Injection pressures and flow rates primarily depend on nature of the geology and injection fluid. Low-pressure injection and high-pressure injection techniques can be employed to install CBIs. In high-permeability tills, either method can be effective (McGregor 2020). In low-permeability geologies (hydraulic conductivity below 10–5 m/s), it is difficult for low-pressure methods to achieve sufficient distribution and adequate contact with contaminants. High-pressure techniques are superior (Christiansen 2010).

Note that pressure gauges on most (if not all) injection pump systems are located near the pump discharge. At any given flow rate, the pressure at this location is mostly an indication of the pressure drop across the pipes, fittings, hoses, and tooling. Additional calculations or the use of a downhole pressure gauge are required to measure the pressure at the formation (i.e., at depth).

### *16.3.2 Injection Technique and Spacing*

The relative difficulty of distributing AC in the subsurface results in implementation designs using low-volume injections across a high-density injection point grid within the target treatment zone or PRB. These points are laid out horizontally in a triangular grid along the direction of contaminant transport. The spacing between injection points should be adjusted based on geological setting, project goals, and costs. The distance between horizontally placed injection points is informed by experience. Efforts to control distribution out beyond three to six meters are rarely successful in overburden materials because moving closer to the surface correlates with a decreasing weight of the overburden and a corresponding decrease in resistence to flow.

Regarding the vertical spacing between discrete injection depths, a similar installation practice consists of alternating injection depths within the treatment zone on adjacent points. This injection depth "staggering" approach helps ensure that all targeted depth intervals receive CBI. Experience has shown that in low-permeability materials, injections can be completed every 0.5–0.6 m in depth. In high-permeability materials, the ideal spacing is around 1 m, and high-energy injection techniques are required to achieve good distribution of injectate in this case.

Regarding the injection technique, bottom-up injections should be avoided when fine-grained sediments are present. Imagine completing the first injection at depth, then as the injection rod is withdrawn to the next targeted zone, the borehole below the rod remains open. In this type of profile, it takes more pressure to open a fracture than it does to propagate the deeper one, and injectate will simply flow into the first injection location. Bottom-up injections are effective in heaving sand. In general, injections should be completed in a top–down manner so that as injections are completed, the rod is advanced, and it tends to seal off the previous location and prevent injectate cross-circuiting.

### **16.4 Patterns of AC Distribution**

After injection, the CBI will ultimately follow the path of least resistance. As discussed in Chaps. 1, 4, 7, and 8 among others, subsurface properties can vary widely over small scales so it is challenging to predict where CBIs may travel when forced into the formation. Through high-pressure injection (jetting and fracture generation), some control over placement of CBI can be exerted within a region localized around the injection point. However, energy quickly dissipates, and further outward movement of injectate is again relegated to low resistance pathways. In sedimentary environments, small gradations in particle size distributions can represent low resistance pathways and channel flow of injectate.

CBIs is intensely black in color and can be recognized in most cores collected after injection. As illustrated in Fig. 16.1, PAC injected under pressure is commonly found in the form of thin horizontal seams and sometimes as angular or vertical features following the path of least resistance. This is also depicted in Fig. 16.2, where CBI follow subtle differences in sediment texture. CBI injection flow paths are often found to preferentially follow strata interfaces or high-permeability pathways.

Injection of slurries into sands is more challenging than injecting solutions because the suspended solids tend to be filtered from the injectate. Water readily moves through saturated sands, leaving suspended solids localized in a small region around the point of injection if low-energy injection is employed. High-energy injection

**Fig. 16.1** Excavation post-CBI emplacement in the vadose zone demonstrates significant horizontal distribution. The CBI contained PAC. Note the horizontal carbon sheet above the bucket mark traced by a yellow line (dashed). The carbon sheet was ~1.2 m across. A shallower horizontal sheet is traced by an orange line (solid). Two angular carbon sheets having more vertical character are marked by red lines (dotted)

**Fig. 16.2** PAC intercalated between materials exhibiting subtle texture variations

**Fig. 16.3** Distribution of AC throughout aquifer sands after high-energy injection. The sleeve is a standard 3.6-cm diameter tube

techniques can overcome this difficulty as depicted in Fig. 16.3, where a slurry of granular AC was jetted into the aquifer sands at an exit velocity of over 60 m/s.

In summary, high pressure and flow can be employed to force slurries out from the point of injection and emplace it in both high- and low-permeability formations. On the contrary, low-flow and low-pressure (low energy) injection techniques will not distribute most CBI in clay lenses or other low-permeability zones. One strength of colloidal AC-based injectate is that vary uniform distributions in sands, and other high-permeability materials can be achieved under low-flow and low-pressure conditions.

### **16.5 Amount of Carbon Injected**

The amount of carbon to be installed at a particular site will depend on the remedial goals established for that site. Figure 16.4 is an idealized model intended to help the reader understand the amount of carbon that needs to be injected to address a mass of contamination. Think of a mixing tank containing some volume (*V*) of water with of water with a dissolved contaminant at an initial concentration (Co). Adding and mixing a mass of AC (*W*) results in an equilibrium dissolved concentration (Ce) with a certain mass of contaminant adsorbed to the carbon. The concentration of contaminant sorbed in the AC is denoted Sc. In this system, a simple mass balance can be written as follows (Eq. 16.1):

$$\text{CoV} = \text{CeV} + \text{WSc} \tag{16.1}$$

The left side of Eq. (16.1) is the mass of the contaminant in the vessel (CoV). The first term on the right is the mass of contaminant dissolved in water at equilibrium (CeV). The last term is the mass of contaminant adsorbed in carbon (WSc). Therefore, increasing the amount of carbon increases the contaminant adsorbed to the carbon and decreases the contaminant in the aqueous phase at equilibrium.

In order for the above mass balance to be useful, a relationship (isotherm) between dissolved phase equilibrium concentration and the concentration of contaminant adsorbed in the AC is needed. A variety of different isotherm equations have been proposed, some have a theoretical foundation, and some are empirical in nature. Commonly, the Langmuir or Freundlich isotherms are used to model adsorption behavior. The Freundlich isotherm is often applied to adsorption of organic compounds onto AC and is written as follows (Eq. 16.2):

$$\mathbf{Sc} = k \mathbf{C} \mathbf{e}^{1/n} \tag{16.2}$$

A plot of Ce versus Sc in log–log space yields a linear-plot whose slope is (1/*n*) and intercept is (*k*). Substituting into the mass balance for Sc yields the following equation (Eq. 16.3):

$$\text{CoV} = \text{CeV} + \text{WkCe}^{1/n}.\tag{16.3}$$

**Fig. 16.4** Idealized model to understand how much carbon may need to be injected consisting of a mixing tank containing some volume (*V*) of water with a dissolved contaminant at an initial concentration (Co) where some mass of AC (*W*) is added and mixed

For example, if we have 10 ppm toluene in groundwater and our target is to reduce the concentration to 0.5 ppm, we can look up the Freundlich parameters (Dobbs 1980), *k* = 26.1 and 1/*n* = 0.44. Alternatively, isotherms are often available from product vendors. Assuming an average porosity of 30%, there will be 300 L of groundwater per cubic meter of saturated formation. The only unknown is *W*, which can be calculated as in the equation below (Eq. 16.4):

*W* = 300(10 − 0.5)/26.1(0.5) 0.44 = 148 g AC per cubic meter of saturated formation. (16.4)

This model is simplistic and does not reflect the complexities found in the subsurface. It is impossible to obtain complete mixing, and, usually, there are multiple contaminants competing for adsorption to AC. In the field, PHC impacted sites may be some of the most complex in that contamination consists of a multitude of adsorbates that interact very differently with AC that could require the development of isotherms specific to the PHC products found on a site. A simplification arises when concentration of absorbable species is low. In this case, each adsorbate equilibrium will be unaffected by the presence of other components. This presumption would likely apply at locations in the plume where only dissolved phase exists but not within or near an LNAPL source zone.

Typically, the practical experience of CBI vendors and installers suggests application of an engineering factor of safety (EFS) to estimated mass dosing from isotherm calculations. While efforts have been made to develop EFS systematically, the factors remain a matter of experience and commonly vary from two to tenfold the mass dose estimated based on isotherms. When very small amounts of AC are apparently needed, this is when the highest EFS are indicated. A more extensive AC loading increases the probability of contact between AC and a contaminant when the contaminant is present in low concentration.

The previous discussion regarding calculation of injection mass dosing applies to plume-wide treatment. With permeable reactive barriers (PRBs), one needs to consider the contaminant mass flux across and through the barrier, the design life of the barrier, and contaminant degradation during transition through the barrier. PRBs constructed with CBI can be thought of as a conventional carbon bed. In a conventional carbon bed, contaminant mass is adsorbed by the carbon as it enters the bed. Adsorption continues until the adsorption capacity of the carbon is approached, and the contaminant then travels a bit further into the bed. As this process continues, a narrow zone forms that slowly moves through the bed. This zone is called the mass transfer zone. The barrier design is based on the amount of carbon adsorption needed to meet the contaminant concentration goals on the downgradient side of the barrier for a defined time. Many references, e.g., (Thomas 1998; Ruthven 1984), describe how flow-through AC beds are designed. In a CBI PRB, biodegradation can contribute to increase the PRB operational lifespan. Biodegradation effects could also be accounted for during the design of the PRB to optimize the amount of carbon needed.

# **16.6 General Response to Installation of AC into a Contaminated Aquifer**

After AC-based injectate for passive in-situ remediation of soil and groundwater was introduced to the industry (Noland 2002), initial expectations of some practitioners were that dissolved phase contaminants would simply disappear due to adsorption, and contaminated sites would clean up overnight. This expectation has been tempered over time as the complexities of subsurface environments and inefficiencies associated with CBI installation have come to be appreciated.

Figure 16.5 depicts an idealized general response in terms of contaminant concentration in groundwater after CBI installation. Dissolved contaminants are rapidly adsorbed, and the transient concentration reduction is often better than predicted based on design. This is because an equilibrium initially existed between the dissolved and sorbed mass of contaminant. The injection of AC disrupts this equilibrium as the rate of adsorption of contaminant from groundwater outpaces the rate of contaminant desorption from the formation. As contaminant desorbs from the formation into groundwater, adsorption continues, and the interplay between adsorption capacity of the AC and the contaminant mass flux from the formation results in rising concentrations in the aqueous phase until a new equilibrium is established (Ci on Fig. 16.5).

The magnitude of Ci and the time required for a new equilibrium to be established is a function of the contaminant mass initially present in the aquifer, the distribution of AC within the formation, and the stratigraphy. If the target treatment zone resides in a high-permeability environment, the time to achieve a new equilibrium will be short, often within a few weeks. If the aquifer sediments consist of silt or clay, the time required may span months. What is often incorrectly called "rebound" is a predictable partitioning of contaminant mass between the groundwater, the aquifer solids, and the AC as the system approaches a new equilibrium.

Presuming no significant contaminant mass is present that would cause overloading of the carbon and assuming robust biological activity is operating to degrade contaminants, a general decline from Ci should be observed. Given a general decline, a semilog plot of concentration over time should result in a straight line with a negative slope that will be related to the rate of contaminant degradation.

### *16.6.1 Effects Related to Contaminant Mass*

The most important variable affecting the response to injection of AC into the aquifer is the amount of contaminant mass present. At locations far removed from a source area and where the contaminant mass sorbed to the formation is insignificant, observed post-injection concentration perturbances are slight if they occur.

In contrast, the other extreme is represented by significant non-aqueous phase liquid (NAPL) hydrocarbon in the formation. This condition is common in source areas, and it is unlikely that enough CBI can be injected to absorb the NAPL altogether. After injection of CBI, experience has shown that a short-term drop in dissolved phase concentration is often observed; however, recontamination of groundwater due to continued contact with NAPL may overcome the adsorption capacity of the CBI, and a return to original conditions is predictable.

The above-described static state should not be interpreted as a deterrent to using CBI to treat NAPL. Instead of employing a design based simply on adsorption capacity, the design should be based on the surface area of the injected AC. NAPL mass removal rates are a function of the total amount of AC injected due to the available surface area of the AC that supports degradation. Regardless of adsorption, more surface area correlates to increased biodegradation. In this manner, AC is like a catalyst. This allows one to predict the time required to eliminate NAPL and see the subsequent decline in groundwater concentration. This is very similar to measurement of carbon dioxide generation at NAPL sites to estimate the average mass removal rate and then use that rate to predict performance over time.

### *16.6.2 Competitive Desorption*

There is a widely held belief within the environmental industry that organic compounds, once adsorbed to AC, are stabilized for, perhaps, centuries. An article published in 2015 stated: "organic compounds are sorbed to AC so strongly that it is almost certain that the contamination will be stable and unavailable for leaching for at least 50–100 years" (Fox 2015). The reality is that the adsorption of organic compounds is a reversible process controlled by the heat (enthalpy) of adsorption (Kolasinski 2019). This feature has significant consequences.

AC will efficiently remove contaminants from groundwater and soil until the adsorption capacity is reached. Refined petroleum, whether gasoline, diesel, or other fuels, contains many different organic compounds. AC interacts with all these compounds differently as heats of adsorption are specific to each compound, and there will be competition for available absorption sites on the AC. In addition, naturally occurring organic matter (NOM) also must be considered. Some dissolved organic compounds (NOM) are present in all groundwater, and they typically have high heats of adsorption and will also compete for adsorption sites.

