Saline–freshwater interaction in porous media is a phenomenon of practical interest particularly for the management of water resources in arid and semi-arid environments, where precious freshwater resources are threatened by seawater intrusion and where storage of freshwater in saline aquifers can be a viable option. Saline–freshwater interactions are controlled by physico-chemical processes that need to be accurately modeled. This in turn requires monitoring of these systems, a non-trivial task for which spatially extensive, high-resolution non-invasive techniques can provide key information. In this paper we present the field monitoring and numerical modeling components of an approach aimed at understanding complex saline–freshwater systems. The approach is applied to a freshwater injection experiment carried out in a hyper-saline aquifer near Cagliari (Sardinia, Italy). The experiment was monitored using time-lapse cross-hole electrical resistivity tomography (ERT). To investigate the flow dynamics, coupled numerical flow and transport modeling of the experiment was carried out using an advanced three-dimensional (3-D) density-driven flow-transport simulator. The simulation results were used to produce synthetic ERT inversion results to be compared against real field ERT results. This exercise demonstrates that the evolution of the freshwater bulb is strongly influenced by the system's (even mild) hydraulic heterogeneities. The example also highlights how the joint use of ERT imaging and gravity-dependent flow and transport modeling give fundamental information for this type of study.
Multiphase flow in porous media has been the subject of intensive study for many decades, motivated, amongst other factors, by important economic considerations linked to the petroleum industry. Another field where interaction of pore fluids having different physical properties, which is of particular importance, is saline–freshwater systems. In this case, important density and viscosity differences between saline and fresh waters control the relative motion and mixing of the two phases. Characterizing and modeling these coupled flow and transport phenomena is a very challenging task, particularly in the presence of the hydraulic heterogeneities always present in natural porous media (e.g., Werner et al., 2013; Ketabchi et al., 2016).
The most common situation where saline–freshwater systems have practical environmental and socio-economic implications is related to seawater intrusion in coastal aquifers, often exacerbated by overexploitation of groundwater, particularly in arid and semi-arid regions such as those surrounding the Mediterranean basin (e.g., Kallioras et al., 2010; Rey et al., 2013; Dentoni et al., 2015). Another context where the study of saline–freshwater interactions is highly important is the injection and storage of freshwater in brackish or salty aquifers for later use in agriculture or for domestic purposes, also known as aquifer storage and recovery (ASR; e.g., Pyne, 1995; Dillon, 2005).
Many studies of density-dependent flow and transport phenomena in porous media have been conducted over the past decades (e.g., Gambolati et al., 1999; Simmons et al., 2001; Diersch and Kolditz, 2002). Instabilities and fingering can take place when denser water overlies lighter water (e.g., Simmons et al., 2001). Ward et al. (2007) gave an introductive literature review on density-dependent modeling, with a particular focus on ASR. The first studies on the injection of freshwater into a saline aquifer were performed by Bear and Jacobs (1965) and Esmail and Kimbler (1967). The latter investigated the tilting of the saltwater–freshwater interface, a phenomenon known as “buoyancy stratification”. More recent studies have analyzed the efficiency of ASR for both field and synthetic cases (e.g., Kumar and Kimbler, 1970; Moulder, 1970; Kimbler et al., 1975; Ward et al., 2007, 2008; Lu et al., 2011; Zuurbier et al., 2014). Ward et al. (2008) conducted a numerical study to evaluate the efficiency of ASR under density-dependent conditions with anisotropy and heterogeneity of high and low permeable layers. Van Ginkel et al. (2014) studied the possibility of extracting saltwater below the freshwater injection to prevent the spreading of freshwater at the top of the aquifer. Alaghmand et al. (2015) investigated fresh river water injection into a saline floodplain aquifer and developed a numerical model for the optimization of injection scenarios.
