Tuning Foam Parameters for Mobility Control using CO2 Foam: Field

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Tuning foam parameters for mobility Control using CO2 Foam: A Field Application to Maximize Oil Recovery from a High Temperature High Salinity Layered Carbonate Reservoir Ali M. AlSumaiti, Abdul Ravoof Shaik, Eric Sonny Mathew, and Waleed Alameri Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.6b02595 • Publication Date (Web): 01 Mar 2017 Downloaded from http://pubs.acs.org on April 17, 2017

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Tuning foam parameters for mobility Control using CO2 Foam: A Field Application to Maximize Oil Recovery from a High Temperature High Salinity Layered Carbonate Reservoir Ali Al Sumaiti*1, Abdul Ravoof Shaik2, Eric Sonny Mathew3, Waleed AlAmeri4 *1Abu Dhabi National Oil Company, Abu Dhabi 2,3,4

ADNOC technical and innovation center, Abu Dhabi

KEYWORDS Mobility control, Carbonate, HTHS, CO2 foam, Empirical modeling, surfactant, CO2 gas, Chemical EOR ABSTRACT This paper investigates the reduction in gas mobility during the EOR (Enhanced Oil Recovery) process of gas injection due to the presence of foam thereby increasing sweep efficiency. The presented work is focused on developing a systematic approach to tune the CO2 foam parameters based on two separate core flooding experiments, the former conducted at variable foam qualities while the latter conducted at a fixed foam quality. The paper discusses the experimental data required for the modelling of mobility control using CO2-foam for a high temperature, high salinity layered carbonate reservoir. An empirical foam model is used for parametric matching of laboratory data and foam parameters are calculated and tuned. The key objective of the model is not only to match the measured apparent foam viscosity for varying foam qualities but also be able to capture the pressure drop measured for various experimental runs. The tuned foam model can be applied to field scale and design the injection strategy to maximize the oil recovery.

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INTRODUCTION Statistical reports indicate that about two thirds of the initial oil in place tends to be left behind even after secondary recovery[1]. In order to resolve this condition, several methods are implemented to produce more oil from a reservoir than that produced by primary and secondary recovery and this process is known as EOR[2-4]. Gas injection is a type of EOR process[5]. In this method oil is displaced by gases such as natural gas, nitrogen or carbon dioxide that act as displacing fluids[6, 7]. Gas injection can be miscible or immiscible flooding. If the displacing fluid is miscible with the reservoir oil at first contact (termed as First Contact Miscibility, FCM) or on multiple contacts (coined as Multiple Contact Miscibility, MCM) then it is known as miscible flooding[1]. On the other hand it is noteworthy that immiscible flooding is efficient when gas is injected into the secondary gas cap where the oil wells are producing as far down dip as possible[8]. These are termed as vertical gravity drainage projects[8]. Over the years gas injection especially CO2 flooding has been increasing as a preferred EOR method[9]. CO2 has relatively low minimum miscibility pressures (MMP) with a wide range of crude oils and it has the benefit of enabling CO2 sequestration in oil reservoirs making it popular over the years[6, 10]. However, in spite of the increasing popularity, CO2 injection has numerous challenges. One of the major problems seen in CO2 injection is the phenomenon of gas override which leads to an early gas breakthrough. This can be attributed to the low density and viscosity of CO2 gas in comparison to oil and water[5]. Further it is observed that viscous instability due to high mobility of CO2 makes the override worse and also affects the heterogeneity by forming high mobility channels[5, 11]. Hence recovery decreases drastically due to the above stated problems even though microscopic displacement efficiency is high[5]. Surfactant flooding is a type of EOR process which increases oil recovery by altering wettability and reducing the interfacial tension (IFT) between oil and water[12]. After a reservoir is water flooded, due to high capillary pressure, oil globules are left trapped behind. Surfactant targeting IFT is able to mobilize these trapped residual oil globules. However, the major hassle of surfactant flooding is the high cost and surfactant loss due to adsorption, trapping and precipitation[5, 6, 13] . The previous mentioned challenges of the individual EOR processes of gas injection and surfactant flooding ultimately lead to the conceptualizing and testing of foam[14]. It is observed that generation of foam by the combination of surfactant flooding with gas injection is not only cost effective but also a promising EOR process. Foam is known for its ability to reduce gas mobility and effects of heterogeneity, and hence increase the sweep efficiency[15]. It reduces the gas mobility since the trapped gas reduces the relative permeability to gas by blocking some of the flow channels[16]. Consequently the gas bubbles that flow experience a significant drag thereby increase the effective gas viscosity[5, 6, 17].