When AC is first injected, adsorption sites within the carbon are unoccupied, and there is a vast space for compounds to bind. It does not matter if some compounds bind more energetically to AC than others; initially, there is room. As the space begins to fill up, those compounds with higher heats of adsorption tend to displace those having lower heats of adsorption. Now, those compounds with low heats of adsorption begin to desorb from the carbon into solution, while other compounds with high heats of adsorption continue to be adsorbed. This process is called "rollover" within the AC industry, i.e., displacement of compounds having lower heats of adsorption.

Important examples of this are benzene and 1,2-dichloroethane. It is common for benzene concentration to be a site risk driver. Benzene has a relatively low heat of adsorption and can be displaced from AC by any number of common contaminants, including xylene, naphthalene, and NOM. To mitigate bleeding of contaminants from the AC, robust degradation mechanisms or pathways must be present to effectively remove and degrade adsorbed compounds from the carbon. Ideally, the rate at which compounds are degraded should effectively maintain unoccupied adsorption sites on the AC. This facilitates continuing adsorption of new contaminants without displacing those already bound to the AC. The most common means for accomplishing this with PHCs is biological degradation. The AC is biologically regenerated as bound contaminants are degraded. This mechanism will be discussed in the next section.

The proposed degradation mechanisms should be carefully evaluated whenever CBI technology is considered. Any application of CBI to contaminants where no viable degradation mechanism is known should be conscientiously weighted because the installed CBI may become a new source in the future. The clearest example of this is the use of AC at sites impacted by per and polyfluoroalkyl substances (PFAS), known as "forever chemicals" in the vernacular.  Biodegradation is the preferred mechanism for most CBIs on the market intended for remediation of PHCs.

### *16.6.3 Adsorption by Activated Carbon*

Literally every municipality in the USA uses beds of activated carbon to polish drinking water before it is released for consumption. Because of its widespread use and the number of large manufacturers competing to make effective carbons, there is a sense within the environmental industry that all carbons are the same (Fan 2017). As detailed in previous sections, activated carbons are manufactured from a wide variety of raw materials, and they have very different properties. The example in Sect. 16.5 used design parameters associated with F400 high grade carbon derived from bituminous coal manufactured by Calgon Carbon. One of the more important characteristics of AC is its adsorption capacity. This is a measure of the maximum mass of contaminant the carbon is capable of adsorbing at any given equilibrium concentration. The adsorption capacity is strongly dependent on contaminant concentration, the type of carbon (its source and activation method), and finally on the specific contaminant. There are organic compounds that are not adsorbed by AC at all. When dealing with multi-component products present at PHC sites, the adsorption dynamic becomes very complex, particularly in view of competitive desorption. Rules of thumb like AC can absorb roughly 15–20% by weight of organic contaminants are very misleading and should be discarded. PHC specific and AC product specific data should be relied upon.

One of the great advantages of AC is the ability to regenerate or reactivate spent carbons. Housekeeping on food-grade carbons is very good because when reactivated, the original customer will repurchase it. The same cannot be said for spent non-food-grade carbons. These carbons may originate from a multitude of sources ranging from wastewater to metal plating to manufacturing facilities. The reactivation process removes any organic contamination that may have been present, but inorganic contaminants will persist. Reactivated carbons that are not derived from food or drinking water applications should not be used for in-situ remediation because they may contain metal contaminants that could impact groundwater quality.

### **16.7 Microbial Growth on Carbon**

### *16.7.1 Carbon Structure Providing Living Space*

Natural organic matter, nutrient concentrations, and species richness decrease with depth, with notable increases in the transition zones such as the capillary fringe (Smith 2018). The typical decline in microbial abundance that accompanies increasing subsurface depth, at least in part, arises from decreasing transport pathways, the accompanying reduction in the influx of fresh organic carbon, and other required chemicals, such as electron donors or acceptors (Santamarina 2006). The size and shape of the cavities within the media and interstitial space between media grains restrain subsurface organisms' size, form, and mobility (Luckner 1991), and thus the population density. Population density is linked to the cavities and interstitial spaces, which are essential to the supply of nutrients, electron donors and acceptors, carbon sources, and the elimination of microbial metabolic wastes (Fredrickson 2001).

Essential requirements for subsurface microbial life include moisture, chemical factors such as electron donors, and media factors such as granularity. Since some of these elements are linked to the transport of water in the subsurface, microbial populations often reflect groundwater flow pathways (Maamar 2015; Graham 2017; Danczak 2018). The installation of AC into the subsurface by high-pressure, highflow injection may establish new habitat by creating and expanding flow paths beyond that which existed in the native subsurface material (Lhotský 2021; CLU-IN U 2022). The AC may accentuate water movement (Siegrist 1998; Sorenson 2019; ITRC 2020) due to the addition of three-dimensional networks of carbon seams and surfaces (Murdoch 1995; Bradner 2005; Or 2007). These seams can increase connectivity in the subsurface media as AC acts as a proppant holding the fractures open. The propped fractures can accommodate the introduction of additional microbes and microbial resources such as water and electron acceptors (Mangrich 2015; Zamulina 2020). Relatedly, AC's granular nature can also increase transport in the subsurface due to the formation of thick liquid films on the rough surfaces of AC (Or 2007; Massol-Deyá 1995). Together the AC seams and surfaces should support contaminant dispersion, movement of nutrients and terminal electron acceptors, the interchange of microbial metabolic products (Bures 2004), and microbial dispersal and colonization on newly accessible soil and aquifer compartments (Krüger 2019).

The surface of AC may provide recesses offering shelter for microbes, while its adsorptive properties enrich the environment of the AC with substrate (Weber et al. 1978). Microbial cells that inhabit the macropores and surface recesses are better sheltered against stressful environmental conditions, such as predation, than microbes in the native media (Kindzierski 1992; Zur ˙ 2016). Due to the surface recesses, more habitat exists for microbial growth (Liang et al. 2009). Concerning adsorptive capacity, by comparison to AC, organic resources in the subsurface are scattered within structurally heterogeneous media. AC consolidated resources result in higher substrate biodegradation and higher specific growth rates than non-adsorbing or weakly adsorbing media such as sand (Li 1983). Thus, AC consolidates organic resources (Weber et al. 1978) on a homogenous media, and microbes are drawn to the enriched environment of the AC (Liang 2007), which facilitates contact between degraders and contaminants (Chen 2012; Thies 2012; Bonaglia 2020).

It has long been observed that bacteria attach to and multiply upon the surface and in the macropores of AC (Board 1989; Morsen 1987; Liang 2008), and that biodegradation occurs on AC in the subsurface. (Peacock 2004) compared in-well coupons consisting of either glass wool or Bio-Sep® beads (75% PAC and 25% Nomex®) to investigate the in-situ bioreduction of technetium and uranium in a nitrate-contaminated aquifer. It was found that the beads supported microbes that reduced technetium and uranium, and the beads supported tenfold more microbial biomass than glass wool. It was stated that higher microbial biomass was most likely to exist due to the higher surface area of the beads and the concentration of nutrients on the beads as compared to the glass wool. Other studies have concluded that benzene, toluene, and phenol are effectively extracted from Bio-Sep® beads (Williams et al. 2013), metabolized by the microbes, and incorporated into fatty acids produced by microbes (Geyer 2005). Similar results were obtained for in-situ biodegradation of naphthalene at the McCormick and Baxter Superfund Site in Stockton, California (McCormick 2010). Nineteen well locations were tested, and all were positive for 13C enrichment in the microbial lipid biomass.

PAC amended sediments in a microcosm study also demonstrated that bacteria attach to AC and multiply on the surface and within the macropores (Pagnozzi 2020). AC stimulated naphthalene biodegradation and mineralization under anaerobic conditions, as shown by the production of 14CO2 from radiolabeled naphthalene (Pagnozzi 2021). The relative abundance of Geobacter, Thiobacillus, Sulfuricurvum, and methanogenic archaea, known to be associated with naphthalene degradation, increased after amendment with PAC. Under aerobic conditions, sediments containing 16 PAHs amended with PAC increased microbial community diversity, structure, and activity relative to both sand and clay controls. The alteration in the microbial community in response to PAC is consistent with that reported by Bonaglia (2020), who also examined and reported an increase in the biodegradation of high molecular weight PAHs for microbes associated with AC as did Acosta (Zapata Acosta 2019).

Furthermore, AC sustains microbial activity by decreasing toxic concentrations in the dissolved phase (Leglize 2008) and buffering the microbes against toxic shock (Sublette 1982). Specifically, the adsorption of PHCs onto AC significantly reduces their toxicity to microbes (Morsen 1990). The toxicity of petroleum compounds is reduced, as indicated by a sharp increase in the abundance of both native and inoculated petroleum degraders (Semenyuk 2014). Furthermore, AC added to sediments decreased toxicity as measured by ecological parameters (Kupryianchyk 2013). Likewise, adequately designed and deployed CBI should reduce contaminant toxicity to microbes.

### *16.7.2 AC Priming*

Contaminant degradation is a function of the density of microbial microaggregates (Gonod 2006; Becker 2006). As discussed, AC installed in the subsurface is new habitat for microbial exploitation. Therefore, it may be advantageous to give hydrocarbon-degrading and degradation supporting microbes an opportunity to adhere to the AC before subsurface installation. This process is referred to as priming. Priming allows the AC to function as both a microbial inoculum delivery system and a readymade microbial habitat (Piai 2020). Similar approaches have proven successful in a variety of applications (Das 2017) and can result in more robust microbes (Meynet 2012) and microbial degradation that is sustained (Marchal 2013; Bonaglia 2020).

Priming stimulates formation of biofilms (term typically used to identify clusters of bacteria or archaea in a matrix of extracellular polymeric substances—EPS). Microorganisms embedded within biofilm achieve high resistance to toxic pollutants (Köhler 2006) and are durable, so biodegradation is persistent (Akta¸s 2007; Ran 2018; Edwards 2013). In the subsurface, microbial-inoculated AC can establish a biofilm that promotes growth and diversity, degrades hydrocarbons and other contaminants, regenerates the AC, and persists over time (Lhotský 2021; Cho 2012). Figure 16.6 shows a case of microbial growth on AC in laboratory samples.

### *16.7.3 AC Adsorption as Barrier to Biodegradation*

Studies have also shown that AC may have an inhibitory effect on microbial growth by decreasing the dissolved concentration of adsorbate available for microbial degradation. Marchal (2013) demonstrated for phenanthrene sorbed to soils that the stronger the adsorbent (AC, biochar, compost), the lower desorbed concentration, and the lower the microbial degradation (Marchal 2013). These results are not unique (Rhodes 2010), but they may be specific to adsorbates having high heats of adsorption like phenanthrene. Three-ring PAH, as is phenanthrene, degradation has been reported to be inhibited by AC, but 4 and 5-ring PAHs degradation is supported by AC (García-Delgado 2019). Aromatic compounds, having lower heats of adsorption, are not affected by AC to the degree reported for phenanthrene. Mangse (2020) found that AC carbon made toluene less bioavailable, but AC did not have a detrimental effect on toluene degradation (Mangse 2020). These observations are reasonable in that adsorbates that enter the AC are protected to some degree from biodegradation. The extent of that protection is influenced by the strength of the interaction with the AC. In the end, these observations follow from the adsorptive character of AC and they have not been demonstrated to be relevant to field application of AC, where multiple chemical adsorbates and NOM are present.

# **16.8 Evidence of Biodegradation at Sites with AC**

### *16.8.1 Microbial Communities*

Microbial attachment to AC is associated with an increase in the abundance of both native and inoculated petroleum degraders (Semenyuk 2014; Bonaglia 2020; Pagnozzi 2021). While the data to separate the function of AC from the CBI does not exist, an increase in microbial abundance is also associated with CBI emplacement. Figure 16.7 presents microbial data from groundwater samples collected from a CBI emplacement site 3 months post-emplacement. According to next-generation sequencing (NGS), the CBI emplaced sample has more total species, more individuals per species, and more rare species than either the control or the PHC impacted control. Figure 16.8 provides a stark visual confirmation of the abundance data presented in Fig. 16.7. More information on NGS can be found in Chap. 10.