The behavior of saline–freshwater systems becomes increasingly complex with
larger density and viscosity contrasts. To date, very little research has
been done on the effects of freshwater injection in highly saline aquifers
that can reach total dissolved solids concentrations of 100 g L
As in many other subsurface characterization problems, a major contribution can be made by non-invasive, spatially extensive, geophysical techniques. In particular, electrical and electromagnetic methods are very suitable in the context of saline–freshwater interactions, since electrical conductivity varies over orders of magnitude depending on solute concentrations. While the use of these methods is common in seawater intrusion studies (e.g., Goldman and Kafri, 2006; Nguyen et al., 2009), only few studies have used geophysics to monitor ASR experiments. Davis et al. (2008) used time-lapse microgravity surveys to monitor the utilization of an abandoned coal mine as an artificial ASR site. Maliva et al. (2009) investigated the use of geophysical borehole logging tools applied to managed aquifer recharge systems, including ASR, to improve the characterization of aquifer properties. Minsley et al. (2011) developed an integrated hydro-geophysical methodology for the siting, operation and monitoring of ASR systems using electrical resistivity, time-domain electromagnetics and seismic methods. Parsekian et al. (2014) applied geoelectrical imaging of the subsurface below an aquifer recharge and recovery site alongside with hydrochemical measurements to identify preferential flow paths.
A major step forward in saline–freshwater systems monitoring can be made by improving the efficiency of advanced geophysical techniques, and electrical tomographic methods in particular. Electrical resistivity tomography (ERT) is widely used today in hydro-geological and environmental investigations. Often applied in tracer studies (e.g., Kemna et al., 2002; Vanderborght et al., 2005; Cassiani et al., 2006; Doetsch et al., 2012), ERT is a natural choice for saline–freshwater interaction monitoring, given the correlation between the salinity of a pore fluid and its electrical conductivity. Time-lapse ERT, where only the changes in electrical conductivity over time are imaged (e.g., Kemna et al., 2002; Singha and Gorelick, 2005; Perri et al., 2012), can be especially effective in tracking dynamic processes. Whereas tracer studies are typically designed with injection of a saline tracer into fresh surrounding groundwater, only very few studies have dealt with the inverse case of freshwater injection into a saline formation. For instance, Müller et al. (2010) conducted tracer tests also using a less dense tracer with lower electrical conductivity than the ambient groundwater, monitored with ERT.
The goal of this study is to present a general approach for the characterization, monitoring and modeling of complex saline–freshwater systems, based on the combination of non-invasive techniques and accurate numerical modeling. To our knowledge, no such a comprehensive hydro-geophysical approach concerning freshwater injection in saline aquifers has been presented so far in the scientific literature; thus, we believe this case study can be very useful as a starting point for other, more comprehensive methodological testing. In this study we limit ourselves to integrating field data and modeling in a loose manner, with no aim at this stage to develop a full data assimilation framework, as implemented elsewhere for simpler systems (e.g., Manoli et al., 2015; Rossi et al., 2015). The key message that can be derived from the joint use of advanced field techniques and advanced numerical modeling is nonetheless apparent in the presented case study, and more complete assimilation approaches are possible provided that the advantages and limitations of the individual components (data and models) are fully understood as shown in the present paper.
The approach is presented in the context of a case study where we injected freshwater into a hyper-saline aquifer in the Molentargius Saline Regional Park in southern Sardinia, Italy. The experiment was monitored using cross-hole time-lapse ERT. To investigate the mixing processes, the resulting ERT images are compared with the results of a synthetic numerical study of the same experiment. We consider here both homogeneous and heterogeneous (layered) systems. For a quantitative comparison between the field and synthetic studies, spatial moments of the freshwater bulb are calculated.
The Molentargius Saline Regional Nature Park is located east of Cagliari in southern Sardinia, Italy (Fig. 1). The park is a wetland situated very close to the coastline. The exceptional nature of the site is given by the presence of both freshwater and salty water basins separated by a flat area with mainly dry features (called “Is Arenas”). The freshwater areas include two ponds that originated as meteoric water retention basins. The salty water areas include the stretches of water of the former system of the Cagliari salt pans.