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In comparison to gas injection, foam affects the recovery of oil in two ways, one is by increasing the viscosity of displacing fluid which makes the displacement process stable and the second method is by diverting the displacing fluid towards the un-swept zones by obstructing the high permeable swept regions. This paper focusses on the effect of foam by the second method on a field scale[18, 19]. Significant efforts have been made in past to study the foam behavior in sandstone reservoirs at various temperature and reservoir conditions[20]. The CO2 foam studies has focused on the performance of foam in core samples with commercially available surfactants[6] . For example, at conditions with reservoir pressures up to 1450 psi and reservoir temperatures up to 50°C, a suitable surfactant for foam application can be Chaser CD-1045[6, 21]. However, the biggest challenge in the middle east is to find a suitable surfactant for the harsh reservoir conditions which are governed by temperatures around 120°C and salinity around 220,000 ppm. These conditions demand the requirement of a surfactant which in the presence of concentrated brine have high cloud points[22]. The cloud point temperature is a crucial value to be vary of as above this value, the CO2 foam will not be stabilized by the surfactant since precipitation of surfactant occurs from the brine[22, 23]. A promising surfactant that has been reported to be effective as a foaming agent in harsh conditions is Ethomeen C12[24]. The foam generated with this surfactant is tested using Silurian dolomite cores and is seen to exhibit low mobility at temperature of 120 °C, high pressure of 3400 psi and at high salinity[24]. Ethoxylated amines or commonly known as Ethomeen C12 is a type of surfactant that is soluble in CO2 thereby enabling additional options in injecting surfactants in water, CO2 or both phases[24-26]. For example, 0.2 wt% of Ethomeen C12 is dissolved in CO2 at 2600 psi and 120°C[22, 24] and exhibits low adsorption rates in the range of 0.1 – 0.13 mg/m2[24, 27]. However the stable foam is generated only if the pH value ranges between 4 to 6[22] . Foam is defined as a dispersion of gas in liquid where the liquid is the continuous phase and gas is the discontinuous phase[28]. The gas is made discontinuous by thin liquid films known as lamellae[28, 29]. Foam’s behavior is dependent on the geometry of the pore size, shape and connectivity[30]. Foam quality i.e. gas volumetric fraction in the total injected fluids can divide steady foam behavior into low quality foam regime and high quality foam regime at a transition foam quality[31, 32]. Foam flow exhibits two distinct regimes and this is clearly indicated in the pressure gradient contours[33]. In Fig 1, high quality foam regime is characterized by a large superficial velocity of gas and a low superficial velocity of water thereby exhibiting nearly vertical ∆P contours whereas the low quality foam regime is characterized by a low superficial velocity of gas and a large superficial velocity of water which ultimately leads to nearly horizontal ∆P contours[34].


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Fig 1 - Pressure gradient (psi/ft) as a function of superficial velocities of gas Ug and water Uw for one foam formulation in Berea sandstone core[34] The foam strength is attributed to the value of apparent viscosity of foam and the observed trend is that it increases with increase in foam quality in the low quality regime and then reaches a maximum, beyond which the increase in foam quality leads to a decrease in apparent foam viscosity in the high quality regime[6, 24]. The display of such a trend can be easily explained by the differences in foam behavior in the different regimes. In the low quality regime, the gas bubbles are widely spaced in porous media and are separated by thick liquid lenses whereas in the high quality foam regimes the pore spanning bubbles are closely packed and separated by individual lamellae[6, 35]. Based on this fact, in the low quality regime, it is a valid assumption that the bubble sizes are constant and so the bubble population increases leading to the consequent increase in foam viscosity[24, 31, 36]. However, in the high quality regime, the stability of foam is dominated by the limiting capillary pressure[24]. Limiting capillary pressure is a concept based on which most foam models are built, it is a value above which foam is considered to be unstable. In foam displacement experiments in porous medium, as gas saturation increases, capillary pressure also increases till it reaches a certain point beyond which the foam films do not survive and become coarser in texture[37]. This concept is depicted in Fig 2.

Fig 2 – Limiting Capillary Pressure Concept[37]


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When the foam quality increases after the transition point, foam starts collapsing trying to maintain the capillary pressure lower than or equal to the limiting value[24, 38-40]. Hence, the foam strength is a decreasing function of foam quality in the high quality regime. Behavior in the high quality regime can be modestly shear thickening or shear thinning but in the low quality regime it is markedly shear thinning, reflecting the reduction in gas trapping with increasing pressure gradient[31]. Further the foam strength is a function of the total flow rate at a specific foam quality. Empirical equations can be used to describe the shear thinning effect in the low quality regime[24, 38, 41, 42]. In the high quality regime since the effect of either shear thinning or shear thickening are observed, foam behavior can be physically predicted by models that use the limiting capillary pressure concept[24, 38, 42]. The shear thinning behavior is an added peril for CO2 foam flooding as foam is less viscous near the wellbore and more viscous in the deep reservoir[24, 38, 42]. This can be explained as follows, near the injection wellbore, injectivity and velocity will be high while the apparent foam viscosity will be low but when we go deep in the reservoir, injectivity will be low while the apparent foam viscosity will be high. This enables foam to act as an efficient mobility control agent in transferring surfactant solution from high permeability to low permeability media such fractures to matrix in naturally fractured reservoirs[43]. Since most of the liquid is separated from the gas when entering the rock, foam is not considered as a phase in reservoir rocks[6, 44]. In order to understand the mobility of foam, it is important to distinguish between continuous gas foams and discontinuous gas foams. As shown in Fig 3, in continuous gas foam, the gas channels are interconnected whereas in discontinuous gas foams, the gas phase is made discontinuous by lamellae[6, 36].

Fig 3 – Pore level schematic of fluid distribution for foam flow[45] The reduction of gas relative permeability is the only effect of continuous gas foams but for discontinuous gas foams the flow resistance of lamellae contributes to the increase in apparent viscosity of the gaseous phase[6, 35]. Thus, it can be concluded that the discontinuous gas foams exhibit better mobility control in comparison to continuous gas foams. To understand the effect of foam, another term to be kept in mind is foam texture which is defined as the number of lamellae per unit volume present in porous media. The finely textured foam with small bubbles can reduce gas phase mobility significantly making it strong foam[24].