A more definite question is whether the microbial populations in the CBI emplacement contain the individual microbes that are pertinent to PHC degradation. Figure 16.9 presents microbial data from groundwater collected from a CBI emplacement site 2 years post-emplacement. The microbial samples were examined

**Fig. 16.7** Groundwater samples for both CBI treatment and controls analyzed by next-generation sequencing. Compared to controls, the CBI emplaced samples had more total species, more individuals per species, and more rare species. The Shannon–Weaver Index is the natural log of the total number of species in a sample. It reflects the total number of species present. The Simpson Diversity Index is the square of each species' abundance summed for the total sample population subtracted from 1; thus, proportional abundance is weighted more on the uniformity of individuals in the population, which is referred to as evenness. It indicates species dominance and reflects the probability of two individuals that belong to the same species being randomly selected. The scale is zero to 5, with 5 meaning that each species in a dataset is represented by the same number of individuals. The Chao1 abundance index is the number of individuals present and single or double individuals. It emphasizes individuals microbes present in small numbers

**Fig. 16.8** Heat map presenting NGS microbial data at the species level by aligning taxa based on similarity. The three samples were from the same release site: the control sample was unimpacted by PHCs. The impacted control was impacted by PHCs but not treated with CBI. The CBI sample was drawn from an area in which CBI was emplaced. The colored scale indicated relative abundance taking into consideration genome size and number of reads

**Fig. 16.9** NGS data displaying the most abundant twelve strains in samples within the CBI (BOS 200®) area. The colors in the columns and scale bar are relative intensity. The "control" sample was taken from the footprint of the injection but was intermittently impacted by petroleum. Due to site boundary constraints, an unimpacted control was not available. In the heavy impact sample, Smithella, Desulfobulbus, and Methanothrix are 48-, 36-, and 132-fold greater in absolute abundance than the low-impacted control

by NGS. Unambiguous control samples were not available. One side-gradient monitoring well, which had a history of intermittent PHC impacts, was selected as the best "control" available. Two consistently impacted monitoring wells were selected for comparison. All three monitoring wells were within the footprint of the in-situ injection of CBI. The samples were collected 2 years post-injection with a CBI containing nitrate and sulfate as electron acceptors. Added nitrates are short-lived being depleted in 1–3 months. The sulfate levels at the time of sampling ranged from 110 to 483 mg/ L. The species that increased relative to the control monitoring well were catabolically and thermodynamically linked syntrophic communities. Figure 16.9 presents the most abundant species relative to the selected control. The colored scale indicated relative abundance taking into consideration genome size and number of reads. The most abundant microbes, Smithella, is a known alkane-degrading microbe (Ji 2020; Embree 2015). Under sulfate- and nitrate-reducing conditions, the primary condition at the site, Smithella degrades alkanes to acetate with the production of H2 (Gieg 2014). *Desulfobulbus* sp. Tol-SR is distinguished by suites of genes (BSS, BBS, and BAM) associated with anaerobic toluene metabolism, which leads to acetate production (Laban 2015). Methanothrix utilizes acetate as a substrate for methanogenesis, while *Candidatus sulfuricurvum* derives energy from H2 oxidation (Handley 2014). The connection between Smithella, Desulfobulbus, and H2-utilizing methanogens is a typical hydrocarbon-degrading syntrophic relationship.

The difficulty with data presented in Fig. 16.9 is the lack of a control not impacted by PHCs. That does not prevent the observation that the microbes present were consistent with a petroleum degradation syntrophy. It does, however, make it difficult to determine how the CBI injection changed the population apart from the observation that the groundwater sample collected from the intermittently impacted monitoring well was less abundant than the samples drawn from the consistently impacted monitoring wells and that the microbial population present was capable of degrading PHCs.

The site data presented in Fig. 16.10 had appropriate control samples. The control samples were from areas that were not treated with CBI. Control 1 was drawn from an area that was unimpacted by PHC. Control 2 was drawn from an area impacted by PHC. Control 3 was drawn from an impacted area that bordered the CBI emplacement. It should be viewed as potentially influenced by the CBI. The three CBI emplacement groundwater samples were drawn from monitoring wells within the CBI treatment area. The samples were analyzed by NGS.

The microbial populations presented in Figs. 16.9 and 16.10 shared some characteristics. Smithella was the most consistently abundant microbe, but it was identified in all the impacted samples without regard to CBI emplacement status. Desulfobulbus was consistently present across the CBI emplaced samples. Methanothrix soehngenii was present but not confined to the CBI emplacement samples. *Candidatus sulfuricurvum* was not present. Two microbes absent from the Fig. 16.9 site were present at the Fig. 16.10 site. *Geobacter metallireducens* was present in the CBI samples and control 3, which was about 3 m from the CBI emplacement. *Geothrix fermentans*  was present in the same pattern. *Geobacter metallireducens* uses electron donors such as acetate and uses Fe(III) as an electron acceptor (Coates 1999). *Geobacter*

**Fig. 16.10** NGS data from monitoring wells. The data is displayed at the species level. Multiple controls are present. Control 1 was drawn from an area that was unimpacted by PHC. Control 2 was drawn from an impacted area. Control 3 was drawn from an impacted area that bordered the CBI emplacement and should be viewed as potentially influenced by the CBI. The three CBI samples (CBI 1–3) were drawn from monitoring wells within the CBI emplacement. Smithella and Desulfobulbus were elevated relative to control. Methanothrix, however, did not have a consistent pattern. In this dataset, Geothrix and Geobacter were elevated

*metallireducens* couples the oxidation of aromatic compounds to the reduction of Fe(III)-oxides (Butler 2007), but acetate and ethanol are preferred over benzoate, although, benzoate is co-consumed with toluene and butyrate (Marozava 2014). These microbes are known to participate in direct interspecies electron transfer (DIET) using the AC. And the presence of these microbes may establish a second syntropic pathway to oxidize acetate and reduce CO2 (Rotaru 2018). Both sites had a microbial population capable of degrading PHCs.

While describing the microbes present in the monitoring wells 2–3 years' postinjection and demonstrating that they were consistent with a hydrocarbon-degrading syntrophy is a good qualitative indicator of biodegradation in response to a CBI, the presence of genes encoding hydrocarbon enzymes in a microbial population can also be used to characterize biodegradation potential. Figure 16.11 presents qPCR data (QuantArray®-Petro from Microbial Insights, Inc.). The array quantifies a select suite of 25 functional genes involved in aerobic and anaerobic biodegradation of PHCs. Samples were collected from unimpacted monitoring wells and from impacted monitoring wells prior to injection with CBI (BOS 200®). Of the 25 functional genes, five of them were not present in any of the samples. Of the remaining 21, eight function genes were present in the unimpacted background, six additional ones were expressed in the impacted background post-injection, and post-inject seven additional function genes were observed. No identified function genes were lost by the first-month post-injection. Two previously identified functional genes were not identified at month five. At month five, 19 of the 21 quantified function genes were observed and on average the microbial count using the functional genes as markers increased 56-fold in cells/ml (range from 3.4 to 178.5-fold cells/ml). The average and range did not include three functional genes that increased from a low estimated value to 106-fold the estimated value. The gene array demonstrated that, for the period examined, the petroleum-degrading population expanded post-injection of CBI relative to both impacted and unimpacted controls. More information on qPCR can be found in Chap. 10.

Examining a subset of the data from Fig. 16.11, benzylsuccinate synthase (BSS in the QuantArray®) catalyzes the addition of fumarate to toluene (Biegert 1996) and potentially the monoaromatics xylene and ethylbenzene (Kharey 2020) to generate benzylsuccinate, the first step in anaerobic toluene metabolism (Biegert 1996). Benzylsuccinate becomes benzoyl-CoA and succinyl-CoA. Succinyl-CoA is recycled back to fumarate, while benzoyl-CoA proceeds through benzoyl-CoA reductase (BCR in the QuantArray®) to acetate, a small fatty acid easily used by many microbes. This is the toluene anaerobic pathway previously described above for site presented in Fig. 16.9. Thus, a selected subset of the gene expression from the Fig. 16.11 site provides a similar picture in Fig. 16.9 and has overlap to the site presented in Fig. 16.10. Other enzymes coding genes, associated with both aerobic and anaerobic pathways, are increased in number post-CBI emplacement, and the total microbial count in the impacted area also increases post-injection (Fig. 16.11) Notice that the total eubacteria, sulfate-reducing bacteria, iron-reducing bacteria, and denitrifying bacteria are increased more than tenfold. Thus, the release site presented in Fig. 16.11 confirms that CBI emplacement increases microbial abundance, increases microbes

**Fig. 16.11** QuantArray®-Petro data from a site where CBI was injected. Microbes having selected genes associated with both aerobic and anaerobic petroleum hydrocarbon metabolism increased in number relative to background. The increase was higher at 5 months post-emplacement than at 1 month

having genes known to be important in PHC degradation, and confirms the data conclusions drawn from Figs. 16.8, 16.9, and 16.10.

### *16.8.2 Additional Lines of Evidence of Microbial Activity*

#### **<IP_ADDRESS> Decrease in Electron Acceptors and Increase in Gases**

For CBI that provide electron acceptors, decreasing concentration in potential electron acceptors (such as oxygen, nitrate, and sulfate) is indicative of microbial activity. Figure 16.12 is from a petroleum release site. The decrease in electron acceptors followed an often-observed pattern. Contaminant concentration significantly decreased upon installation of CBI. Early post-emplacement, the system reaches equilibrium. When electron acceptors such as sulfate and nitrate were added with

**Fig. 16.12** Electron acceptors at a site where CBI was installed. Nitrate concentrations dropped first followed by a slight rise in nitrite. Sulfate concentrations persisted longer. Benzene concentrations were added to the graph for comparison

the AC, nitrate concentrations decreased quickly, leading to a slight, transient elevation of nitrite. Sulfate dropped slowly over time. The nitrate and sulfate consumption patterns were consistent with anaerobic metabolism using nitrate and sulfate as electron acceptors.

Increasing respiration products (such as CO2, Fe(II), sulfide, and CH4) is also consistent with microbial activity. Figure 16.13 demonstrates the rising concentration of methane (associated to anaerobic biodegradation processes as explained in Chap. 5) and other gases at a site where CBI was installed.

#### **<IP_ADDRESS> Decreasing Concentrations in Soil and Groundwater**

The most often presented data to demonstrate decreases in contaminant concentrations are monitoring well data. Figure 16.5 depicted a typical groundwater concentration pattern after subsurface CBI emplacement. Figure 16.14 presents groundwater data for benzene and TVPH post in-situ emplacement of CBI. Contaminant concentrations in groundwater decrease due to PHCs adsorption onto the AC. The most water-soluble constituents can move into the AC quickly, but those same constituents may have lower heats of adsorption than constituents presenting a lower solubility, so there is a period over which a new equilibrium is established. The total amount of contamination relative to the AC determines the time it will take to reach equilibrium. From that point forward biodegradation controls the final phase of site remediation.

Core samples collected periodically at adjacent locations from sites treated with CBI typically show a decrease in contaminant concentration in soil, often exhibiting consistent patterns despite the high variability commonly associated to physical sampling of soil and aquifer materials. Figure 16.15a present ethylbenzene data

**Fig. 16.13** Prior to injection, which was completed in June of 2015, gases, except for methane, were not detected above detection limits. Three months post-CBI emplacement, the concentration of gases linked to microbial metabolism like methane, *n*-Butane, and iso-Butane rose. Gases such as cis-2-Butene, propane, ethane, *t*-2-Butene, 1-Butene, and iso-Butylene (not displayed) exhibited similar patterns

**Fig. 16.14** The red arrow marks the injection of the CBI BOS 200®. The initial decrease in TVPH concentrations was followed by a re-equilibration phase. Biodegradation may have initiated early post-CBI emplacement but did not seem to dominate the PHC concentration pattern until the final stages of site remediation

#### **c)**

**Fig. 16.15** Ethylbenzene concentration from soil samples was significantly lower in 2020 than in 2016. All other PHCs analyzed were also significantly lower in the 2020 soil samples than in the 2016 soil samples

from a BOS 200® in-situ injection into fat clay geology at an LNAPL site. The raw statistics for ethylbenzene shown in Fig. 16.15b are illustrative of the data for all other contaminants examined from the same site. All constituents examined were significantly decreased in concentration in the aquifer solids tested at 40 months (see Fig. 16.15c).

#### **<IP_ADDRESS> Compound-Specific Isotope Analysis (CSIA)**

As discussed in Chap. 11, CSIA tracks the stable isotope composition of selected volatile organic compounds (VOCs) to obtain information on attenuation processes. In tracking the degradation of benzene due to biological degradation, benzene should become increasingly enriched for 13C isotopes as the benzene containing 12C isotopes preferentially has its carbon bond broken.

The international standard for the ratio of 13C–12C isotopes is the Pee Dee Belemite (PDB) standard and is 0.01123720 (IAEA 1993). So about 1% is 13C and 99% is 12C. To determine if biodegradation is indicated, one can measure the increase in 13C above the PDB standard. It has become convention to present the change (δ) in 13C, (δ13C) as the isotopic ratio of the sample (Rs) divided by the isotopic ratio of the standard (Rstd) (Meckenstock 1999; Hayes 2004). Next, because the fraction is small the value is multiplied by 1000 to shift the decimal place and present the data as part per thousand often reported as permill (‰). Thus, for isotopic data presented as the δ13C, as δ13C moves closer to zero the value becomes less negative because the molecules are enriched for δ13C. See Eq. 16.4 from (Mackenstock 2004).

$$\text{\ $1\$ C(\%\_\text{\(\%)} = (\text{Rs}/\text{Rstd} - 1) \times 1000)}$$

$$(\text{Rs}/\text{Rs}) = [(1\text{\\$C}/12\text{C})\text{s} - (1\text{\\$C}/12\text{C})\text{std}]/(1\text{\\$C}/12\text{C})\text{std} \tag{16.5}$$

In Fig. 16.16, δ13C enrichment data are presented form a CBI injected site. To determine if benzene sorbed to the formation was being degraded, impacted but untreated core samples were collected in a benzene impacted area upgradient of the CBI emplacement and within the CBI emplacement area. These samples were examined for isotopic carbon enrichment under the assumption that the upgradient and downgradient samples were immediately connected by a shared flow path. The isotopic fractionation from the extracted mass indicated enrichment for δ13C in the downgradient, within the injection area, smear zone samples. The difference between three of the four points exceeds δ13C ≥ 2‰. The data were indicative of biodegradation.