Geographical location of the test site:
The park area is characterized by an oligocenic–miocenic sedimentary succession of ca. 100 m (Ulzega and Hearty, 1986) overlaid by pleistocenic deposits of marine and continental origin and by alluvial and offshore bar deposits, whose origin is still debated (Coltorti et al., 2010; Thiel et al., 2010). This ongoing scientific debate has implications for the comprehension of the phenomenon of hyper-saltiness of the park groundwater.
The specific site of investigation is located in the flat dry area within
the park (Is Arenas, Fig. 1c). The water table of the unconfined aquifer
is stable at 5.2 m below ground surface (b.g.s.), and practically neither
lateral groundwater flow nor tidal effects are evident. The sediments are
composed mostly of sands, with thin layers of silty sand, clayey sand and
silty clay (Fig. 2). The groundwater reaches salinity levels as high as
3 times the NaCl concentration of seawater. Such high salt concentration
is likely the long-term legacy of infiltration of hyper-saline solutions
from the salt pans dating back, in this area, to Roman times. Electrical
conductivity fluid logs (see Fig. 3) recorded in boreholes allowed two
zones to be discriminated, with a transitional layer in between; (1) from
the water table to a depth of 6.5 m the water electrical conductivity is
about 2 S m
Generalized stratigraphy log from the five drilled boreholes including lithology, percentage of fine fraction, and porosity from samples as well as electrical conductivity of borehole fluid. The water table lies at 5.2 m b.g.s.
Five boreholes for ERT measurements were drilled with 101 mm inner diameter to a depth of 20 m and positioned in the shape of a square with 8 m sides (four corner boreholes) and one borehole at the center (Fig. 1b). All boreholes are equipped with a fully screened PVC pipe (screen with 0.8 mm size).
In November 2011, 19.4 m
The direct electrical conductivity measurements described in the previous subsection correspond to the data that would be available as a result of a standard monitoring plan, and is highly insufficient for drawing any conclusions concerning the processes that take place during and after freshwater injection. The available dataset was great enriched by ERT measurements, described below.
Time-lapse ERT monitoring was applied during the injection experiment in order to image the developing freshwater bulb, “visible” thanks to its lower electrical conductivity compared to the surrounding saltwater. Each borehole bears externally to the casing 24 stainless steel cylindrical electrodes, permanently installed from 0.6 to 19 m depth with 0.8 m separation, with the exception of the central borehole where the first electrode is placed at the surface and the last at 18.4 m depth. ERT measurements were carried out in a two-dimensional (2-D) fashion, along two vertical planes diagonal along the boreholes, i.e., one plane was using the borehole numbers 1, 5, and 3 and the second plane the borehole numbers 2, 5, and 4 (see Fig. 1b), thus making use of 72 electrodes per plane. This choice, in contrast to a full 3-D acquisition, was predicated on minimizing the acquisition time, given that the freshwater–saltwater movement was expected to be relatively rapid.
Electrical conductivity log of the fluid in borehole 5 at different times after start of freshwater injection (Sect. 2.2). 0 h denotes the background measurement before injection. At 1 h there are no measurements below 12 m b.g.s. because the packer system occupied the borehole.
The ERT measurements were conducted using a Syscal Pro and adopting different configuration setups, consisting of in-hole dipole–dipole measurements in a skip-zero mode (i.e., adjacent electrodes form a dipole) and cross-hole dipole–dipole (hereafter referred to as bipole–bipole) measurements (Fig. 4). Measurements were collected in normal and reciprocal configurations (i.e., exchanging the current and potential dipoles) for estimation of data errors. The acquisition for one complete measurement frame (consisting of roughly 7300 individual readings) required about 40 min.
Schematic description of the ERT measurement configurations used. For dipole–dipole measurements, one dipole is always within one borehole, the other dipole also moves into the adjacent borehole. Bipole–bipole measurements are done as cross-hole measurements and are also changing as diagonals (i.e., A stays while B moves downwards for up to five electrode positions before A is also moved, similarly for M and N).