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EFFECT OF OIL AND WATER SATURATION ON FOAM STABILITY To understand foam’s behavior, it is essential to understand the effect of different parameters on foam’s stability. In order to do so, it has been proven that Method of Characteristics (MOC) is useful in highlighting key mechanisms and strategies for improving foam performance[46-50] and also in better understanding of foam simulation models[48, 51]. The application of MOC to foam is mostly limited to two phase flow; oil if present is assumed to be immobile at its residual saturation. Three phase MOC is applied by Mayberry et al.,(2006)[49] where foam strength does not depend on oil or water saturation and gas is completely immiscible with water and oil. Three cases are examined in which foam reduces gas mobility greatly, moderately and not at all. Their experimental evidence indicates that apparent foam viscosity is strongly reduced at oil saturations greater than some critical oil saturation; below this saturation foam is weakened proportionately to oil saturation[52-54]. Moreover, another factor to note is that foam dries out and at least partially collapses abruptly at a water saturation corresponding to limiting capillary pressure[39, 47]. Mayberry et al.,(2006)[49] made a few assumptions and restrictions to simplify the description of the three phase flow process. Some of the basic assumptions and restrictions are flow is rectilinear and one dimensional in a horizontal porous medium, effects of gravity is neglected, immiscible displacement occurs and three phases (oil, gas and water) are present, properties depend only on phase saturations and not on pressure, phases are incompressible and porous medium is considered to be homogeneous, no chemical reaction and no degradation of surfactant with time[55]. On imposing these restrictions and using Darcy’s law with linear Corey-type relative permeability model in the equation for mass conservation of each of the three phases in porous media results in a homogeneous, reducible system of two first order equations with two dependent variables[55].

+ = 0 ; α = w, o

Where and are the saturation and fractional flow of phase α (i.e water or oil), respectively. The dimensionless position is defined as = x/L where L is the length of the medium. The dimensionless time is defined as the total volume of fluid injected until time t divided by the total pore volume of the medium. Riemann[56] gives an analytical solution to a system of first order partial differential equations for the above equation describing the fundamentals of the method of characteristics. On solving they are able to find a relation for rarefaction wave characteristic velocity which is a function of saturation of water and that of oil. The sufficiency condition states that if the relative permeability of each phase during three phase flow is a unique function of only its own saturation then the characteristic velocities are real everywhere in the three phase region in a ternary diagram[55].


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Most foam models are built on the concept of limiting capillary pressure above which foam is unstable. In foam displacement experiments in porous medium, as gas saturation increases, capillary pressure also increases till it reaches a certain point called the limiting capillary pressure, beyond which the foam films start to break and become coarser in texture[37]. It has been observed that as foam quality increases and water saturation decreases correspondingly, there reaches a value of Sw below which the coalescence of foam occurs and it starts drying up thus becoming coarser in texture[37]. The capillary pressure corresponding to this water saturation value as mentioned earlier is known as limiting capillary pressure. It must be noted that if the foam collapse is abrupt then the transition region between high and low quality regime occurs at one particular value of water saturation which is represented as Sw*. If the foam collapse is not abrupt then the transition between high and low quality foam regimes depends on a range of water saturation values[37]. The presence of oil has significant effect on the formation and breakdown of foam. The interactions between oil phase and foam lamellae are extremely complex[57]. Experimental evidence shows that solubilized oil destabilizes foam[55]. Foam destabilization may often include the migration of emulsified oil droplets from the foam film lamellae into the plateau borders[57]. Law et al.,1992[52] has shown that in general foam degrades as oil saturation increases, if oil saturation (So) is above a value called critical foaming saturation (So*) foam would collapse. It is observed that lighter and lower viscous crude oils are more destructive to foam stability than heavy crude oils[55]. The effect of oil is such that there is a critical oil saturation (represented as fmoil in simulation) which marks the maximum value of oil saturation that has an effect on foam and it has a limiting value (represented as floil in simulation) below which oil saturation has no effect on foam. It should be noted that above the critical oil saturation, foam cannot survive [55]. The experiments are said to be conducted on two cases: a) SoSw* - The foam stability is unaffected because foam is acting at a So value below the critical So and also above the Sw value where foam dry out occurs. b) So>So* and SwSo*[55]. EFFECT OF SURFACTANT CONCENTRATION ON FOAM The rate at which coalescence of foam occurs depends on the local capillary pressure, the surfactant formulation and the concentration of the aqueous phase. It is usually observed that at low surfactant concentration a high rate of coalescence occurs because there is little surfactant to exert a stabilizing effect. On the other hand, a dramatically low rate of coalescence is observed when the surfactant concentration is high. So it can be said that the surfactant is known to produce stable foam provided that the porous medium does not exceed limiting capillary pressure[58]. Literature suggests that various experiments done previously indicate that the efficiency of displacement by foam increases with increasing surfactant solution concentration. One of the observations from the experiments done is that the saturation change across the displacement front becomes less sharp/dispersed as the concentration of surfactant decreases. Another profound discovery is that lower concentrations of surfactant in the aqueous phase saturation Sw is larger near the outlet than the center of the sand pack column and this effect becomes more and more pronounced as the concentration of surfactant decreases. It is important to understand the concentration of surfactant has an immense effect on the foam strength and experiments are done in a manner such that the Cs used as a foaming agent is much higher than the critical micelle concentration value[58]. FOAM PILOT STUDIES It is worth mentioning that a few CO2-foam pilots have been performed in the past. One of the very first pilots that are implemented is at Rock Creek Field in Roane County, West Virginia. This field is a low permeability sandstone formation with high clay content[59]. In the North ward-Estes field, foam EOR was practiced for over two years. The injection scheme is such that initially foam is flooded followed by gas injection and it is observed that the generation of foam in-situ led to an effective reduction of CO2injectivity by 40 – 85%[60]. This observation indicated that the foam is successful at diverting the CO2 and improving areal distribution of CO2 in the field. Another foam injection pilot is conducted in the East Vacuum Grayberg / San Andreas Unit as a part of a joint industry DOE and academic study[61]. It has been concluded


