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The role of fines transport in low salinity waterflooding and hybrid recovery processes
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The role of fines transport in low salinity waterflooding and hybrid recovery processes ⁎
Ngoc Nguyena,b, Cuong Dangb, , Seyhan Emre Gorucub, Long Nghiemb, Zhangxin Chena a b
Department of Chemical and Petroleum Engineering, University of Calgary, Canada Computer Modelling Group Ltd., Canada
A R T I C LE I N FO
A B S T R A C T
Keywords: Low salinity waterflooding Fines migration Geochemistry EOR Hybrid EOR
Low Salinity Waterflooding (LSW) is a promising technology for improving oil recovery in secondary and tertiary stages over the conventional waterflooding (HSW). As widely reported in the literature, this emerging recovery approach has relatively low operating costs and is more environmentally-friendly than conventional Enhanced Oil Recovery (EOR) methods. In fact, LSW is a multi-physics recovery process; however, most of the previous studies focused on wettability modification as the sole mechanism for the incremental oil recovery by LSW. Unfortunately, these studies often ignored other crucial factors that importantly affect the performance of LSW, e.g., fines transport under a low salinity injection environment. This paper introduces a robust modeling workflow to capture important recovery mechanisms occurred in LSW by integrating fines migration, wettability alteration, and geochemistry. The results showed that fines transport including fines deposition, fines migration, and fines plugging plays an important role in LSW process. Fines plugging by LSW can cause formation damage, but it also can be used as a mobility control agent. The LSW model equipped with fines transport shows an excellent agreement for incremental oil recovery and pressure drop against laboratory coreflooding. Finally, a novel concept of hybrid low salinity assisted gas flooding was proposed to overcome the existing technical challenges associated with the conventional CO2 flooding.
1. Introduction Previous evaluations and implementations reported that Low Salinity Waterflooding (LSW) provided incremental oil recovery over the conventional high salinity waterflooding from both laboratory coreflooding and pilot tests worldwide, e.g., the Bastrykskoye and Zichebashskoe oil fields in Russia [1,5], the North Sea oil field [24], the Endicott oil field in Alaska, the Omar oil field in Syria, and the Clair oil field in Shetland Islands. Although LSW can increase oil production in both sandstone and carbonate reservoirs, the recovery mechanisms of LSW are complex and numerous hypotheses have been suggested by several research groups [44,28,3,41,30,4,25,16,36]. Up until now, three main mechanisms are considered as the principal mechanisms of a LSW process:
• Wettability alteration: multiple ion exchanges and pH changes were considered as the reason for wettability alteration during low salinity brine injection. Based on laboratory measurements, wettability often alters toward more water-wet or mixed-wet conditions. Wettability alteration was observed in both sandstone and carbonate
⁎
• •
reservoirs. Fines migration: clay particles are detached at a low salinity condition leading to changing the rock wettability from oil-wet to more water-wet. The fines migration and fines plugging can block high conductivity zones and consequently improve sweep efficiency. Interfacial tension (IFT) reduction: A pH change during LSW can decrease the oil–water IFT, consequently forming in-situ soap and increasing recovery factor.
Although LSW is a multi-physics process, most of the previous studies have focused on wettability alteration without addressing other important phenomena such as formation damage by fines transport. Besides, fines migration is important due to its effect on the flow in porous media; e.g., issues such as how much fines are detached and reattached to the rock, and their impact on the sweep efficiency in field applications must be addressed. Studies conducted by Tang et al. [44] and Yuan and Shapiro [50] showed that the fines migration was the reason for changing rock surfaces from mixed-wet or oil-wet to waterwet and diverting water flow path by fine straining in pore throats. Numerous studies have indicated that clay particles, especially
Corresponding author. E-mail address: [email protected] (C. Dang).
https://doi.org/10.1016/j.fuel.2019.116542 Received 12 July 2019; Received in revised form 9 October 2019; Accepted 29 October 2019 0016-2361/ © 2019 Elsevier Ltd. All rights reserved.
Please cite this article as: Ngoc Nguyen, et al., Fuel, https://doi.org/10.1016/j.fuel.2019.116542
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Nomenclature Aj D fij kj Kj ′ Ca KNa nc Ni p Pcog Pcwo q Qj rj t
Tj V Vdepo Vt Yij β1 β2 β3 β4 α γ δ vsw viw vcr Δt
reactive surface for reaction j depth fugacity of component I in phase j rate constant for reaction j equilibrium constant for reaction j ion-exchange selectivity coefficient number of components moles of component I per unit block volume pressure oil – gas capillary pressure water – oil capillary pressure injection/production rate activity product for reaction j reaction rate time
transmissibility of phase j gridblock volume volume fraction of deposited fines total fines volume fraction mole fraction of component I in phase j fines deposition coefficient fines migration coefficient fines plugging coefficient fines snow ball coefficient activity specific gravity ion-exchange equivalent fraction superficial water velocity interstitial water velocity critical water velocity timestep
implemented into GEMTM, a robust Equation-of-State compositional reservoir simulator developed by Computer Modelling Group Ltd. The important features of LSW modeling with fines transport are summarized as follows:
kaolinite, were often produced in LSW coreflooding experiments. A study by Russel et al. [42] showed that a permeability reduction after LSW for rocks containing high kaolinite was higher than the one in lowkaolinite content rocks. Zeinijahromi et al. [51] studied the effects of the movement of fines particles on water cut using an analytical model. The results showed that fines migration caused a delay in water breakthrough, and reduced the water cut during LSW in heterogeneous reservoirs. A higher heterogeneous reservoir has a larger reduction in injected water volume between high salinity waterflood and LSW. Yu et al. [47] confirmed fines detachment and migration during LSW using micro-CT images; their results showed that permeability was reduced by 50% during low salinity brine injection. Chequer et al. [13] developed an analytical model to predict a well injectivity decline during LSW based on the data obtained from laboratory experiments and concluded that the well injectivity was affected only in the near injector’s wellbore regions by permeability reduction. Moreover, the formation damaged area grew when the injection rate was higher or the salinity was lower. Yu et al. [48] investigated impacts of fines migration during LSW by performing corefloodings with different rock types at different salinities. Laboratory measurements showed that water relative permeability is reduced by 50% and an oil recovery factor is increased by 3% with the non-polar oil. These effects were solely attributed to fines transport. Several analytical and mathematical models have been proposed to simulate fines transport in porous media [51,25,5,6,14,7,13,12]. However, these analytical models often ignore other important factors that could lead to a biased evaluation of LSW at the field scale. This paper presents a comprehensive modeling workflow that allows simulating LSW with important physics including fines transport (deposition, migration, and plugging), geochemical reactions, multiple ion exchanges, and wettability alteration. The proposed model provides excellent matching results with laboratory core flooding. Additionally, the model developed can be efficiently extended to simulation of complex hybrid recovery processes, e.g., low salinity assisted CO2 flooding with presence of fines.