ERT data were acquired in a time-lapse manner to investigate the changes
over time caused by the electrical conductivity changes of the developing
freshwater bulb within the saline aquifer. The first time step,
Due to technical errors (such as bad connection of electrodes, problems with power supply) and varying data quality, the ERT data were processed prior to inversion. In particular, data having a misfit larger than 5 % between normal and reciprocal readings were removed.
The temperature difference between the groundwater (21
The ERT field data from the freshwater injection experiment were inverted
using the smoothness-constraint inversion code CRTomo. A full description of
the code is given by Kemna (2000). In the inversion, the data errors are
represented according to a linear model expressed as
Resistivity images exhibit a variable spatial resolution (e.g., Ramirez et
al., 1995; Alumbaugh and Newman, 2000; Nguyen et al., 2009). A useful
indicator for this variation is the cumulative sensitivity
Cumulated sensitivity distribution for the inverted background
(
In a time-lapse monitoring framework, one is primarily interested in the
temporal changes of data and parameters. Therefore, we used the “difference
inversion” approach of time-lapse ERT (e.g., LaBrecque and Yang, 2000;
Kemna et al., 2002), where the inversion results are changes with respect to
the background data at time
The ERT dataset was collected under challenging conditions, in particular as the very large salinity contrasts are manifested as extreme electrical conductivity differences over space and time. Large electrical conductivity can occasionally bring DC electrical currents into a nonlinear (non-Ohmic) regime, which in turn can lead to violation of the conditions for the reciprocity theorem (Binley et al., 1995; Cassiani et al., 2006). This has clear implications in terms of data processing, as in particular the error analysis based on reciprocal resistances may not guarantee that direct and reciprocal resistances are equal to each other. Filtering the data according to a reciprocity discrepancy equal to the data error level chosen for the inversion (see above) meant that a fairly large percentage of the data (about 50 %) were rejected. Nonetheless, a large volume of resistance data were still retained (nearly 2000 values per time instant).
The very high electrical conductivity of the system, which is characteristic of this experiment, has also another consequence; i.e., separated inversion of the different electrode configurations (dipole–dipole and bipole–bipole) showed that the bipole–bipole configurations provide better overall results than the dipole–dipole configuration results (not shown here). This is not a common situation, as observed elsewhere in situations of standard resistivity ranges (e.g., Deiana et al., 2007, 2008), where dipole–dipole data provide higher-resolution images than bipole–bipole data that generally only give smoother images as information is averaged over large volumes. In the case shown here, for an in-hole current dipole, the current lines will not penetrate far away from the borehole as they are short-circuited by the large electrical conductivity of saline water surrounding at all times the external boreholes, whereas for the cross-hole current bipole the current lines “have to” penetrate through the volume between the boreholes. Thus, the sensitivity for the dipole–dipole configurations decreases very strongly with increasing distance from the boreholes. However, the dipole–dipole configuration still manages to provide high sensitivity in the area close to the central borehole, particularly at measurement times where the freshwater bulb surrounds this borehole. Hence, the data coming from both configurations were used for inversion.
Figure 6 shows the background image (time
Inverted background (
Electrical imaging (difference inversion) results for the field experiment at different times (in hours after start of injection). The top panel shows the results from borehole plane 1–5–3 and the bottom panel from plane 2–5–4. Black diamonds denote the position of electrodes.
The obtained time-lapse ERT images of the freshwater injection experiment are shown in Fig. 7; the distribution of the injected freshwater in the aquifer surrounding the central borehole is clearly visible, in agreement with the time-lapse conductivity logs in Fig. 3. The very fast vertical migration of the freshwater plume is also apparent. Between 2 and 6 h after the start of injection, the injection borehole (and its surroundings) is nearly totally filled with freshwater, as confirmed by Fig. 3 (after 5 h). However, from the ERT images the freshwater also seems to move downwards below the injection chamber. A few hours after injection, the freshwater plume nearly disappeared in the ERT images, and 1 day after injection the ERT image seems to have gone back to the background situation (as also confirmed by the conductivity logs in Fig. 3).