To compare δ13C enrichment prior to interaction with the CBI emplacement versus the isotopic enrichment post interaction with the CBI emplacement, a stable isotope enrichment factor, epsilon (ε), may be calculated (see Eq. 16.7). The calculated epsilon for the data presented in Fig. 16.16 is (ε) = −3.5. This value is consistent with the stable isotope enrichment factors reported for a sulfate-reducing culture,

**Fig. 16.16** Core samples were collected from a CBI emplacement on a petroleum release site. Points below the red line were upgradient of the CBI emplacement. Points above the red line were samples collected within the CBI emplacement. The data are indicative of biodegradation

which ranges from -2.7 to -3.6 (Mancini 2003). The CBI used, BOS 200®, contains significant sulfate; thus, the ε value is consistent with expectations and indicated biodegradation.

$$\varepsilon = \left\{ \left[ \frac{(1000 + \ $13 \text{Cprior})}{1000 + \$ 13 \text{Cpost}} \right] - 1 \right\} \times 1000 \tag{16.7}$$

To be thorough, neither abiotic nor non-degradative processes are known to result in significant stable isotopic fractionation of benzene relative to biological processes. Non-degradative processes such as sorption, volatilization, and dissolution are typically smaller than the analytical uncertainty associated with CSIA, which has been reported as ± 0.5‰ (Hunkeler 2008). Specifically, the sorption of benzene to activated carbon does not result in stable isotope fraction greater than this ± 0.5‰ accuracy (Slater 2000; Schüth 2003). Abiotic degradation processes associated with benzene can be similarly disregarded. Plotting the carbon stable isotope concentration δ13C against the natural logarithm of the concentration can be used to support that a single process is the main control behind the change in contaminant concentration (Hunkeler 2008).

Data from another site where CSIA was conducted are presented in Fig. 16.17, which depicts a sustained reduction of BTEX contamination downgradient of a CBI PRB despite BTEX continuing to enter the barrier from the upgradient side. The CBI PBR was installed because an existing groundwater extraction and reinjection system was failing to perform. CSIA was conducted to determine whether the decrease in concentration was due only to adsorption to the AC or if the decrease was also affected by biodegradation. Groundwater samples were collected from monitoring wells located on a transect through the CBI PBR. The monitoring well upgradient of the CBI PBR was impacted by the same PHCs as the monitoring wells downgradient of the CBI PBR. The groundwater extraction and re-injection system remained active after the CBI PBR was installed. It withdrew groundwater from a series of wells located ~55 m downgradient from the control monitoring well and re-injects oxygenated water at ~18 m upgradient from the same control well.

Figure 16.18 shows δ13C enrichment data. Of the five VOCs examined by CSIA, three (*n*-propylbenzene, ethylbenzene, m,p-xylene) were enriched, greater than analytical uncertainty, relative to the upgradient monitoring well. O-xylene did not demonstrate enrichment, and 1,2,4-trimethylbenzene enrichment was within laboratory error (data not shown). Determining the cause of the observed isotopic enrichment was complicated by the presence of the extraction and re-injection wells because re-injection may enhance degradation by oxygenating the re-injected groundwater. Nevertheless, enrichment is indicated.

**Fig. 16.17** Groundwater sampling data from upgradient and downgradient of emplaced PRB. BTEX on the downgradient side of the barrier remains suppressed despite BTEX continuing to enter the barrier from the upgradient side

**Fig. 16.18** Samples of groundwater collected upgradient of a PRB and at multiple distances downgradient. *N*-propylbenzene, ethylbenzene, and *m*, *p*-xylene showed 13C enrichment. The results indicated the occurrence of biodegradation by having an enrichment of δ13C ≥ 1‰

### **16.9 Biological Regeneration**

As a general process, biological regeneration (BR) of AC is well documented (Akta¸s 2007; El Gamal 2018). Adsorption, biofilm formation, and biodegradation can occur simultaneously on AC (Piai 2020; Betsholtz 2021), with the combination of microorganisms and the adsorptive capacity of AC being synergistic in promoting BR (Nath 2011a, b). Upon emplacement in the subsurface, AC in CBIs adsorbs hydrophobic chemicals such as PHCs and natural organics. Microbes in the resource-rich regions near the AC bloom. As the biomass on the AC expands, desorption (Leglize 2008; Ying 2015) and regeneration rates increase (Betsholtz 2021) such that the adsorptive capacity of AC is extended (Chan 2018; Piai 2022). In time, some of the products of degradation as well as natural organics fill a percentage of the AC micropores, but they have not been demonstrated to either completely fill the micropores nor cause biodegradation to cease (Smolin 2020; Díaz de León 2021). This general scenario has been developed from in vitro experiments and the examination AC used in water treatment (FRTR 2021; El Gamal 2018). The development of microbial growth on AC is referred to as biologically activated carbon (BAC). The development of BAC is well documented and has been presumed to specifically occur on AC in CBIs emplaced in the subsurface (Regenesis 2022).

The specific source material (coal, coconut, etc.), activation method (Coelho 2006), and particle size (GAC, PAC, colloidal) (El Gamal 2018) all influence BR. For example, coconut carbon does not bioregenerate as well as wood-based (Piai 2019) or coal-based AC (Zhang 2013). The activation method, e.g., chemical versus physical, influences the AC's surface area, pore type distribution, and adsorptive capacity, which all affect BR. As concerning particle size, BR has been demonstrated for GAC and PAC (El Gamal 2018). Generally, increasing particle size lowers substrate diffusion within the biofilm while increasing biodegradation (Liang 2007; Rattier 2012; El Gamal 2018).

AC porosity is likely the most important characteristic influencing both the rate and extent of AC BR (El Gamal 2018; Lu 2020; Akta¸s 2007). Larger pores generally support BR (Akta¸s 2007; Díaz de León 2021) and may provide microbes, enzymes, and surfactants with more access to the micropores or better habitat for microbes. The microbial community that develops on AC is influenced by the pore structure and the compounds available for biological degradation (Yu 2021).

Individual chemicals vary in how they affect the degree and rate of BR (Nath 2011a, b; Chan 2018). The biodegradability of a specific contaminant varies due to molecular structure among other factors (Cirja 2008). In general, large molecular weight compounds having low heats of adsorption are biodegraded easier than small molecules having high heats of adsorption (García-Delgado 2019).

The influence of multiple factors makes it difficult to estimate the degree to which AC can be recycled. The percent of dissolved organic carbon (DOC) degraded by biological activity varies from 25 to 42% (Fundneider 2021). Bioregeneration ranges from 12 to 95% (Nath 2011a, b). The percentages of pore restoration reported in the literature are broad (Akta¸s 2007; Nath 2011a, b). A model system of biofilm on AC degrading 2-nitrophenol was run for 38 months after which it was reported that 39% of the initial pore volume was still available (Smolin 2020). Thus, AC is not typically fully restored by microbial regeneration. This may be due to binding products of microbial metabolism and non-organic carbon (Smolin 2020; Díaz de León 2021). Nevertheless, the apparent adsorption capacity of microbial biofilm on AC exceeds that of the AC alone.

# *16.9.1 Process of Bioregeneration: Adsorption–Desorption, Exoenzymes, and Surfactants*

It has been argued that biodegradation occurs in the dissolved phase external to the AC after which the PHCs or other chemicals on the AC re-equilibrate with the dissolved phase (de Jonge 1996; Abromaitis 2016; Díaz de León 2021). However, not all chemicals that bind to AC are released with equal ease (Nath 2011a, b) or released as rapidly as BR has been reported to occur (Piai 2021). Therefore, this process is insufficient to explain BR of AC.

Conceivably, multiple processes are involved in BR, including adsorption and desorption and the participation of exoenzymes (Perrotti 1974) and surfactants (Marchal 2013; Edwards 2013). Biofilm producing microbes have been demonstrated to degrade phenanthrene adsorbed to AC, while non-biofilm forming microbes failed to do so (Leglize 2008). Microbes create a scaffolding made of EPS, which adheres to surfaces and allows microbes to build a biofilm habitat that supports microbial cooperation. One aspect of the formation of the biofilm habitat is the ability for the microbes to release extracellular enzymes (exoenzymes) into the biofilm forming a kind of extracellular digestive system (Flemming 2010).

The exoenzyme hypothesis proposes that enzymes secreted by the bacteria diffuse into the micropores and catalyze a transformation that desorbs the adsorbate from the micropores so that it moves out of the AC (Perrotti 1974; Nath 2011a, b). The argument against the exoenzyme theory is that enzymes are too large to enter the micropores (Xiaojian 1991), which is generally correct since most enzymes range from 3 to 7 nm in diameter if viewed as a sphere (Erickson 2009). However, it may not be necessary for the exoenzymes to enter the micropores. Focusing on the aqueous phase versus the adsorbed phase may overlook biocenoses within the biofilm. If the concentration of PHCs is reduced in spaces within the biofilm, PHCs may be released from the micropores (Klimenko 2003a, b) and be degraded within the extracellular space. The release of PHCs from AC may be enhanced by the presence of biosurfactants, which are also common in biofilms and have been demonstrated to facilitate BR (Klimenko 2003a, b).

### *16.9.2 Diet*

It has been proposed that PHCs in the micropores of AC are still available for biodegradation through DIET (Liu 2012; Summers 2010). Under methanogenic conditions, the biodegradation of PHCs requires the syntrophic cooperation between bacteria and archaea (Gieg 2014). Syntropic bacteria oxidize PHCs to CO2, H2, and acetate (Kim 2019). The methanogenic archaea remove the hydrogen, which helps the fermenters because the fermentation reaction is only thermodynamically favorable at low hydrogen ion concentrations (Barton 2005; Dolfing 2008). Microbial interactions whereby electrons are exchanged through intermediates such as hydrogen and acetate are well described (Kim 2019). A substantial body of evidence, however, supports that electron exchange can occur without chemical intermediates, also in the case of bacteria and methane-producing archaea (Summers 2010; Chen 2022), also in the case of bacteria and methane-producing archaea (Lovley 2017; Yee 2019). As a result of this process, bacteria may not need to be in direct contact with PHCs to be able to degrade them.

### *16.9.3 Conclusions and Future Research Needs*

While the general efficacy of AC and the derived CBIs has been demonstrated in field applications and laboratory studies, further research is needed. Failure to meet remediation goals occasionally occurs. Most of those failures are due to insufficient site characterization and inadequate distribution of the CBI within the hydrocarbon mass. Therefore, technological improvements are needed to optimize remedial design characterization and CBI installation. Further research is also encouraged to better understand the adsorption–desorption mechanisms of different AC types for in-situ remediation of PHC fuel mixtures.

On the biological side, how the syntrophic relationships form, which individuals become most numerous in the syntrophy, and how the relationships evolve as degradation proceeds are appreciated, but more needs to be done to understand these processes in CBI treatment zones as well as questions like whether co-injection of bacterial consortia significantly improves performance and how indigenous microbes interact under bioaugmentation. Further metagenomic and CSIA studies across hydrocarbon types and geological settings could be enlightening. The cooccurrence of microorganisms has helped unwind some associations, but employing transcriptomics and proteomics will move us beyond descriptions or cooccurrences.

High contaminant concentrations and NAPLs are difficult to bioremediate quickly. The capacity of AC to adsorb contaminants thereby lowering dissolved phase contamination can decrease the negative impacts to the soil biota by hydrocarbon contamination. Adsorption onto AC can reduce the bioavailability and, thus, the toxicity of hydrocarbons to subsurface microbes (Meynet 2012). While the adsorptive capacity is helpful and biodegradation proceeds despite the presence of NAPL, the remediation market craves faster degradation rates. Therefore, optimization of both products and installation practices to increase the rate of NAPL degradation is a pertinent area of research.

On the other hand, many remediation technologies that are initially successful with fuel hydrocarbons suffer from mediocre performance when contaminant concentration becomes low. For example, MTBE is very soluble in water. Consequently, it moves readily with groundwater and often creates expansive low concentration plumes. Biodegradation will stall as the concentration of the MTBE drops too low to sustain a robust population of microbial degraders (USEPA 1999; Muller 2007; Li 2014). AC overcomes this limitation by concentrating the oxygenate in its pores. Optimizing CBI remedies to address very low-concentration plumes is an additional area of research.

AC is versatile and will function under aerobic or anaerobic conditions. Mixers used to prepare CBI slurries typically entrain air and oxygenate the water. AC readily adsorbs dissolved oxygen, so the state within the pore structure is aerobic at the time of injection. This feature suggests that injection of CBI at sites where sparging systems are installed may significantly enhance remedial performance. Typical sparging systems are inefficient as most of the oxygen added is lost in the vapor leaving the saturated formation. The addition of AC may enhance sparging efficiency because of its ability to adsorb oxygen. This illustrates the possibility of many different applications of AC in subsurface remediation that are yet to be considered or investigated.

### **References**

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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# **Chapter 17 Application of Foams as a Remediation and Blocking Agent**

#### **Olivier Atteia, Henri Bertin, Nicolas Fatin-Rouge, Emily Fitzhenry, Richard Martel, Clément Portois, Thomas Robert, and Alexandre Vicard**

**Abstract** Foam consists of a mixture of water loaded with surfactant and gas. Injected into the porous medium, foam has many useful properties for soil remediation. The properties of surfactants facilitate the mobilization of pollutants, and the presence of gas greatly reduces the consumption of reagents but also makes it possible to block the passage of water. The foam rheology also leads to specific effects such as the stabilization of the front. This chapter first describes the characteristics of the foam in air and then in the porous medium. Subsequently, a review of the literature on the experiments carried out in the laboratory makes it possible to highlight all the effects of the foam. The following section is devoted to rare foam injection experiments carried out in a real environment. Finally, a section is devoted to the modeling of foam displacement in a porous medium.