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from this pilot study that strong foam could be generated in-situ and that the mobility of CO2 could be reduced by 1/3rd of that observed during the initial CO2 injection. Furthermore a CO2 foam pilot study is conducted in the SACROC field in west Texas[62, 63]. This is the first instance when a CO2 soluble surfactant formulation is delivered in the CO2 cycle. It has been observed that the in situ generation of foam occurred near the injection well and so the diversion of CO2 due to foam in the non-swept layers is quite evident. It is also observed that oil production at an offset producer almost doubled during the single well pilot evaluation[63]. HISTORY MATCHING THE FOAM CORE FLOODING DATA In a most recent work, Ethomeen C12 is used as a foaming agent and the behavior of foam in a 3 inches and a 6 inches carbonate outcrop core samples at high temperature and high salinity conditions is investigated[64]. For completeness, the experimental procedure is mentioned only in brief here and full details of the same can be found elsewhere[64]. Two sets of experiments are conducted, the first being fixed velocity with variable foam qualities and the subsequent one being fixed foam quality with variable velocities. The core data and experimental conditions of the first experiment are presented in Table 1. Initially the core is saturated with 100% brine and then it is injected with CO2 to create a base line giving an idea about the breakthrough time of CO2 gas by itself. Later Ethomeen C12 and CO2 are co-injected into the core sample at a total flow rate of 1cc/min (0.05ft3/d). The ratio of the injected surfactant and CO2 volume (thus foam quality) is varied and change in pressure drop along the core is measured and the same is presented in Table 2. The measurements are made by keeping the total superficial velocity constant at 0.08 cm/min (3.78 ft/d) while varying the injected gas fraction. For example, to attain a foam quality of 0.8, CO2 is injected at 0.04ft3/d and surfactant is injected at 0.01ft3/d. The change in pressure gradient is shown in Fig 4 and the apparent foam viscosity is depicted in Fig 5. The plot depicts that the pressure gradient increases with increasing gas fraction. It attains a maximum at a certain gas fraction (0.8 in this case) and the pressure gradient decreases as the gas fraction increases further.

Table 1 – Experimental Data (Run 1)[64] Core Length 7.62cm (0.25 ft) Core Diameter 3.81cm (0.125 ft) Absolute Permeability 240.571mD Porosity 0.31 Pressure 3500 psi Temperature 120°C Surfactant Concentration 0.1wt% Flow rate during co-injection 1cc/min (0.05 ft3/d)


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Table 2 – Foam experimental results conducted at fixed injection velocity[64] Injection Flow Foam Apparent foam Pressure Pressure gradient Rate (q) Quality viscosity cp Drop psi/ft cc/min (fg) Psi 1 0 0.743 0.506 2.022 1 0.2 2.137 1.455 5.819 1 0.3 2.332 1.587 6.350 1 0.5 2.805 1.910 7.639 1 0.7 3.488 2.375 9.498 1 0.8 4.992 3.398 13.593 1 0.9 1.224 0.833 3.333 In the second run of experiments, Rehan et al.,(2016)[64] measured the pressure drops for variable velocities at a fixed foam quality of 0.8. It is assumed that a foam quality of 0.8 produces strong foam as it has the highest apparent foam viscosity calculated in comparison to the other foam qualities tested. The core and experimental data for the second experiment is presented in Table 3. Similar to the previous run of experiments, the core is initially saturated with 100% brine and then it is injected with CO2 to create a base. Later, Ethomeen C12 and CO2 are co-injected into the core sample in such a manner that the foam quality of 0.8 is maintained. The ratio of the flow rates of the injected surfactant and CO2 volume is varied to maintain the foam quality while the pressure drops at different flow rates are measured. The results of the same are presented in Table 4. Table 3 – Experimental Data (Run 2)[64] Core Length 15.24cm (0.5 ft) Core Diameter 3.81cm (0.125 ft) Absolute Permeability 71.742mD Porosity 0.158 Pressure 3500 psi Temperature 120°C Surfactant Concentration 0.1wt% Foam quality during co-injection 0.8 Table 4 – Foam experimental results conducted at fixed foam quality[64] Injection Flow Foam Apparent foam Pressure Pressure gradient Rate (q) Quality viscosity Drop psi/ft cc/min (fg) cp Psi 0.05 0.8 2.398 0.958 1.916 0.1 0.8 2.939 2.348 4.696 0.2 0.8 3.353 5.358 10.716 0.5 0.8 2.898 11.578 23.156 1 0.8 1.882 15.044 30.088 1.5 0.8 1.889 22.643 45.286 2 0.8 0.921 14.725 29.451