At about 10 to 11 m depth, the difference images show a separation of the plume into two parts. A layer of finer sediments (see Fig. 2) is likely to cause this separation. Note that the overall high electrical conductivity masks these lithological differences in the background ERT images. This fine layer is a hydraulic barrier that forces freshwater to flow even more through the preferential flow path provided by the borehole itself and its surrounding gravel pack. Above the fine layer, the plume expands again due to the larger hydraulic conductivity of the coarser sediments.
During the experiment, the water table as well as the electrical conductivity and the temperature of the borehole fluid were measured manually in all five boreholes. The water table rose about 1.5 m in the injection borehole and about 0.2 m in the surrounding four boreholes. The electrical conductivity log of the central borehole before, during and after injection is shown in Fig. 3. It can be observed that during injection (i.e., about 1 h after start of injection), the saltwater in the borehole was pushed up by freshwater. Shortly after injection stopped (5 h after start of injection) the freshwater filled the entire borehole length, whereas it is visible that the saltwater already entered the borehole in the bottom part (at about 16 m depth) and made its way upwards. Therefore, 1 day after the injection experiment, the fluid electrical conductivities in the central borehole were practically back to their initial values, with small differences between 8 and 14 m depth still visible. The electrical conductivities of the fluid in the four corner boreholes showed only small changes that nonetheless indicate that part of the freshwater bulb also reached the outer boreholes.
In order to investigate the behavior of the injected freshwater bulb, and assess in particular the influence of the subsurface hydraulic properties on the bulb evolution, we performed a synthetic study based on the field experiment. This was undertaken using a density-dependent flow and transport simulator. Given the computational burden of the simulations and our goal of examining in detail some of the governing parameters, we did not use a data assimilation approach at this stage, opting instead for analyses of specific scenarios. We considered four scenarios of hydraulic conductivity distribution, and compared the simulated results to each other and with the field evidence in order to gain some first insights on the dynamic response of the hyper-saline–freshwater system.
For the coupled flow and transport modeling of the freshwater injection
experiment, we used a 3-D density-dependent mixed-finite element-finite
volume simulator (Mazzia and Putti, 2005). This algorithm was shown to be
very effective in the presence of advection-dominated processes or
instabilities in the flow field induced by density variations (Mazzia and
Putti, 2006). Here, groundwater flow is described by Darcy's law
The mass conservation equations for the coupled flow and transport model can
be written as (Gambolati et al., 1999)
Flow and transport input parameters for the different zones in the model.
For the flow and transport model we used a 3-D mesh (Fig. 8) with about
57 000 tetrahedral elements and 10 000 nodes. The size of the mesh was a
good compromise between mesh resolution and computational effort. The
computational domain extends for 20 m in the
In addition to the boundary condition described above for pressure and with
The conditions for the injection were set by giving the cells that represent
the injection chamber (between 13 m and 14 m b.g.s.), a pressure head
The immediate increase of the injection rate, observed in the field experiment, was modeled by a “de-clogging“ effect of the material closely surrounding the injection chamber (i.e., representing the backfill material). This was done by increasing the hydraulic conductivity of the corresponding cells by about 1 order of magnitude after a corresponding time (i.e., about 5000 s). The simulated and true injection rates are compared in Fig. 9.
Dispersive processes play a minor role for the relatively short timescale of the experiment. In fact, several dispersivity values were tested and compared (modeling results not shown here); their influence is not significant over the short timescale considered here. Thus, only advective transport is studied.
Injection rate of the experiment. The dashed line shows the
observed injection in the field experiment (total volume of injected water
19.4 m
Hydraulic conductivities of each layer for the four different scenarios.
To investigate the influence of heterogeneous hydraulic conductivity distributions in the aquifer, four different scenarios were simulated, including one homogeneous model and three different layered models, with a fine (clay-silt) layer between 10.5 and 11.5 m depth (Table 2). The hydraulic conductivity values for the different scenarios were calibrated manually.