O. Atteia (B) · A. Vicard UMR EPOC, Bordeaux-INP, Talence, France e-mail<EMAIL_ADDRESS>
A. Vicard e-mail<EMAIL_ADDRESS>
H. Bertin I2M-CNRS, University of Bordeaux, Nouvelle-Aquitaine, France e-mail<EMAIL_ADDRESS>
N. Fatin-Rouge IC2MP, CNRS, Université de Poitiers, Poitiers, France e-mail<EMAIL_ADDRESS>
E. Fitzhenry · R. Martel · T. Robert INRS-ETE, Quebec, QC, Canada e-mail<EMAIL_ADDRESS>
T. Robert e-mail<EMAIL_ADDRESS>
C. Portois Colas Environnement, Bordeaux, France e-mail<EMAIL_ADDRESS>
© The Author(s) 2024

J. García-Rincón et al. (eds.), *Advances in the Characterisation and Remediation of Sites Contaminated with Petroleum Hydrocarbons*, Environmental Contamination Remediation and Management, https://doi.org/10.1007/978-3-031-34447-3\_17

591

**Keywords** Experiments · Foam · Mobilization · Modeling · Remediation

### **17.1 Introduction**

Foam is defined as a dispersion of gas bubbles within a surfactant solution. In bulk, gas bubbles are separated by a continuous liquid phase composed of interconnected thin films called *lamellae*. Foam lamellae are stabilized by amphiphilic surfactant molecules characterized by hydrophobic tails and hydrophilic heads, a configuration which enables the molecules to rapidly adsorb onto the gas–liquid interface. Surfactants reduce the interfacial tension of biphasic systems, thereby promoting gas–liquid emulsification. As the active matter concentration of a surfactant solution is increased, the interfacial tension of the system will decrease, until the point at which surfactant molecules fully saturate the gas–liquid interface. At this concentration, referred to as the *critical micelle concentration* (CMC), the interfacial tension of the system is minimized and will remain relatively constant despite any further increases in active matter concentration. At concentrations above the CMC—the latter which typically ranges from 0.01 to 0.3 wt%—surfactant molecules will selfassociate to form structures called *micelles*. Any given surfactant's CMC value will depend on the charge of its hydrophilic head (cationic, anionic, or non-ionic), as well as on the ionic strength of the solution and its temperature. The interfacial tension induced by surfactant stabilizes the lamellae; foam lamellae can be further stabilized through the addition of polymers and/or nanoparticles to the surfactant solution.

An important characteristic of injected foam is its gas fractional flow rate (equivalent to the volume of gas divided by total foam volume), commonly referred to as *foam quality* ( *f q*). Given that the gas phase is usually the major component of foam, *f q* typically ranges from 30 to 99%. Foams have large surface areas (~200 m2/L of solution) and can occupy large volumes (~0.1 m3/L of solution), therefore making it useful in many applications (Cantat et al. 2013). Foams are often described in terms of their foamability defined as the capacity of the surfactants to form foam irrespective of the special foam properties. Foam is generated (i.e., lamellae are formed) by mechanical friction in presence of both phases. In bulk, foamability can be measured using normalized tests, such as the Ross-Miles or the Bikerman (Denkov et al. 2020; Longpré-Girard et al. 2019). Foamability depends on surfactant type and concentration and is optimal when the surfactant solution concentration is slightly greater than the CMC. However, foams are thermodynamically metastable systems that undergo constant cycles of bubble generation and destruction. Foam destruction can result from several phenomena: (i) gravitational liquid drainage in the lamellae, (ii) foam coarsening due to lamellae thinning (Ostwald ripening), and (iii) coalescence due to the pressure differences that exist between bubbles of different sizes.

### **17.2 Foam in Porous Media**

In porous medium, the liquid phase is continuous and covers the solid walls, while the gas bubbles, separated by liquid films, are located within the pore space. The presence of a solid phase and a disordered porous network changes the properties of foam from the one measured outside the porous medium (liquid/gas ratio, viscosisty…).

### *17.2.1 Adsorption of the Surface-Active Agents*

Adsorption of surfactants can be important because of the small molecule sizes and depends much on both the nature of the surfactant molecule and the solid material. When the liquid phase containing surfactant molecules is in contact with the solid surface, the rapid adsorption of the surfactant molecules may arise due to several interactions: (i) H-bond and acid–base interactions, (ii) electrostatic interactions between ionic groups, and (iii) chemical dispersion forces. Adsorption has been studied extensively and summary of the mechanisms as well as concatenation of results have been reviewed (Rosen and Et Kunjappu 2012; Schramm 2000). In practice, a surfactant slug should be injected before gas injection to ensure local availability of surfactant molecules at concentrations able to make lamellae.

### *17.2.2 Foam Generation*

Following the pioneering work of Roof (1970), the topic of foam generation has been extensively studied in the literature. The following four mechanisms are responsible for bubble generation in porous media, the first of which, *snap-off* , has the most important influence on the resulting foam rheology of all the mechanisms.

This phenomenon is illustrated in Fig. 17.1a. When gas flows from a pore body toward a pore throat, the velocity increases locally, and the liquid film thickness increases until the two surfaces join and form a lamella that is stabilized by the presence of surfactant.

In the *leave behind* configuration (Fig. 17.1b), a lamella is generated parallel to the direction of flow due to the pore geometry.

In *Lamellae division a* moving gas bubble is separated into two parts when it encounters an obstacle (Fig. 17.1c).

**Fig. 17.1** Schematic of the snap-off (**a**), the leave behind (**b**) and lamellae division mechanism (**c**), 1 is before the mechanism action and 2 after the action

### **17.3 Pinch-Off**

Liontas et al. (2013) described this mechanism by observing foam experiments performed in micromodels and showed that *Pinch-off* differs from *snap-off* in that it occurs in presence of several bubbles interacting (Fig. 17.2).

**Fig. 17.2** Neighbor-wall pinch-off (**a**) and neighbor-neighbor pinch-off (**b**) (from Liontas et al. 2013)

### *17.3.1 Foam Stability in Porous Media*

When flowing in a porous medium, the overall stability of foam is directly dependent on the stability of its individual lamellae, which in turn depends on parameters such as surfactant concentration, gas diffusion, and petrophysical properties. Capillary pressure (*Pc*) plays a key role, as it causes destabilization of liquids film at high values. Khatib et al. (1988) found that foams collapse when capillary pressure exceeds a critical value, *Pc\**, called *limiting capillary pressure*. Given that *Pc*  is in general inversely proportional to soil permeability, foam destruction is more significant in fine-grained materials.

Foam structure changes with time due to gas diffusion between bubbles (Fig. 17.3). This mechanism, called *foam coarsening* or *Ostwald ripening*, leads to bubble coalescence. It depends on several parameters, including but not limited to surfactant concentration, gas type, pressure, and temperature (Marchalot et al. 2008; Jones et al. 2018).

### *17.3.2 Foam Rheology in Porous Media*

When gas is injected into a porous medium, there are three possible flow behaviors, as illustrated in Fig. 17.4. First, in absence of surfactant in the liquid solution, foam cannot be generated, and gas and liquid flow separately (Fig. 17.4a), because of gravity, the gas flows with upward vertical fingering through the liquid phase. Second, foam may occur with a low lamellae density (density is the number of lamellae per unit volume, see Fig. 17.4b) because of lamellae instability or lack of generation efficiency. The low lamellae density only slightly diminishes the gas flow, and the given foam is called *weak foam*. Third, the lamellae density is high, resulting in a high resistance to foam flow with flat front displacement into the porous medium (Fig. 17.4c). In this case, the given fluid is called *strong foam*.

**Fig. 17.4** Different regimes of foam flow in porous media (from Sheng 2013)

Starting from the rheology of gas bubbles in capillary tubes where viscosity is due to porous medium resistance to lamellae movement, interface deformation, and surfactant gradient, Hirasaki and Lawson (1985) established the following relationship:

$$
\mu\_f = \mu\_{\rm g} + \frac{\alpha n\_f}{\nu\_{\rm g}^{1/3}}.
$$

where μ*f* and μ*g* are, respectively, foam and gas viscosities, α is an empirical parameter that depends on the porous medium, surfactant, and flow conditions, *n f* is the density of flowing lamellae, and *vg* the local gas velocity.

In soils, gas mobility also depends on the size ratio of gas bubbles to pore throats, soil permeability, and gradient as well as the local saturations of the gas and liquid phases (Falls et al. 1988; Maire et al. 2018a, b). For strong foams at steady state, the trapped gas fraction typically ranges from 85 to 99%, and bubbles move irregularly according to the pore geometry, some bubbles being trapped other move more rapidly (Friedmann et al. 1991).

### *17.3.3 Resistance Factor*

A foam's lamella density is directly associated with the viscosity it exhibits when flowing in porous media: High densities of lamella are associated with highly viscous foam. *Resistance factor* (RF) is commonly used to express the increase in pressure drop, ∆*P*, during foam flow by comparing it to the pressure drop induced by a single-phase water flood at the same volumetric flow rate, *Q*:

$$\text{RF} = \left. \frac{\Delta P\_{\text{foam}}}{\Delta P\_{\text{water}}} \right|\_{Q}$$

Similarly, the *mobility reduction factor* (MRF) can be used when the gas phase is used for the comparison:

$$\text{MRF} = \left. \frac{\Delta P\_{\text{foam}}}{\Delta P\_{\text{gas}}} \right|\_{Q}$$

These parameters are useful for describing foam flows in porous media. Typically, strong and weak foams are characterized by MRF values above and below 100, respectively.

### *17.3.4 Foam Injection in Porous Media*

The following three methods can be used to inject foam into a porous medium (Fig. 17.5). The first and third methods listed below result in the highest and lowest injection pressure values, respectively, while the second method is associated with injection pressures that are intermediate between the two (Maire et al. 2018a, b):

**Fig. 17.5** Illustration of the three mechanisms of injection of foam in porous medium

# **17.4 Non-Aqueous Phase Liquid (NAPL) Recovery Mechanisms**

At the microscopic scale, foam is a two-phase material that is subjected to many complex mechanisms in porous media, which taken together give foam its macroscopic properties. In an environmental remediation context, foam (i) has improved sweep efficiency in heterogeneous aquifers due to its non-Newtonian properties, (ii) eliminates displacement front instabilities caused by viscous forces, and (iii) lowers the capillary forces that otherwise maintain non-aqueous phase liquid (NAPL) droplets trapped, by decreasing the interfacial tension between the aqueous and organic phases in the presence of surfactants. The main characteristics of foam and a description of how each one contributes to contaminant mass removal are summarized in Fig. 17.6 (Fitzhenry 2021). Surfactant foams are able to recover NAPL present in an aquifer through three mechanisms: solubilization, mobilization, and volatilization.

### *17.4.1 Solubilization (Miscible Displacement)*

The particular structure of amphipathic surfactant molecules allows them to position themselves at the interface between two immiscible liquids, and to lower the interfacial tension of the system (see Fig. 17.7) as also discussed in Chapter 16.

In a contaminated aquifer, micelles have the ability to both encapsulate NAPL droplets in their cores (emulsification) and dissolve the molecules of the NAPL

**Fig. 17.6** Foam characteristics and NAPL recovery mechanisms (adapted from Fitzhenry 2021)

**Fig. 17.7** Structure of foam bubble walls, with gas (green), surfactant solution (blue), and NAPL (red) adapted from Farajzadeh et al. (2012)

constituents directly into the surfactant solution (solubilization). Micellar solubilization thus increases the concentration of the contaminant in the liquid fraction of the foam, making the contaminant more available for removal.

According to the mechanism proposed by Schramm and Novosad (1990), when foam in a porous medium encounters a NAPL, the decrease in interfacial tension between the surfactant solution and the NAPL can lead to the formation of fine micelles around the NAPL droplets which become embedded in lamellae, as shown in Fig. 17.8. Although this allows the droplets to be transported for some distance, the incorporation of emulsified NAPL into lamellae can negatively affect foam stability, causing lamellae to eventually collapse and release the NAPL. Subsequently, an intact lamella may arrive, pick up the NAPL and carry it a little further, and so on. This mechanism of transport is very effective, leading to high ratio of mass of hydrocarbon recovered to mass of surfactant used (10:1; Maire et al. 2015). Moreover, the displacement of initially trapped NAPL droplets in the form of an emulsion results in a reconnected continuous phase of non-wetting fluid, which increases its mobility when subjected to pressure.

**Fig. 17.8** Left, visualization of n-hexadecane droplets, stained with Oil Red-O, embedded in lamellae of a foam outside of porous media (Simjoo et al. 2013b). Right, theoretical diagram illustrating the configuration of surfactant monomers (gas in green, surfactant solution in blue, and organic phase in red), adapted from Denkov (2004)

### *17.4.2 Mobilization (Immiscible Displacement)*

Entrapped NAPL saturation (*S*oe) in an aquifer takes the form of discontinuous and near-immobile NAPL droplets that are trapped in pores by capillary forces. By definition, *Pc* is the difference between the pressures of the organic (typically the nonwetting phase) and aqueous (wetting) phases (*Pc* = *Po* − *Pw*). In order for NAPL trapped at residual saturation to flow into a neighboring pore, the capillary pressure must exceed the entry capillary pressure of said pore *P*ce (Joekar-Niasar et al. 2008):

$$P\_{\rm ce} = \frac{2\sigma\_{\rm ow}}{r} \cos \theta$$

where θ is the contact angle at the interface between the two liquids and the pore surface, and *r* is the pore radius (*m*). The efficiency of immiscible displacement of a NAPL by a washing solution is controlled by the capillary number (*N*Ca), which is defined as the ratio of the viscous forces that promote displacement of NAPL droplets, to the capillary forces that maintain NAPL droplets trapped in the pores (Pennell et al. 1996):

$$N\_{\rm Ca} = \frac{q\_{\rm w} \mu\_{\rm w}}{\sigma\_{\rm ow} \cos \theta}$$

where *qw* and μ*w* correspond to the Darcy flux (m/s) and dynamic viscosity (Pa·s) of the displacing fluid (water), respectively, σow to interfacial tension between the displaced (NAPL) and displacing fluid (N/m), and cos θ to the wettability condition of the medium. In order to promote mobilization of NAPL as a free phase bank in the porous medium, it is necessary to increase *N*Ca significantly by lowering interfacial tension by several orders of magnitude using surfactants.

