3

5.98×10-4

1.07×10-4

96.8%

2.14×10-3

2.30×10-3

DO concentration after deoxygenation (DO) [mol L-1]

1.55×10-5

5.72×10-5

7.70×10-6

98.2%

2.02×10-6

1.73×10-5

Exchange species (X) [mol L-1]

2.19×10-3

5.00×10-3

1.28×10-3

93.4%

1.80×10-4

3.01×10-3

Surface sorption site (Con_w) [mol L-1]

1.08×10-3

4.97×10-3

2.31×10-3

78.5%

3.29×10-5

5.73×10-3

Surface Complexation Stoichiometries and Constants (log Ks) Con_wOH + H+ = Con_wOH2+ (log K1)

4.16

1.73

1.19

52.5%

5.01

6.73

Con_wOH = Con_wO- + H+ (log K2)

-8.27

1.17

0.44

85.7%

-8.27

-6.96

Con_wOH + PO43- + 3H+ = Con_wH2PO4 + H2O (log K3)

33.02

2.31

1.53

56.1%

29.72

32.13

Con_wOH + PO43- + 2H+ = Con_wHPO4- + H2O (log K4)

12.99

2.31

1.91

31.4%

14.17

16.84

Con_wOH + PO43- + H+ = Con_wPO42- + H2O (log K5)

20.53

2.08

1.14

69.8%

17.59

19.94

492 493

Note: a The most likely ranges for the parameters are based on the modal bin ranges of the posterior

494

histogram; each parameter range was divided into 3 bins for this calculation. Some estimates remain

495

slightly outside the modal range suggesting that they may have a slightly lower likelihood.



Page 24 of 31

496 497

Figure 1. (A) Injection/extraction rates and (B) calculated cumulative water volume injected during

498

the trial. Injection phases are marked in red background, recovery phases in blue background, and

499

intercycle storage phases in white. A zero cumulative volume injected indicates the amount of water

500

extraction is equal to the amount of injection.


Page 25 of 31


501 502

Figure 2. Observed and simulated groundwater (A) bromide, (B) chloride and (C) boron

503

concentrations, and (D) temperature during the trial. During the injection phases (red background), the

504

symbols represent the injectant conditions; and during the recovery phases (blue background), the

505

symbols represent the measurements of the recovered water. On (D) temperature subplot, yellow ‘□’

506

symbols represent the temperatures that were manually measured at the ground surface, whereas red

507

‘o’ symbols represent in situ measurements that were continuously recorded by a temperature logger

508

during PPT2 and PPT3. Reactive and conservative transport simulations are plotted as solid blue lines

509

and dashed black lines, respectively.



Page 26 of 31

510 511

Figure 3. Temperature and concentration fronts of bromide, boron, arsenic and phosphate (A, C, E) at

512

the end of injection (time = 38, 125, and 198 days) and (B, D, F) at the end of extraction (time = 71,

513

176, and 250 days) during PPT1-PPT3, calculated based on reactive transport simulations. Cmax stands

514

for the maximum value for individual species.


Page 27 of 31


515 516

Figure 4. Observed (red ‘o’ symbols) and simulated groundwater (A) arsenic, (B) molybdenum, (C)

517

iron, (D) sulfate, (E) pH, (F) phosphate, (G) alkalinity and (H) calcium concentrations during the trial.

518

Reactive and conservative transport simulations are plotted as solid blue lines and dashed black lines,

519

respectively. Note that reactive simulations (solid lines) are taken from the model after the reaction

520

step, which should not necessarily agree with the injectant composition (symbols and dashed lines)

521

during injection (red background). On (A) arsenic and (B) molybdenum subplots, data and simulation

522

results from PPT1 and PPT2 are shown on two scales for clarity. On (E) pH subplot, yellow ‘□’

523

symbols represent laboratory measurements, whereas red ‘o’ symbols represent field measurements

524

that were recorded immediately following sample collection. On (F) phosphate and (G) alkalinity

525

subplots, red ‘+’ symbols represent observed total phosphorus and dissolved inorganic carbon

526

concentrations, respectively.



Page 28 of 31

527

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529

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