In order to compare, at least in a semi-quantitative manner, the observed
ERT inversions with the results of the synthetic study, it is necessary to
convert first the simulated normalized salt concentration from the
flow-transport model into bulk electrical conductivity, for example through
Archie's law relationship (Archie, 1942), here expressed for saturated sediments:
The next step is to simulate the field data that would be acquired given the
simulated bulk electrical conductivity. For the 3-D electrical forward
modeling, we used the same approach as Manoli et al. (2015) and Rossi et al. (2015). The electric potential field,
The final step was to process and invert the synthetic ERT data in the same way as the field data.
In order to provide a more quantitative comparison between the field and
synthetic experiments, we analyzed 2-D moments as defined for example by
Singha and Gorelick (2005):
Flow and transport modeling results at different times (in hours after start of injection) for scenario 4 (see Table 2).
Comparison of simulation results for different hydraulic
conductivity parameterizations at time 4.2 h after start of injection. The
top panel shows the flow and transport modeling results, the bottom panel
the corresponding simulated ERT results;
Results of synthetic ERT experiment for selected times (in hours after start of injection) for scenario 4 (see Table 2). Black diamonds denote the position of electrodes.
As a first step, let us consider the results of the synthetic study. Figure 10 shows the salt concentration of the flow and transport simulations for scenario 4, which represents the most complex parameterization of the aquifer and is assumed to be most realistic for the test site (see the site stratigraphy reported in Fig. 2). A general upward motion of the injected bulb is visible, with the highest velocities occurring within the injection hole. After some time, the freshwater starts to enter the aquifer along the entire borehole length. Although its density is much less than the density of the surrounding saltwater, the freshwater also moves downwards within the borehole, pushed by the pressure gradients. The 1.2 m thick fine-material layer also plays a clear role in the bulb dynamics. This is expected. In correspondence to this layer, the flow only takes place along the borehole and the backfill material. Above the fine layer the plume expands laterally into the aquifer. Also the transition between the saltwater and the upper freshwater layer above 7.4 m depth moves entirely upwards since the overall movement in the model domain is upwards. One can also observe in the simulation results the tilting of the freshwater–saltwater interface in the lower part of the borehole as well as below the groundwater level, as described by Ward et al. (2007, 2008). The higher the ratio of hydraulic conductivity between the two layers, the stronger is the tilting, as predicted by Ward et al. (2008) (results not shown here).
Figure 11 shows the inverted images for four different subsurface scenarios
at time 4.2 h after start of injection for the flow and transport
simulations and the synthetic ERT monitoring (see Table 2 for definition of
the scenarios). The figure clearly shows the dramatic influence of the
hydraulic conductivity distribution on the shape of the freshwater bulb,
both in the “real” images and in the corresponding inverted ERT images.
Scenario 4, which includes the fine layer, is closest to the field results
as already discussed above. However, scenario 3, with just two layers, shows
a similar behavior in terms of plume development. In general, given the
strong influence that hydraulic conductivity has on the results, it is
conceptually possible to try and infer the site's hydraulic properties on
the basis of the freshwater injection experiment. However, it is also
apparent that calibrating
Indeed, the governing hydraulic effect comes from the different
conductivities of the upper and lower parts of the aquifer (scenarios
1
It is instructive to examine in detail (Fig. 12) the similarities and differences between the ERT field data and the reconstructed ERT images from the simulation scenario that visually appears better than the others (scenario 4). The simulated ERT images show the same general behavior in response to the injection process and associated plume development as the ERT field results. In the field ERT images the freshwater body disappears much faster. After 24 h, although in the field ERT images the freshwater bulb is hardly visible, the simulation still shows its presence. It should be noted that in the simulations the boundary condition at the well is imposed as a Dirichlet (head) condition, so flux is computed depending on the applied head. We applied the head as actually measured in the injection tank. Consequently, the flow is never zero, not even at the end of the experiment. On the other hand, the tilting of the freshwater–saltwater interface as seen in the flow and transport model results is much less visible in the ERT images.
The imaged resistivity changes in the field experiment show less contrast than in the synthetic study. The salinity difference between the freshwater and the saltwater is very large and thus so is the NaCl concentration. Within this range, the electrical conductivity of the water might no longer follow a linear relation with concentration (e.g., Wagner et al., 2013), while here it is assumed to be linear. This can lead to a shifting in the contrast when the concentration is converted into electrical conductivity.