NAPL recovery by mobilization can be represented by a capillary desaturation curve (CDC), a semi-logarithmic graph that shows the relationship between residual saturation in soils and capillary number (Lake 1989). A typical CDC (Fig. 17.10left) is characterized by a constant residual saturation (*S*or) of the non-wetting phase at low capillary numbers, a critical capillary number (*N*Ca*\**) required to initiate desaturation, and a full desaturation capillary number (*N*Ca*\*\**) when *S*or becomes zero (Robert et al. 2017).

The mobilization capacity of a NAPL depends on the properties (viscosity, density, wettability, and interfacial tension) of both the NAPL and the displacing fluid, as well as on the properties of the porous medium (permeability and pore connectivity). In fact, the wettability of the system and the particle size distribution of the soil both have an important influence on the shape and position of the CDC. For example, poorly sorted soils result in more inclined CDCs that cover a wide range of capillary numbers, due to large variations in pore size.

In addition, Dwarakanath et al. (2002) observed that the use of anionic surfactants can render an aquifer preferentially water-wet, resulting in a shift of the associated CDC to the left by 1–2 orders of magnitude, as shown in Fig. 17.9. A left-shifted CDC in a water-wetting system means that complete desaturation will be more readily achievable than in a mixed wetting or oil-wetting system, since the same saturation value can be achieved with a lower capillary number. It should be noted that the effect that the adsorption of different types of surfactants onto the porous material has on system wettability is complex. Wettability depends on the way that the solid phase and the surfactant chemically interact, on the degree of saturation of the available adsorption sites, as well as on the way that the surfactant molecules interact with each other (monolayer *vs.* multilayers).

In the literature, alternatives to the *NCa* have been proposed, in an effort to improve the assessment of mobilization capacity by accounting for the buoyancy force. The Bond number (*NB*) is the ratio of buoyancy to capillary forces, while the total trapping number account for both *N*Ca and *NB* contributions (Pennell et al. 1996).

Despite the destabilizing effect that light hydrocarbons have on foams, the latter still demonstrate better sweep efficiency in heterogeneous media when compared to conventional soil washing techniques that employ liquid solutions (Wang and Chen 2012). This is the result of the foam behavior presented in the first section of this chapter. This phenomenon provides a more uniform sweep of the porous medium. Nevertheless, foam flow in porous media is very complex and difficult to predict

**Fig. 17.9** Left: Schematic capillary desaturation curve (from Lake 1989), and right: Effect of foam on capillary desaturation of a dense NAPL (DNAPL) (adapted from Maire and Fatin-Rouge 2017)

Before Foam Injection After Foam Injection

**Fig. 17.10** Recovery of p-xylene (red) in 2-D sand tank within 2-layer silica sand (white): left pxylene at a residual saturation of 0.17 before foam injection; right p-xylene at negligible saturation after foam injection

at the microscopic scale, which makes the total desaturation difficult to achieve in practice, due in part to bypassing of NAPL due to pore blocking by trapped bubbles.

### *17.4.3 Volatilization*

A foam that is stable enough to flow through the porous medium can be composed of gas up to 98%, which represents the majority of the foam volume. Thus, the presence of gas bubbles in the saturated zone during foam flow makes it possible for some of the contaminant to be transferred into the gas phase by volatilization, depending on the vapor pressure of the contaminant. Mulligan and Eftekhari (2003) demonstrated that a large portion (66%) of pentachlorophenol in a fine sand was removed by volatilization during foam injection tests in columns. Similarly, Yan et al. (2019) conducted foam injection tests in columns of nitrobenzene-contaminated sand and found that 56% of the contaminant was removed by volatilization. Experiments studying the removal of hydrocarbons from creosote-contaminated soils involved mass balances calculations (C10–C40 TPH index) that indicated 80% of the contaminant was removed in the gas phase (Fatin-Rouge 2020). A foam injection experiment conducted by Longpré-Girard et al. (2016) in a 2D sandbox contaminated with p-xylene at residual saturation indicated that 65% of the contaminant mass was removed by volatilization.

It should be noted that the degree of volatilization depends on the physicochemical properties of both the contaminant and the mobilizing fluid (in this case, foam).

Regarding the contaminant, its vapor pressure and Henry's constant are of main importance. Both values tend to decrease the longer the hydrocarbon chain, and the higher the degree of H-bonds or stacking interactions. For mixtures of contaminants, the molar composition also plays an important role, as dictated by Raoult's law. Temperature also influences these values and is important to consider, especially when considering coupling foam injection with in situ thermal desorption. The octanol–water partition constant (*K*ow), which quantifies a contaminant's affinity for the aqueous phase, is equally important.

Regarding the foam, both the foam quality as well as the extent of micellar solubilization are important. Higher foam quality is associated with increased transport in the gas phase, whereas high concentrations of micelles will stabilize the contaminants in the liquid phase. Some authors have reported that the degree of volatilization of a contaminant tends to decrease the higher the surfactant concentration (Bouzid et al. 2019; Roy et al. 1995), which is likely due to the isolation of the compound within the micelles, making it stabilized in the liquid phase.

### **17.5 Laboratory Experiments**

# *17.5.1 Column Experiments Examining Foam–Oil Interactions*

The various experimental approaches employed in the literature have examined different aspects of foam–oil interactions and oil mobilization mechanisms in porous media. 1D experiments carried out in well-controlled laboratory conditions can provide excellent insights into pressure drop versus injection velocity and imbibitiondrainage versus capillary pressure relationships. These types of experiments also allow for easy exploration of different parameters, such as chemical formulation of foams. In the enhanced oil recovery (EOR) literature, the behavior of foam in porous media in the presence of hydrocarbons has been studied in detail. Most of these studies were performed in Bentheim sandstone core, a material whose permeability (on the order of 2–10−12 m2) and mineralogy are considered representative of petroleum reservoirs (Peksa et al. 2015). In order to replicate the typical conditions of an EOR process, the common practice is to perform tests at elevated temperatures and pressures on columns initially saturated with brine.

Farajzadeh et al. (2010) performed foam tests by alternating surfactant (AOS) and gas (N2 or CO2) injection in sandstone cores (SAG method). The front half of the cores were saturated with brine, while the back half of the core were at residual oil saturation (Isopar H, C9–C11). This configuration was used in order to reproduce the water–oil transition encountered in oil reservoirs. They observed that at low pressures (1 bar), the presence of oil does not allow foaming in the core. At higher pressures (90 bar), the CO2 foam reduced gas mobility in the region of low oil saturation; however, upon contact with the oil-containing zone, the gas and surfactant separated, and there was no oil recovery at the outlet. At even higher pressures (137 bar), the foam formed in the front part of the core persisted in contact with the oil, and the resulting increasing pressure increased the recovered mass.

Simjoo et al. (2013a) sought to improve the understanding of how the presence of an organic phase in a porous medium affects the rate of removal of that organic phase by foam. Previous studies often involved cores saturated with oil over only a portion of their length, in order to create a clean "antechamber" in which foam can form in situ prior to contact with oil. In this case, foam injection tests were carried out in sandstone cores containing a residual hexadecane phase along its entire length; this configuration is relevant in an ER context, where injection wells are placed within a source zone in order to displace NAPL and direct it to surrounding recovery wells. Thus, foam was formed by co-injecting gas (N2) and surfactant solution (1 wt%) at a constant total flow rate (*Q* = 1.1 mL/min, *f q* = 91%). The surfactants employed included an AOS, as well as a mixture (1:1 by weight) of AOS and a polymeric fluorosurfactant (FC), and all solutions were prepared using brine (0.5 M NaCl). From their tests, Simjoo et al. (2013a) identified two regimes of oil displacement. The first involves an increase in the *N*Ca and the formation of an oil bank. During this regime, oil saturation decreases until a value is reached at which oil cannot be displaced by the viscous force alone. This is when the second regime occurs, where the *N*Ca remains nearly constant, and the oil is moved droplet by droplet as a dispersed phase.

Janssen et al. (2019) investigated a combined foam and alkaline solution injection process in sandstone cores for hexadecane recovery. They sought to understand the effects that different salt contents in a surfactant solution might have on the formation of an oil bank and its subsequent mobilization by foam. The sandstone cores used, initially at residual oil saturation, were previously treated by injection of a solution bank composed of brine (0–2.5 wt% NaCl), an alkali (Na2CO3), a surfactant (internal olefin sulfonate, or IOS), and an alcohol (sec-butanol). This first step led to an increase in capillary number, which allowed the mobilization of oil droplets that coalesced into an oil bank. Downstream of the oil bank, some oil was solubilized and formed a microemulsion. The next step was to simultaneously inject gas (N2) and surfactant solution (IOS and Na2CO3 mixed with brine), which resulted in the formation of a strong foam and efficient oil displacement. The authors hypothesized that upon contact with the oil bank, a decrease in effective porosity caused a local increase in pressure drop, large enough to initiate foam generation. Thus, the results demonstrated that foam formation is possible in the presence of high oil saturation under these experimental conditions.

Tang et al. (2019) conducted sandstone core tests to compare how foams injected in different modes (i.e., pre-generation and co-injection) are affected by an organic phase present at residual saturation. The surfactant solution used was 0.5 wt% AOS with 3 wt% NaCl, and the gas used was N2. A series of tests were performed for each of the two oils studied: (1) hexadecane, and (2) a mixture of hexadecane and oleic acid (80:20 by mass). Tests with hexadecane (known to be weakly destabilizing) revealed similar results for both pre-generation and co-injection. In both cases, formation of a foam bank was observed, with gradual refinement of the foam texture along the core. Tests with the oleic acid/hexadecane mixture (known to be highly destabilizing) revealed significant differences between the propagation of the foams formed by the two injection modes. Co-injection had barely formed foam, even in the presence of low oil saturation (*S*or ≈ 0.1). Weak foam began to form near the column outlet, and the foam began a very slow propagation in the opposite direction of injection. On the other hand, the pre-generated foam showed two phases of propagation, the first characterized by weak foam that gradually lowers the oil saturation, to allow for the eventual formation of a strong foam front in the second phase.

### *17.5.2 Adding Particles to Foam in Presence of Oil*

The molar mass and molecular structure of surfactants may influence the stability of foam lamellae in the presence of organic phases (Vikingstad et al. 2006), although general trends remain unclear. Some studies (Osei-Bonsu et al. 2015; Yu et al. 2019) indicate that the rate of liquid drainage from lamellae was slowed for foams created from viscous surfactant solutions. This suggests that additives included in the surfactant solution to increase viscosity could help maintain foam stability in the presence of NAPL. In contrast, in contaminated sand column tests, Forey et al. (2019) observed that the higher the polymer concentration (and thus the viscosity of the surfactant solution), the lower the foam RF, meaning that the foam collapses easily upon contact with the organic phase.

The incorporation of colloidal particles into surfactant solutions to improve the efficiency of the foam injection process has been studied extensively in petroleum engineering (Nguyen et al. 2014; Singh and Mohanty 2017; Sun et al. 2014; Yang et al. 2017) and in environmental settings (Forey et al. 2019; Karthick and Chattopadhyay 2017). It is necessary to optimize the surfactant solution formulation, including particle type (composition, hydrophobic/hydrophilic behavior) and concentration, to avoid the formation of complexes (precipitates and aggregates) that can interfere with foam formation and clog the porous medium. Moreover, solid particles used to reinforce a foam in an environmental context must be non-toxic. Finally, the particles must be small enough not to reduce the permeability of the medium by deposition.

Sun et al. (2014) conducted micropattern and sand column foam injection tests to investigate the effect of adding hydrophobic silica nanoparticles (diameter = 14 nm) on the recoverability of a crude oil. The surfactant solutions used for the sand column tests consisted of sodium dodecyl sulfate (2.2 CMC), NaCl (0.5 wt%), and a variable SiO2 concentration (between 0 and 2 wt%). Foam was generated by co-injection of the surfactant solution and gas (N2) at a constant total flow rate (*Q*foam = 0.005 mL/min, *f q* = 50%). An increasing recovery efficiency was observed for SiO2 concentrations of 0–1 wt%, but for concentrations ≥ 1.5 wt% the improvement was negligible. As confirmed visually in micropatterning, the adsorption of solid particles onto the walls of foam bubbles allows the bubbles to maintain a spherical shape in the medium, indicating that they are more difficult to deform. This increased viscoelasticity allows the bubbles to exert a greater "micro-force" on the trapped droplets and dislodge them in greater numbers, even from dead-end pores.

Karthick and Chattopadhyay (2017) compared the effect of adding hydrophobic and hydrophilic silica nanoparticles for soil column diesel recovery by pre-generated foam injection. The surfactant solution used was Tween-20 (0.1 vol%; non-ionic) and 0.5 wt% SiO2 nanoparticles (diameter = 50–80 nm). The hydrophobic particles had formed aggregates in water and had to be pretreated with tetrahydrofuran. The results revealed recovery rates of 78 and 57.5% with the solutions containing hydrophobic and hydrophilic particles, respectively. The authors attribute this difference to a better adsorption capacity at air–water interfaces in hydrophobic particles. For comparison, a soil washing test with the Tween-20 solution without nanoparticles in the same column recovered only 42% of the initial diesel; no nanoparticle-free foam test was performed.