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Pressure Gradient 16 Pressure Gradient (psi/ft)

14 12 10 8 6 4 2 0 0

0.2

0.4

0.6

0.8

1

Foam Quality (fg)

Fig 4 – Measured Pressure gradient at total injection rate of 1cc/min

Apparent foam viscosity 6 Apparent Foam Viscosity (cp)

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5 4 3 2 1 0 0

0.2

0.4

0.6

0.8

1

Foam Quality (fg)

Fig 5 – Measured Apparent Foam Viscosity at total injection rate of 1cc/min To capture the foam behavior in the first run of experiments, a core scale simulation model is developed and a Semi empirical approach is used to history match the core flooding data. The generation of foam and the attainment of a high value of apparent viscosity of foam are investigated in this model. The model is homogeneous with a single layer in which the well distance between the injector and the producer is 0.25 ft (3 inches). The thickness is 0.1 ft (1.2 inches) while the porosity and permeability are 0.3 and 240.57 mD, respectively. The laboratory


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experiment is conducted at reservoir pressure of 3500 psi and temperature of 120 °C, so the same conditions are applied to the model as well. It is assumed that the surfactant concentration does not affect the gas water interfacial tension, thus surfactant is considered to be water like component whose sole function is to act as a foamstabilizing agent[43]. In the model, similar to the experimental run both surfactant solution and CO2 are co-injected into the core and foam parameters are tuned to match the experimental data. Since the experiment is done in the absence of oil, oil saturation is considered to have no contribution to the foam strength. A set of steady state foam experiments has been done at a total superficial velocity of 4.14ft/d and the effect of foam quality on apparent foam viscosity is measured. As mentioned earlier, it observed that as the injected gas fraction increases, the pressure gradient increases and continues to do so till it reaches a maximum[34]. Further increase in injection of gas fraction causes the pressure gradient to decrease leading to two distinct regimes[34]. The apparent foam viscosity is defined as the normalized pressure gradient with respect to total flux of surfactant solution, gas and permeability[65]. Thus, when the injected gas fraction increases, the foam apparent viscosity increases. This is termed as the low quality regime. Whereas, further increase in the injected gas fraction indicates a region where in the apparent foam viscosity decreases; this region is termed as the high quality regime[6]. Foam obtains its maximum apparent viscosity at a given surfactant concentration and total superficial velocity at the boundary of the two regimes[6]. The gas fractional flow at the boundary of these two regimes is known as transition foam quality[38]. The data points in Fig 5 denote a transition foam quality at 80% gas injection with a maximum foam apparent viscosity of 4.99cp. In order to obtain the match, foam parameters have to be tuned. A high value of epdry is chosen indicating that the transition between the high quality and low quality regimes are abrupt[6]. Epdry or sfbet is a reference dry out slope used in the calculation of dimensionless drying out of foam[66]. The tuned foam parameters, which has shown the closest match with the apparent foam viscosity data is shown in Table 6. Other parameters used in the simulation model are presented in Table 5. History matched data for foam quality vs apparent viscosity is shown in Fig 6. It can be seen that the tuned foam parameters assist in predicting a pretty close match with the experimental results. Once the apparent foam viscosity data is matched, it is then vital to interpret and match the pressure drop observed experimentally. In this particular simulation run, the model is designed to obtain results for a particular injected gas fraction (fg=0.8). A value of 3503.39 psi is observed experimentally across the core. In the designed model, for the first 8 days sole CO2 gas is injected and then on the ninth day, surfactant is injected creating foam which is verified by the increase in pressure as seen in Fig 7. An important criterion to be cognizant of while tuning the model is that out of the different foam parameters, fmmob and sfbet have a sizeable effect on the pressure drop values. The model is tuned based on these two parameters along with the value of Krgf (end point of gas relative permeability after foam) in order to obtain the experimental pressure drop. After several attempts it is seen that the pressure drop is 3503.27 psi which is


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quite close to the experimental value of 3503.39 psi and well within the acceptable error margin. The foam parameters incorporated to obtain the match is shown in Table 7. A similar approach is implemented to the experimental results of other foam qualities (fg=0.5, 0.7 and 0.3). It can be noticed that the fmmob values used for the matches are relatively high but these have been chosen with assurance based on values noticed in various published literature by Boeije et al.,2015[34]. It is seen in Fig 8 that the pressure obtained from simulation is a value of 3501.95 psi which is in the acceptable error margin in comparison to the 3501.91 psi measured experimentally for a foam quality of 0.5. Further as seen in Fig 9 and 10, simulation models for foam qualities of 0.7 and 0.3 results in pressure values of 3502.53 psi and 3501.55 psi in similitude to 3502.37 psi and 3501.58 psi respectively. The parameters required for tuning of these models are shown in Tables 8 and 9 clearly indicating the variations in fmmob and sfbet for different cases. Table 5 – Parameters for CO2 foam simulation (fg=0.8) Parameters Values Swi 0.35 Krw 0.79 Krg 0.7 Krgf 0.003 Nw (Corey exponent) 3.5 Ng (Corey exponent) 4 Viscosity of water (cp) 0.308 Viscosity of gas (cp) 0.018 Table 6 – Foam Parameters tuned to match experimental results of Rehan et al.,(2016)[64] Foam Parameters Values fmmob (mobility reduction factor) 10000 fmsurf (Surfactant Fraction) 0.001862 epsurf (surfactant exponent) 3 fmdry (critical water saturation) 0.334 epdry (foam dry out exponent) 69900