Note also that the gradual change of electrical conductivity in the transition zone (i.e., between 5 and 7.4 m depth) is not visible in the ERT images (Fig. 11). In the transport simulations it can be seen that this zone also moves upwards in the aquifer and becomes thinner (Fig. 10).
Another difference between the field and the synthetic ERT results is the
sharpness of the freshwater body; the boundaries appear smoother in the
field study. Although dispersion effects were not further investigated in
this study, a higher value of
Figure 13 shows the spatial moments (0th moment: total mass; 1st moment: center of mass) of the freshwater bulb for the field and synthetic ERT inversion results, as well as the “true” moments from the flow and transport model (see Sect. 3.3). The total mass is well recovered by the synthetic ERT results (using backwards the same Archie's law parameterization used in the forward modeling). However, the field ERT underestimates the total mass. While this is a known characteristic of moment analysis applied to ERT data for tracer tests (e.g., Singha and Gorelick, 2005), in this specific case it looks likely that the chosen Archie's law parameters are not fully adequate to represent the electrical conductivity–salinity relationship. Considering that even linearity of Ohm's law is questionable at the high salt concentrations observed at the site, one could also question the overall validity of Archie's law. Note that all other factors normally contributing to bad ERT mass recovery under field conditions are the same in the synthetic and the true case, and thus cannot be called into play.
Spatial moments for the field ERT data, synthetic ERT data, and
the true data from the flow and transport model. The moments for the true
field were calculated in 3-D while those for the ERT tomograms were
calculated in 2-D. The field ERT data are separated into the two borehole
planes.
In contrast to the total mass, the vertical center of mass is, despite some early oscillations, well recovered also for the field data. This, however, is known to be a very robust indicator (e.g., Binley et al., 2002; Deiana et al., 2007, 2008).
Overall, and in spite of the differences described above, the comparison between observed and modeled ERT images is satisfactory, particularly in the face of uncertainties concerning the heterogeneities of the real system that could not be investigated in extreme detail. In addition, we cannot exclude the possibility that the linearity of the current flow equation may be violated in such a highly conductive environment, thus leading to inconsistencies between field reality and theoretical assumptions.
Despite the above limitations, the comparison shows that ERT imaging is a viable tool for monitoring freshwater injection in a hyper-saline aquifer. This, by itself, was not an obvious result. The ERT dataset was collected under extreme, challenging conditions. Even so, the ERT data are of fairly good quality considering that we retained only data that passed a fairly strict reciprocity check, knowing that larger reciprocity errors are likely to be related to nonlinear current effects occurring in such high electrical conductivity environments. The study also indicates how an accurate coupled model can mimic in an effective manner the behavior of the observed freshwater bulb that was injected into the domain, and this too was not self-evident.
In this paper we present a hydro-geophysical approach that can be used to study freshwater injections in saline aquifers. In particular the approach is used to monitor and describe a freshwater injection experiment conducted in a hyper-saline aquifer in the Molentargius Saline Regional Park in the south of Sardinia (Italy). The experiment was monitored using time-lapse ERT in five boreholes. A numerical study of the experiment (density-dependent flow and transport modeling in conjunction with ERT simulations) was carried out to investigate the plume migration dynamics and the influence of different hydraulic conductivity parameterizations. The numerical algorithm of the coupled flow and transport model proved to be stable and accurate despite the challenging conditions.