### *17.5.3 Layered 2D Sandboxes*

Studies in 2D cells are useful for improved understanding, controlling, and modeling of the mechanisms that drive the complex behavior of foam and the redistribution of contaminants. 2D studies are ideal for studying fluid behavior in permeability/ wettability contrasted media, which reflect the subsurface anisotropy of complex contaminated sites where usual treatments can be ineffective. The interest of foam in that context is its sweeping efficiency coupled to the processes presented above in the interaction oil/foam paragraphs.

#### **<IP_ADDRESS> Longpré-Girard et al. (2016)**

In this study, the custom 2D sandbox used consisted of a stainless steel tank (1.5 cm thick walls) with an internal volume of 2.5 L (26 cm long, 14 cm high and 8 cm thick) and a tempered glass window (1.5 cm thick) to enable visualization of foam flooding experiments (Longpré-Girard et al. 2016). The ability of surfactant foam to enhance mobility control and light NAPL (LNAPL) recovery in a layered heterogeneous porous medium was investigated. The 2D sandbox was filled with two layers, respectively, of coarse and medium silica sand. The surfactant solution used was Ammonyx Lo at a concentration of 0.1% (w/w). The experimental protocol included tracer tests in uncontaminated and contaminated (p-xylene residual saturation of 0.17) conditions, foam injection under uncontaminated conditions, as well as surfactant solution and foam injection under contaminated conditions. The foam was pre-generated and injected into the tank using increasing pressures (from 14.7 to 51.5 kPa). Tracer tests indicated that the permeability contrast between sand layers was increased by LNAPL contamination. Foam injection under uncontaminated conditions presented an S-shaped front that indicated a better mobility control than the piston-shaped front obtained during tracer tests. During foam injection, complete sweep of the sand tank was achieved with 1.8 pore volume (PV) compared to 2.8 PV during tracer injection, thus indicating better mobility control with foam. Pre-flush of the contaminated sand tank with surfactant solution initiated p-xylene mobilization, but no free phase was recovered at the effluent. LNAPL recovery mechanisms involved during foam injection in the contaminated sand tank were 19% by mobilization, 16% by dissolution, and 65% by volatilization. The p-xylene concentration measured in the sand after the foam injection experiment was < 16.3 mg/kg (Fig. 17.10).

#### **<IP_ADDRESS> Effect of Nanoparticles Addition**

Singh and Mohanty (2017) performed 2D sandbox foam injection tests containing two layers of sand (6:1 permeability contrast), to study the effect of adding hydrophilic silica nanoparticles (diameter = 10 nm) on the recovery of a crude oil. The foam was generated in a pre-column, using gas (N2) and surfactant solution (0.5 wt% AOS with 0.3 wt% SiO2 nanoparticles), and then injected into the tank at a constant velocity (*v*foam = 1.4–10−5 m/s, *f q* = 80%). The particle-stabilized foam (generated ∆*P* = 34.5 kPa after 22 VP injected) recovered 9% more oil than the particle-free foam (generated ∆*P* = 27.6 kPa after 23 VP injected) injected under the same conditions.

The goal of the Forey et al. (2021) study was to investigate how the presence of oil impacts foam formation during a liquid–gas co-injection process. As organic phases, such as LNAPL, usually act as defoaming agents that thwart the use of foam in remediation treatments, the synergy between a biological surfactant (saponin 10 CMC 0.5% wt) and silica colloidal particles was explored to obtain a strong and stable foam. In the investigations, gas–liquid co-injection experiments were carried out in a large 2D porous medium in the absence and in the presence of oil (MACRON 1821 F-4 oil). When no oil was present, although foam visually swept the porous medium in a piston-like way, buoyancy segregation still acts, and a gas/liquid saturation gradient takes place across the flow direction. As the surfactant concentration increases, foam strengthens due to a higher lamellae stability that prevents bubbles to coalesce. If SiO2 colloidal particles are incorporated in the formulation, the resistance factor is slightly enhanced and the water saturation (*Sw*) is substantially decreased. SiO2 addition probably arises in thicker lamellae without significantly affecting the foam structure, and bubbles are in a shield consisting of solid SiO2 colloidal particles. By repeating the experiment in the presence of a layer initially at residual oil saturation, formed foams are destroyed when they come in contact with oil, thus increasing the local gas mobility while it seems unaffected in the rest of the container. Results showed that foam resistance to oil-induced destruction was less damaging when the surfactant concentration was increased and was damped when SiO2 particles were added to the formulation. With this formulation, the pressure gradient within the tank was similar in the regions with and without oil, indicating that the particles provided efficient mobility control.

### *17.5.4 Fractured Media*

Fractured media are among the most difficult systems to manage, because of their strong heterogeneity and anisotropy, lower connectivity, the size, and the traveling of fractures that make flow very heterogeneous both in magnitude and direction (NASEM 2015). Despite all this, the improved sweep efficiency of foams and the ability to generate them in situ by co-injection has been demonstrated (Brattekås and Fernø 2016). Fatin-Rouge and Visitacion Carillo (2018) compared the recovery of different hydrocarbons with densities ranging from 0.9 to 1.8 g/cm3 in a watersaturated cell made of calcareous rectangular blocks with a network of fractures with size of ~3.5 mm. Pre-generated foams ( *f q* = 95%) were injected an compared to aqueous solutions. Conclusions revealed that foam should be preferred for light and volatile contaminants given the low density of both foam and LNAPLs and the importance of the mobilized fraction in the gas phase of foam.

### **17.6 Field Applications**

Foam has been applied to very few sites, and this was on chlorinated solvents contamination. The cases are presented below as the foam behavior and field processes could be very useful to understand the behavior of foam in presence of floating LNAPL.

### *17.6.1 Hirasaki et al. (1997a; b)*

The first application of foams for in situ remediation of soils at the field-scale was performed on a military base located in Ogden, Utah (Hirasaki et al. 1997b). The contamination consisted of trichloethylene (TCE), a chlorinated solvent used for degreasing metal parts, released to the environment as a result of its improper storage in trenches without impermeable bases. The contaminant was present at a concentration of 668 mg/kg as a residual saturation (no NAPL was detected in the test cell). The water table was located at approximately 7.6 m above ground level. The aquifer consisted of a sand horizon (0–15.2 m, *K* > 9.8–10−4 m/s), with an underlying clay layer (>15.2 m, *K* < 3.9–10−4 m/s) that acted as a capillary barrier and prevented vertical migration of contaminant. An environmental characterization of the site revealed the absence of TCE in areas of high permeability. Thus, the approach of the pilot test was to use the foam to regulate the hydraulic conductivity profile of the aquifer by selectively blocking the high permeability layer to improve the recovery by the surfactant solution in the low-permeability layer.

The test cell had an area of approximately 85 m2, on which 10 wells were constructed, including 3 injection wells, 3 recovery wells, 2 observation wells, and 2 hydraulic control wells. The injection and recovery wells were arranged in rows. Two observation wells were installed in the center of the cell and were used to identify the arrival time of the various fluids. A groundwater pumping and re-injection system was set up to prevent the loss of fluids outside the treatment area. The surfactant solution used was 4 wt% dihexyl sulfosuccinate (Aerosol MA-80I) (>4 CMC) and 10,250 ppm NaCl, a formulation that was optimized to maximize solubilization. The test lasted 3.2 days and consisted of simultaneous injection of surfactant solution at a constant flow rate (*Q* = 113 L/min) into two injection wells at a time, and air supplied by a constant pressure compressor (manually adjusted to be between 55 and 76 kPa) into the 3rd injection well. Air injection was alternated between the three wells over a two-hour period. The amount of TCE measured in the soils after treatment was 77 mg/kg, representing a removal of ~88% of the initial mass.

### *17.6.2 Portois et al. (2018a; b)*

A foam injection test was conducted by Portois et al. (2018b) at an active plant site in Spain contaminated by a storage tank leak during the 1980s. The aquifer beneath the site had a very heterogeneous stratigraphy, consisting of an impermeable clay layer from 0 to 5.5 m, a Quaternary aquifer composed of fine to medium sand (*K* ≈ 10–6 − 4.10–5 m/s) from 5.5 to 7.5 m, and an impermeable clay horizon from 7.5 to 11 m. The contamination was present as a dissolved plume of TCE (max. concentration 300,000 µg/L). Given the presence of low-permeability clay layers, there were concerns about exceeding the maximum safe injection pressure during the pilot test. The approach was therefore to inject the foam into the permeable sand layers, to provide temporary containment around the source area and to limit plume flow downstream. This study was a first step in the development of a method that allows for the delivery of reagents into a source zone using foams.

**Fig. 17.11** Left: aerial view of foam injection system showing the position of the wells (black dots), the estimated position of the TCE NAPL source zone (red area), the hydraulic conductivity (*K*, m.s−1) for the natural terrain and the estimated position and *K* of the foam areas (in squared black on white) from modeling. Right: value of the fluxes measured downstream the injection area, before and after the foam injection

The test cell had an area of ~1800 m2 in which 6 injection wells (screened interval 5.7–7.7 m) were installed around the periphery of the source zone. The surfactant solution used was a mixture of sodium laureth sulfate, cocamidopropyl betaine, and lauryl glucoside, and air was supplied by a compressor. No surfactant injection was performed prior to foam injection to avoid mobilizing the contaminant. The test was conducted over a 96-h period, during which surfactant solution and air were co-injected into all 6 wells simultaneously. Compliance with the injection pressure limit of 300 kPa (to avoid fracturing) was ensured during the test by manually adjusting the total injection rate (*Q*foam = 12.1 − 35.4 L/min), foam quality (95–99%), and surfactant solution concentration (0.3–0.9 wt%, i.e. 4–14 CMC). Blocking capacity was defined in terms of the ease of water flow through the medium: a decrease of more than 100 times the initial *Kw* was measured around the wells after foam injection. According to a numerical model, the radius of influence of the injected foam was 3.2 m (Fig. 17.11left). After foam injection, down gradient from the source zone, a three to fourfold reduction in the TCE mass discharged was measured (Fig. 17.11right).

### *17.6.3 Maire et al. (2017, 2018a; b)*

Maire et al. (2018a, b) performed the first field-scale application of foam injection for NAPL displacement and recovery by pumping. The study site was an active chloralkali plant located in Tavaux, France. The simplified stratigraphy of the aquifer was as follows: a fine alluvial horizon (0–2 m, *K* ≈ 10–7–10–9 m/s) at the surface, followed by coarse alluvium (2–9.5 m soil, *K* ≈ 10–2–10–4 m/s), and an underlying impermeable marly bedrock (*K* ≈ 10–9 m/s). Over the course of a year, the water table varied between 2 and 4 m below ground surface. The contaminant at the site consisted of a complex mixture—which included hexachlorobutadiene (58%), hexachloroethane (16%), pentachlorobenzene (3.5%), and tetrachloroethylene (8%)—present as an 8.3 m3 NAPL lens accumulated at the bottom of the aquifer (pool). In preparation for the pilot test, watertight bentonite cement walls were constructed along the perimeter of the cell (100 m2 square) to prevent the loss of contaminants outside the treatment area. Nine wells were installed in the cell, including a central recovery well, an observation well in each of the four corners of the cell, and four foam injection wells. The foam injection wells were constructed around the central well, at a distance of 3 m from the central well, with injection sections of 9–9.5 m between packers. During the first phase of the test, which lasted ~1 month, groundwater was pumped from the central well and re-injected into the four corners of the cell, with the goal of recovering as much free phase as possible via a submersible pump installed in the central well: this configuration resulted in the recovery of 7.6 m3 of NAPL. The second phase of the test consisted of foam injection in SAG mode, i.e., by alternating the injection of 1/6 VP of surfactant solution (sodium dihexylsulfosuccinate, 4 wt% or 2.7 CMC) followed by 1/3 VP of air. The injection was performed at constant pressure (value not specified).

Since the foam flows radially around an injection well, the NAPL was directed not only to the central well, but also to the four corners of the cell. In addition, due to technical limitations, the injection could only be performed in two wells at a time, instead of all four simultaneously. The configuration of the wells was found to not be optimal for the convergence of the NAPL to the central recovery well. No quantification of NAPL recovery was reported, but verification boreholes completed three weeks after injection revealed the presence of foam and the absence of NAPL droplets in the soils.

### **17.7 Modeling Foam Behavior**

As the different mechanisms described above show, foam presents a complex behavior in porous media. Two main types of models have been proposed so far to simulate foam behavior. Note these approaches do not include multiphase simulation of NAPL transport. Firstly, the so-called bubble generation models describe in detail the generation and coalescence of bubbles according to parameters related to the dynamics of the different fluids present and the porous medium. The so-called local equilibrium (LE) models are an extension of the multiphase models in porous media which modify the properties of the gas phase when it is present in the form of foam. Globally, bubble models lead to a finer description of the processes and can be partly validated by micro-model experiments, even if this requires some averaging. On the contrary, LE models are rather intended for core, 2D pilot or reservoir scale simulations and do not include some detailed mechanisms.

Different articles highlight the convergence of the two types of approaches on generic cases, see in particular the excellent synthesis of Ma et al. (2015) and the Lotfollahi et al. (2016) comparative study. We will use here a LE formulation. This formulation is often referred to as the "STARS" approach because this model was one of the first to develop a generic foam model. However, this type of model is currently included in most reservoir models, and we further present an implementation in the OpenFoam free library.