Fig 6 – History matching of data of Foam Apparent Viscosity vs Foam quality (Dots represent lab data)


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Table 7 – Foam Parameters tuned to match experimental pressure results (for fg=0.8) of Rehan et al.,(2016)[64] Foam Parameters Values fmmob (mobility reduction factor) 159700 fmsurf (Surfactant Fraction) 1.862E-7 epsurf (surfactant exponent) 4 fmdry (critical water saturation) 0.5 epdry (foam dry out exponent) 970

Simulated Result Experimental Result

Fig 7 – Simulated vs experimental results of Rehan et al.,(2016)[64] for pressure drop along core for fg=0.8

Table 8 – Parameters for CO2 foam simulation Parameters fg=0.5 fg=0.7 Swi 0.35 0.35 Krw 0.79 0.79 Krg 0.7 0.7 Krgf 0.28 0.06 Nw (Corey exponent) 5 2.3 Ng (Corey exponent) 2.2 2.6 Viscosity of water (cp) 0.308 0.308 Viscosity of gas (cp) 0.018 0.018


fg=0.3 0.35 0.79 0.7 0.55 3.5 4 0.308 0.018

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Table 9 – Foam Parameters tuned to match experimental pressure results of Rehan et al., (2016)[64] Foam Parameters fg=0.5 fg=0.7 fg=0.3 fmmob (mobility 20000 10000 100000 reduction factor) fmsurf (surfactant 1.862E-9 1.862E-8 1.862E-9 Fraction) epsurf (surfactant 2 4 2 exponent) fmdry (critical water 0.5 0.5 0.5 saturation) epdry (foam dry out 600 800 20 exponent)




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Semi empirical approach is used to history match the core flooding data for the second run of experiments by developing a model similar to the previous run. The generation of foam and the contribution of varying velocities in attaining high apparent foam viscosities are investigated in this model. The model is a homogeneous one with a single layer in which the well distance between the injector and the producer is 0.5ft (6 inches). The thickness is 0.125ft (1.5 inches) while the porosity and permeability are 0.15 and 71.74mD respectively. The laboratory experiment is conducted at reservoir pressure of 3500 psi and temperature of 120°C, so the same conditions are applied to the model as well. The surfactant solution and CO2 are co-injected into the core and foam parameters are tuned to match the experimental data indistinguishably like the first run of experiments. The experiment is performed in the absence of oil and oil saturation is considered to have no contribution to the foam strength. The measurements are made by keeping the foam quality constant while varying the total superficial velocity. For example, to maintain the foam quality of 0.8 at a velocity of 0.017 cm/min (0.8 ft/d), CO2 is injected at 0.008 ft3/d and surfactant is injected at 0.002 ft3/d. It is pivotal to match the pressure drop observed experimentally in order to validate the designed model. Rehan et al.,(2016)[64] measured a value of 3505.36 psi at a total flow rate of 0.2 cc/min and so the model is fine tuned to attain this result and therefore as depicted in Fig 11 a value of 3505.38 psi is obtained. In the same fashion as the previous run of experiments, for the first 8 days only CO2 gas is injected and then on the ninth day, surfactant is injected creating foam which is verified by the increase in pressure. The foam parameters and other simulation parameters incorporated to fine tune the model is indicated in Tables 10 and 11. Table 10 – Parameters for CO2 foam simulation (qt=0.2 cc/min) Parameters Values Swi 0.35 Krw 1 Krg 0.57 Krgf 0.00025 Nw (Corey exponent) 2.84 Ng (Corey exponent) 2.8 Viscosity of water (cp) 0.308 Viscosity of gas (cp) 0.018 Table 11 – Foam Parameters tuned to match experimental results of Rehan et al.,(2016)[64] Foam Parameters Values fmmob (mobility reduction factor) 100000 fmsurf (Surfactant Fraction) 1.862E-8 epsurf (surfactant exponent) 4 fmdry (critical water saturation) 0.5 epdry (foam dry out exponent) 50


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A similar approach is implemented to the experimental results of other flow rates (qt=0.5, 0.1 and 1.5cc/min). It is seen in Fig 12 that the pressure obtained through simulation is a value of 3511.57 psi which is in the acceptable error margin in comparison to the 3511.35 psi measured experimentally for a flow rate of 0.5cc/min. Further it’s seen in Fig 13 and 14, simulation models for flow rates of 0.1 and 1.5cc/min results in pressure values of 3502.54 psi and 3520.18 psi in similitude to 3502.35 psi and 3522.64 psi respectively. The parameters required for tuning of these models are shown in Tables 12 and 13 clearly indicating the variations in fmmob and sfbet for different cases.