The results demonstrate the feasibility and benefit of using a combination
of (a) time-lapse cross-borehole ERT and (b) numerical modeling of coupled
flow and transport to predict the same ERT results. The comparison between
measured and simulated ERT images was used as the key diagnostics aimed at
estimating the system's governing parameters and consequently describing the
saltwater–freshwater dynamics. More sophisticated data assimilation
techniques can be used to further refine the presented approach in future
work. We can conclude from the present study the following:
The complex dynamics of hyper-saline–freshwater systems can be tracked using
high-resolution spatially extensive time-lapse non-invasive monitoring. On
the contrary, traditional monitoring techniques alone (e.g., conductivity
logs, as in Fig. 3) give only a very partial image, largely inconclusive
to understand the system dynamics. Numerical modeling of these coupled systems is very challenging due to the
presence of strong density/viscosity contrasts and large hydraulic
conductivity heterogeneities. The latter, in particular, largely control the
dynamics of the saltwater–freshwater interaction. In absence of a robust
numerical model, it is impossible to estimate the impact of hydraulic
heterogeneity on this dynamics. A detailed comparison between field data (here, ERT time-lapse images) and
modeled data of the same type enables a better understanding of the
behavior of a freshwater bulb injected into a hyper-saline environment. Fine-tuning of geophysical constitutive relationships, hydraulic and
transport parameters, and system heterogeneities needs to be improved. We
managed to bring the match between field and synthetic data to an acceptable
level with relatively small effort, but it is very difficult to improve the
match further. For instance, in the case presented here the injected
freshwater bulb “disappears” from the real ERT images faster than in the
simulation results. Also, the mass balance is honored easily in the
simulations, whereas in the real data lack of mass is apparent. All of this
points towards a number of aspects that could be improved in the data
matching. However, the target parameters to be modified for this improvement
are not easy to identify, given their very high number and complex nature.
Among these, there are hydraulic parameters and dispersivities, and their
spatial heterogeneities, as well as also Archie's law parameters. This task is
likely to be challenging even in a rigorous data assimilation framework, and
equifinality of model parameterizations is likely. The extreme hyper-saline system considered here is likely to exceed the
limits of linear relationships between current and voltage (Ohm's law) as
well as between electrical conductivity and salinity. Therefore, a full
nonlinear analysis should be conducted, particularly concerning the
electrical behavior of the system. In absence of this, we have to limit
ourselves to a semi-quantitative interpretation, as shown here. Although in typical ASR applications the contrasts of density and salinity
are usually smaller, this study shows that time-lapse ERT is a powerful
monitoring tool for this (and also other) type of hyper-saline
applications. ERT can provide spatial information that is unattainable using
traditional monitoring techniques (e.g., in boreholes). The movement and mixing of the freshwater plume can be very fast; thus, any
ERT monitoring must adopt configurations for quick measurements (e.g., in
the conditions represented in this study an acquisition time of less than
30 min is recommended). In hyper-saline systems, measuring reciprocity may not be the ideal error
indicator since nonlinear phenomena may be triggered, or during the time
between the normal and reciprocal measurement the system may have already
changed, thus invalidating the reciprocity check.
Our study also serves to highlight some of the weaknesses that should be
addressed in future work:
Finally, with regards to practical aspects of freshwater injection and
monitoring in saline aquifers, we can draw the following conclusions:
The example shown in this paper shows how the joint use of ERT imaging and
gravity-dependent flow and transport modeling give fundamental information
for this type of study.
Measured raw cross-borehole time-lapse ERT field data, additional field data,
inverted ERT field data as well as the modeling data in terms of the
concentration distribution of the density-dependent flow and transport model
and the inverted synthetic ERT monitoring results can be accessed at
The authors declare that they have no conflict of interest.
This research was supported by the Basic Research Project L.R. 7/2007 (CRP2_686, Gian Piero Deidda) funded by the Regione Autonoma della Sardegna (Italy) and the EU FP7 project GLOBAQUA (”Managing the effects of multiple stressors on aquatic ecosystems under water scarcity”). We thank the Parco Naturale Molentargius-Saline for allowing us to set up a test site in the park. We also thank the field crew from the University of Cagliari (namely Luigi Noli and Mario Sitzia) as well as Marco Mura, Enzo Battaglia and Francesco Schirru for their work in the field. Special thanks go to Damiano Pasetto and Gabriele Manoli for their support regarding the 3-D ERT forward modeling code and Annamaria Mazzia for assistance concerning the numerical experiments. The data can be obtained from the authors upon request. Edited by: J. Carrera Reviewed by: three anonymous referees