This modeling part only deals with the specificity of foam flow. For the interaction of foam with contaminants or oil phase, other components of multiphase flow models shall be used. In the oil industry multiphase models like ECLIPSE or others include the interface tension between three phases and shall be able to model foam–oil interactions (e.g., Olabode et al. 2021). To our knowledge there are no models of foam injection with TOUGHREACT (Xu et al. 2012) which lacks of the foam properties part. Foam can be modeled with UTCHEM as a specific fluid, and the model may also simulate the foam–oil interactions. However, Janssen et al. (2020) showed that the parametric approach of UTCHEM could not reproduce the foam behavior observed in experiments.

### *17.7.1 Foam in Porous Media Theoretical Basis*

In the framework of the generalized Darcy theory, the foam is represented as a gas with specific properties, mainly a higher viscosity. The gas permeability is modified according to the following equation:

$$q\_{\mathcal{g}} = -\frac{k k\_{r\mathcal{g}}^f}{\mu\_{\mathcal{g}}} (\nabla P - \rho \mathcal{g}) \text{ with } k\_{r\mathcal{g}}^f = \frac{k\_{r\mathcal{g}}}{1 + f \mathcal{m} m \partial \prod F\_i}$$

where *qg* is the Darcy flux of gas, *k* the permeability, *krg* the relative permeability for the gas phase, *k f rg* the relative permeability modified to account for foam, *P* is the pressure, *g* the gravity, ρ the density, *f mmob* the maximum foam resistance, and *Fi*  are foam factors.

In the STARS approach, each of the *Fi* function lies between 0 and 1 and the lower it is, the higher the *k f rg* will be, i.e., the lower the foam strength. Thus, this model allows, when one of the factors is zero, to return to a model of classical gas transport. In its formulation, the model also implicitly contains the fact that the presence of foam does not directly modify the capillary curves of water in the porous medium, which had been demonstrated by Bernard and Jacobs 1965 or Vassenden and Holt 1998 and more recently by Eftekhari and Farajzadeh (2017). Thus, the presence of foam mainly leads to a change in *Sw* which in turn modifies the gas relative permeability.

As shown in the previous sections, the major specificity of the foam is to present a maximum of viscosity for a given water content (often low), with a very fast decrease of apparent viscosity for low water contents (high quality) and much slower for the high water content part (low quality). This specific behavior is reproduced by a factor directly related to the water content, called *F*dry for the dry-out effect of the foam at high gas contents. In the STARS model, *F*dry is represented by:

$$F\_{\rm dry} = 0.5 + \frac{\arctan(epdry \cdot (S\_{\rm w} - fm dry))}{\pi}$$

where *fmdry* is the critical water saturation under which foam collapses and *epdry* controls the abruptness of the foam collapse (Vicard et al. 2022). This function creates a very steep front close to *fmdry*. Figure 17.12 represents on the left a modeled quality scan (QS) curve and on the middle the corresponding *k f rg* curve on a log scale. We can see that the maximum apparent viscosity point corresponds to a very steep drop in *k f rg*. In the *kr* graph, the *krg* value for pure gas is presented, and one can see that the "gas as foam" relative permeability is several orders of magnitude lower than the pure gas, the difference depending mainly on *fmmob* value. Although close to *Sw \** the value of the water relative permeability is quite low, because the amount of water in the medium is low. This is illustrated by the right panel of the figure where the mobilities of the two phases are presented with the total mobility. It can be seen that close to *Sw \** the total mobility is very small and is driven by the gas phase while when *Sw* increases, i.e., in the low-quality region (low *f g* value in QS curve, high *Sw*) the water mobility becomes more important and finally drives the foam behavior.

The other functions have all the same formula in the STARS model:

$$F\_i = \left(\frac{X}{f\_i}\right)^{\text{epi}}$$

where *X* is a variable, and *f i* and epi are constant. The functions studied here concern the effect of non-Newtonian behavior with the factor *F*cap, the effect of the presence of oil with *F*oil, and the surfactant concentration *F*surf.

**Fig. 17.12** Left: Quality scan curve with the maximum identified; middle: corresponding *kr* (*Sw*) curves for the QS curve, large red symbols indicating the correspondence between the points, *krg*  for pure gas, *k f rg* for gas as foam, *krw* for water; right: mobilities of the foam and water presented as function of *Sw*. Note that the high *f g* region corresponds to the low *Sw* range

### *17.7.2 Fitting Foam Model Parameters*

The STARS model includes in general two parameters for each factor, but the calibration is difficult because the observation is done on QS curves, whereas the real parameters of the model are the *Sw* and *k f rg* curves which are difficult to measure in this context. Moreover, the maximum of a QS curve is rarely obtained experimentally, and it does not correspond directly to the *fmdry* parameter. Therefore, there are different approaches that are all iterative. Ma et al. (2014) propose a method for fitting the *f* mdry parameters using optimization on a steady-state experiment supplemented with a transient experiment. Zeng et al. (2016) start with a quality scan experiment (fixed total velocity) to determine *F*dry parameters and the transition quality point *f g \**  followed by experiments at the given *f g \** value for several total velocities. However, their approach did not cover all the low-quality regime and lead to very low *epdry*  values which are too low compared to the literature. Abbaszadeh et al. (2018) used a similar approach. Boeije and Rossen (2015b) used the high-quality part of the curve to determine graphically the parameters *fmdry* and *fmmob*, then using the curvature of the low-quality part chose a point to obtain a value of *epcap* and then *fmcap*. This procedure gave good results but has the disadvantage of not being automatic. Hosseini-Nasab et al. (2018) used a multi-parameter joint calibration, but it was performed on a single curve.

Vicard et al. (2022) proposed a more general approach which aims at calibrating a set of curves obtained for different total velocities. This involved a calibration of the non-Newtonian part using an Oswald's law type approach on the set of curves, followed by an iterative calibration of the parameters of the *F*dry curve, differentiated according to the total velocities. This calibration approach gave satisfactory results on three different data sets.

Some authors did not use quality scan curves to retrieve foam model parameters. Ding et al. (2020) performed history matching showing a good fit to the model parameters, including the presence of oil.

The study by Valdez et al. (2021) detailed an estimate of the uncertainties on the major parameters of a quality scan curve. It turns out that with a few points and a single curve, the uncertainty is quite high, but it decreases sharply if the number of points on the curve is increased, justifying the need of several curves to calibrate the various model parameters.

### *17.7.3 Effects of Permeability*

Luo et al (2019) gathered about ten studies with permeability variations highlighting, for most of them, a power law increase of apparent viscosity with permeability. However, as the authors pointed out, most of the studies cannot be used directly to determine the model parameters, as most of the experiments were conducted for a single foam quality. The more detailed study by Farajzadeh et al. (2015), confirmed by Gassara et al. (2017), similarly revealed a power relationship, implying small variations of *fmdry* as a function of *k*. In other papers a decrease of *fmdry* with permeability is also shown. If the apparent viscosity increases with the permeability, the global pressure gradient decreases slightly, as the increase in viscosity does not totally compensate the effect of the permeability. This viscous effect leads, however, to a major effect of the foam, which is the profile stabilization. This effect already reported in the theoretical part is explored using modeling by Wei et al. (2018) whom show blocking of the most permeable channels due to foam. Wang et al. (2019) present results showing an increase in the apparent viscosity of the foam with increasing permeability, up to a peak that occurs around 1 Darcy for a 3:5 mixture of LAO and OA-12 (lauramidopropylamine oxide and *N*,*N*-dimethyldodecylamine).

### *17.7.4 Effects of the Surfactant Type and Concentration*

Most studies show major factors ( *fmdry*, *fcap, epcap*) that differ widely with the type of surfactant and even with their concentrations. Farajzadeh et al. (2015) show, using moradi-araghi data that the model parameters change depending on the surfactant used for the same medium and injection conditions. Jones et al. (2016) show that the concentration of AOS significantly changes the shape of the quality scan curve during nitrogen foam injection. The curves also differ depending on the solution used (brine or fresh water). Zeng et al. (2016) compared an anionic surfactant (AOS1416) with to zwitterionic surfactants (LB and LS) using nitrogen and demonstrates a better foam strength with the anionic AOS. Presently, there is, to our knowledge, no way to extrapolate the parameters value from one surfactant to another.

### *17.7.5 Effects of Total Velocity*

We performed a global study on the role of the total superficial velocity (*ut*) on the variation of the quality scan curves and associated parameters gathering most of the studies providing such data (Vicard et al. 2022). This was done on three different media as no other studies report quality scan curves for different interstitial velocity. Figure 17.13 shows the major results as the variation of the transition point position between low and high quality with the total interstitial velocity. The value of *f g \** is constant for the studies in quite high permeability media of Vicard et al. (2021) (*k*  = 5·10–11 m2) and Osterloh and Jante (1992) (*k* = 6·10–12 m2) while significantly increases with *ut* for lower permeability in the Alvarez et al. (2001) study (*k* = 5·10–13 m2). As it is the first analysis of this type, we do not know if this effect is really due to the permeability and if the results in other media would be similar. These three studies illustrate that the value of *f g \** largely varies according to surfactant (as already stated above), and quite low *f g \** values were obtained with saponin (Vicard et al. 2022).

Concerning the pressure gradient (Fig. 17.12b) one thing clearly appears is that the pressure gradient is much higher in Alvarez et al. (2021) and Osterloh and Jante (1992) studies which operate in consolidated media and with non-environmentalfriendly surfactant. It is known in the literature that the typical surfactants used in oil industry (AOS, SDS or similar) provide very strong foam and thus high pressure gradients.

In Alvarez et al. (2001), due to the small range of velocity tested, the shear thinning effect appears only at the highest velocity: the pressure gradient decreases (Fig. 17.12b). For the studies in higher permeability media, the relation between the pressure gradients shows a slight shear thinning effect for the Osterloh and Jante

**Fig. 17.13** Evolution of the breaking point of the quality scan curves as a function of velocity for three studies (Vicard et al. 2022, Alvarez et al. 2001, Osterloh and Jante 1992), left: the value of the gas fraction at the transition point ( *f g\**), on the right value of the pressure gradient (*Ut*) at the same point (values of the Vicard's experiments were multiplied by 10 to see them better). *Ut* in m·s−1

(1992) points but a simple linear relationship for Vicard et al. (2022)'s points. Therefore, as could be expected, the shear thinning effect varies with the medium permeability and certainly also with the type of surfactant associated with the pressure gradient value (Fig. 17.13).

# *17.7.6 Model Results to Understand Foam Behavior*

With the limitations provided above, several papers used the STARS approach to detail the foam behavior in porous media.

#### **<IP_ADDRESS> Gravity Effect**

Batot et al. (2016) illustrated the gravity effect on vertical 2D simulations (without radial effect) with a STARS-type foam model calibrated on 1D experiments. Using AOS and CO2, the simulations showed a gravitational effect on the injection without foam, while the foam allowed eliminating almost totally this effect, with velocities of the order of 200 m/year (or about 0.6 m/day). However, in order to limit this gravity effect, the tendency is to increase the strength of the foam, which induces a low injectivity. Boeije and Rossen (2015a) showed that, by using SAG, a simplified model can determine an injection optimum that limits the gravity effect.

#### **<IP_ADDRESS> Heterogeneous Media**

Tham (2015) compared simulation scenarios with and without foam, showing the profile stabilization effect with foam, but under the conditions presented, the amount of oil recovered in the presence of foam increases by less than 10% compared to the water alternating gas case, thus reducing heterogeneity is not always enough to remove all the oil from the porous media.

#### **<IP_ADDRESS> Foam Flow Around Injection Wells**

Gong et al. (2018) pointed out through a simplified model that conventional reservoir models have difficulties in representing the process of foam injection in the vicinity of wells. Their study showed that these simulators, due to the large grid size close to the wellbore, tend to simulate too low injectivity values for both gas and water.

#### **<IP_ADDRESS> Unstability and Transient Effects**

In the presence of oil or in coarse media, different authors show that several PVs are needed before obtaining a stable and efficient foam (e.g., Hosseini-Nasab et al. 2018). These processes were modeled by Lotfollahi et al. (2016), in a bubble model, the process being quite complex. To our knowledge this transient effect is not directly modeled in LE models or simplified by a foam formation approach. Instabilities within the foam in a SAG-type process are described by Farajzadeh et al. (2016), who states that they are due to spatially varying mobilities and can be limited by reducing the mobility at the injection point to a value close to the front.

# **17.8 Applying Foam Models for LNAPL-Contaminated Sites**

The vast majority of published studies have been carried out under conditions similar to those of underground reservoirs for enhanced oil recovery (EOR). However, for an environmental remediation application, many differences exist:

It appears that the conditions in ER are very different, which means that it will not be possible to use the same parameters as those used in the EOR conditions. However, different studies show that the model formulation can remain the same, and foam data are getting more abundant for these media.

### **17.9 Conclusion**

This chapter presented the behavior of foam in porous media with the objective to use it as a remediation or blocking agent. The physics of foam is complex which justifies the introduction of several parameters to transform classical multiphase models into foam specific models. These parameters are quite empirical and require numerous experiments. Moreover, the foam parameters depend not only on the porous medium but also on the chemical properties of the foam and the injection velocity. Laboratory experiments helped to understand the behavior of foam and demonstrated that foam is able to remove NAPL content of the porous medium to various levels, often better than with conventional surfactants or other techniques. Other advantages of foam are its low cost and shear-thinning behavior favoring injection front stability and sweeping of the porous media.