Fig 11 – Simulated vs experimental results of Rehan et al.,(2016)[64] for pressure drop along core for qt=0.2cc/min

Table 12 – Parameters for CO2 foam simulation Parameters qt=0.5cc/min qt=0.1cc/min qt=1.5cc/min Swi 0.35 0.35 0.35 Krw 1 1 1 Krg 0.57 0.57 0.57 Krgf 0.09 0.08 0.127 Nw (Corey exponent) 4.8 4.8 4.4 Ng (Corey exponent) 4.8 4.8 3.8 Viscosity of water (cp) 0.308 0.308 0.308 Viscosity of gas (cp) 0.018 0.018 0.018


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Table 13 – Foam Parameters tuned to match experimental pressure results of Rehan et al. (2016)[64] Foam Parameters qt=0.5cc/min qt=0.1cc/min qt=1.5cc/min fmmob (mobility reduction factor) 10000 100000 10000 fmsurf (surfactant Fraction) 1.862E-7 1.862E-7 1.862E-8 epsurf (surfactant exponent) 4 4 4 fmdry (critical water saturation) 0.5 0.5 0.5 epdry (foam dry out exponent) 800 700 500






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Fig 14 – Simulated vs experimental results of Rehan et al.,(2016)[64] for pressure drop along core for qt=1.5cc/min FIELD APPLICATION Based on the experimental observations, tuned foam model is applied on a field scale to maximize the efficiency of injected gas. The surfactant solution is injected in the high permeability zone and gas in the low permeability zone such that foam is created in-situ as the gas migrates to the high permeability zone thereby leading to the containment of gas in the low permeability zone[67]. The 3D simulation model is a heterogeneous one in which the distance between the injector and the producer well is measured at 800ft separated by 40 grid blocks. Further it consists of 50 layers such that the thickness of the model is 160ft. Fig 15 shows two permeability profiles (perm 1 and perm 2) as a function of depth, the profile termed perm 1 is used as the reference permeability profile to incorporate heterogeneity into the model[67]. The first 22 layers in the model are considered as the high permeability zone while the bottom 28 layers is the low permeability zone. The high permeability zone exhibits permeability values in the range of 50 to 1000mD while the low permeability zone exhibits values in the range of 5 to 20mD. The 3D model representing the heterogeneity is represented in Fig 16. The initial pressure and temperature of the reservoir are 4000 psi and 120°C and a PVT modelling package was used to characterize the oil. The initial fluid distribution is taken from the hysteresis model depicted in Fig 18 and the saturation profile is incorporated using Fig 17 as a


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reference[68]. The foam parameters and other simulation parameters incorporated to fine tune the model is indicated in Tables 10 and 11 which is explained in the earlier sections.

Fig 15 – Permeability Profile with respect to depth[67]

Fig 17 – Saturation profile[68]

Fig 16 – 3D Model – Heterogeneity Profile

Fig 18 – Capillary Pressure Curve with Sw


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In this study, three different field development scenarios are considered as shown in Fig 19: 1) Water flooding in all layers 2) Water flooding followed by gas injection only in the low permeability zone 3) Water flooding followed by foam flooding SCENARIOS

INJECTION SCHEMES

Scenario 1

0.4PV water

1.4PV of water

Scenario 2

0.4PV water

1.2PV of CO2

Scenario 3

0.4PV water

0.89PV surf

0.79PV surf + 1.13PV CO2

Fig 19 – Injection Schemes Scenario 1: In the case of water flooding, it is a vertical well configuration scenario in which 1.8PV of water is injected into the designed model. The results of the water flooding simulation run are shown in figures 20 and 21. Fig 20 shows the recovery factor in the high permeability zone, low permeability zone and the total field along with the water cut as a function of the pore volume injected. It is observed that the sweep efficiency in the low permeability zone is very low in retrospect to the high permeability zone and the total field recovery is hence about 33% only. This is due to the fact that the water quickly sweeps the high permeability zone as soon as it is injected. Fig 21 validates the aforementioned situation. The orange color region in Fig 21 shows high saturation of oil indicating that the lower region is un-swept by the water flood. Scenario 2: The next scenario studied is the injection of gas in a model after two years of water flooding, 1.2 PV of CO2 gas is injected after injecting 0.4PV of water. At this juncture it is essential to accentuate that CO2 gas is injected only in the low permeability zone while water is injected in both the zones primarily. Fig 22 and 23 shows the results from the said injection scheme. The recovery factor from the high permeability zone, the low permeability zone and the total field with respect to the pore volume injected can be seen in Fig 22 while Fig 23 indicates the unswept region in the low permeability zone after the gas injection which is very similar to the results obtained in the water flooding scheme described in the earlier section. But in the case of gas flooding, such an observation can be attributed to the gravity over ride of gas leading it to flow directly to the high permeability zone exhibiting an inability to sweep the lower layers. The total field recovery obtained is about 30% which is less in comparison to sole water flooding doesn’t imply that water flooding is better because the water cut is close to 96% which is not a desired number in any field production data. In hindsight, both water flooding and gas flooding exhibit a low oil recovery factor because a major area of the low permeability zone is un-swept thereby resulting in low recovery of the total field.


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Scenario 3: The next trivial step in the study is to develop a scheme that can address some of the shortcomings of the previously mentioned schemes to sweep the low permeability zone and eventually increase the total field recovery. Masalmeh et al.,2010d[69] introduced a new concept in which foam can be used to control the mobility of gas in highly heterogeneous reservoirs[67]. In this approach, the surfactant solution is injected in the high permeability zone and the gas which is carbon di oxide is injected in the low permeability zone. Due to gravity override the gas will migrate to the upper zone and as soon as it comes in contact with the surfactant solution, foam will be generated. The foam created will have the potential to confine the injected gas in the low permeability zone and hence increase the total field recovery[67]. The reason behind for the said effect is the pressure gradient created in the high permeability zone by the continuous injection of surfactant solution which in combination with the reduced mobility of gas by foam generation, limits the cross-flow of gas into the high permeability zone and thereby leading to gas trapping in the low permeability zone. It is observed that limited or no foam is generated in the low permeability zone and hence the mobility of gas in this zone is not altered [67]. The main focus of the study is to evaluate the capability of foam to enhance the recovery of oil in such a heterogeneous reservoir and hence the effect of oil saturation on foam stability is not considered. The major challenge is to design a model where in the foam generated is confined to the region between the high permeability zone and low permeability zone. Initially few numerical experiments are performed where 0.4PV of water is injected and then 1.15PV of gas is injected in the lower sector and 0.4PV of surfactant solution in the top sector. Unfortunately, by injecting carbon dioxide for a few years and then injecting surfactant enabling the generation of foam on the introduction of surfactant solution into the field didn’t serve the required purpose. It leads to generation of foam in the entire high permeability zone rather than the region between the two zones. This is because since gas flooding is done initially, due to gravity over ride, gas travels quickly to the high permeability zone and saturates the region thereby enabling foam creation in the entire zone as soon as surfactant solution is injected as shown in Fig 24. It is worthy to mention that even this would lead to confinement of gas in the low permeability zone, however, the recovery still low. In order to address this issue the surfactant solution is injected initially followed by gas injection. The idea here is to ensure that sufficient amount of surfactant is present in the model. Hence similar to the previous case of gas flooding, initially 0.4PV of water is injected in both the zones, then 0.89 PV of surfactant solution is injected in the high permeability zone followed by 1.13 PV of CO2 injected in the low permeability zone and 0.79 PV of surfactant solution in the high permeability zone. When such an injection scheme is followed, it can be seen that foam is generated only in the desired region between the two zones as indicated by the blue color sector in Fig 25. Further to primary objective of trapping gas in the low permeability zone is also achieved as indicated by the orange color region in Fig 26 while the oil saturation post foam flooding is shown in Fig 27. Finally, Fig 28 depicts the recovery factor in the high permeability zone, low permeability zone and the total field as a function of the


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pore volume injected. It is observed that the field recovery of the low permeability zone has been increased significantly after the trapping of gas in that sector enhancing the sweep of oil in the region thereby leading to an increase in the total field recovery to a value of 60%. This clearly shows that injection of surfactant solution first and later follow it by gas flooding in the lower sector will give higher efficiency in comparison to the other approaches, indicating quite an effective displacement process in a heterogeneous reservoir. A compiled comprehensive result is indicated in Fig 29 wherein the recovery factors from previously explained injection schemes are depicted for comparison purposes.

Fig 20 – Water Flooding Recovery Curves of the High K zone, Low K zone, total field plus water cut

Fig 21–Oil Saturation after 1.8 PV of water flooding, orange color represents un-swept region


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Fig 22 – Gas Flooding Recovery Curves of the High K zone, Low K zone and total field

Fig 23 –Oil Saturation after gas flooding, orange color represents un-swept region


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Fig 24 – Foam (blue color) formed in the High K Zone

Fig 25 –Foam (blue color) formed in between the High K and Low K Zone in the Foam flooding Scheme

Fig 26 –Gas trapped in the Low K zone due to formation of foam between the two zones


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Fig 27 –Oil Saturation in the total field after foam flooding

Fig 28 – Foam Flooding Recovery Curves of the High K zone, Low K zone and total field


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Fig 29 – Foam Flooding Recovery Curves of Water Flooding, Gas Flooding and Foam Flooding along with Water Cut % CONCLUSIONS The following observations are made from the present work. 1. A systematic approach is proposed to tune a foam model based on the experimental core flooding data. 2. The value of critical surfactant concentration, fmsurf, is chosen in such a manner that the generation of foam is ensured in the model. Investigating the effects of surfactant concentration on foam generation has been included in the further work. 3. Mobility reduction factor (fmmob) and reference dry out slope (sfbet) are seen to have the maximum effect in history matching of the pressure drop values measured in the lab. 4. The injection of surfactant solution initially and followed by co-injection of surfactant and gas is found to be one of the most efficient schemes in displacing oil from a heterogeneous reservoir.


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APPENDIX Foam models come in two groups: Population balance models and the semi empirical model[36, 70-72]. Population balance model represent the dynamics of bubble creation and destruction explicitly along with the bubble size on gas mobility. The second group represents the effects of bubble size implicitly through a gas mobility reduction, surfactant concentration and other factors[42, 73-78]. The simulator models foam based on a function FM which controls the reduction in gas mobility with the incorporation of Darcy’s law for the gas phase:

= − ∇ = − ∇

where is the gas relative permeability in the presence of foam and is the gas relative permeability without foam. The function FM is product of various other functions (F1, F2….F6) each of which is aimed at capturing different physical effects. The complete function FM is given by[42, 66] =

1 1 + . 1. 2. 3. 4. 5. 6. 7

The parameter fmmob is the reference gas mobility reduction factor for wet foams. If the simulator was meant to model dry foam then the formula would include another function which is represented as F7[6].


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