2.1. Flow equations: The material balance for the components in the oil and gas phases and for the water component are, respectively [8,9]: m ΔTom yiom (Δpn + 1 − γom ΔD) + ΔTgm yigm (Δpn + 1 + ΔPcog − γgm ΔD) + qim −
[Nin + 1 − Nin] = 0i = 1, ⋯, nc m Twm (Δpn + 1 − ΔPcwo − γwm ΔD) + qnmc + 1 −
V Δt (1)
V [Nnnc++11 − Nnnc + 1] = 0 ΔT
(2)
where Ni (i = 1, ⋯, nc ) denotes the moles of component i per unit of gridblock volume, and Nnc + 1 denotes the moles of water per unit of gridblock volume. The superscripts n and n + 1 denote the old and
2. Modeling LSW with fines transport The key elements in LSW modeling with the presence of fines are indicated in Fig. 1. In fact, LSW combines different mechanisms in a single recovery process, e.g., complex geochemistry, wettability alteration, and fines transport. These phenomena are related and can occur simultaneously. Nghiem et al.’s geochemical model [31,32] and Dang et al.’s [20] were applied to capture important physics that occur in LSW and a miscible CO2 flooding process, and Gorucu et al., [23] was utilized for fines transport modeling. All features have been
Fig. 1. Key elements for modeling LSW with fines transport. 2
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current time levels, respectively. The superscript m refers to n for explicit gridblocks and n + 1 for fully-implicit gridblocks. 2.2. Phase behavior The phase equilibria are based on the equality of the component fugacities:
fig − fio = 0
(3)
fig − fiw = 0
(4)
where fig is determined from the Peng-Robinson equation and fiw can be calculated using Henry’s law. Fig. 2. Permeability reduction versus injected salinity (data from [25]
2.3. Geochemical reactions Geochemistry plays an important role in LSW and the modeling workflow takes into account different geochemical reactions including multiple ion-exchanges, intra-aqueous reactions, and mineral dissolution and precipitation. In LSW, a low salinity brine slug is normally injected into a reservoir, consequently resulting in different ion-exchange reactions between the injected water and reservoir rock surfaces. This reaction is reversible and can be modeled using ion-exchange selectivity coeffi' cient KNa Ca [20]:
Na+ +
1 1 (Ca − X2 ) ↔ (Na − X ) + Ca++ 2 2
′ KNa
=
Ca
δNa [αCa ]0.5 [δCa ]0.5 αNa
permeability reduction, or formation damage, as shown in Fig. 2. Pang and Sharma [39] indicated that a permeability reduction is linearly proportional with plugging fines. Therefore, a permeability reduction was modeled as a function of volumetric plugged fines. In consistence with the laboratory observations, a permeability reduction is higher with increased amounts of plugged fines during the course of LSW. 2.5. Wettability alteration Numerous studies have reported that wettability alteration, e.g., from oil wetness to water wetness, was frequently observed in LSW for both sandstone and carbonate reservoirs. Laboratory measurements indicated that wettability alteration by LSW was associated with the modifications of a relative permeability’s endpoints and curvatures [45,26,40,21]. From these studies, several important factors, e.g., salinity and composition of the formation water and injection brine, reservoir rock lithology, original wettability condition, fines migration and plugging, can affect the changes in oil and water relative permeabilities and ultimately the incremental oil recovery by LSW. To model wettability alteration by LSW, multiple tables of relative permeability measured from the laboratory associated with different injected salinities are interpolated based on selected interpolant parameters. The modeling approach [10,11] allows a wide range of the interpolant parameters for relative permeability include aqueous phase molality (salinity), an ion-exchange equivalent fraction, mineral, and plugging fines. Additionally, artificial intelligent based multi-dimensional interpolation [18] can be utilized when several factors simultaneously affect the relative permeability modification.
(5)
(6)
where δNa and δCa are the equivalent ion-exchange fractions for sodium and calcium ions, respectively, and α denotes the activity. Equilibrium chemical reactions for the aqueous phase is based on the equality between the equilibrium constant (K) and the activity product (Q), whereas mineral rate reactions are calculated as [31]:
Qj ⎞ r j = Aj kj ⎜⎛1 − ⎟ Kj ⎠ ⎝
(7)
where, r j , Aj and kj are the reaction rate, reactive surface area, and rate constant, for reaction j, respectively. 2.4. Fines transport Fines transport was modeled using Gorucu et al.’s [23] approach in which fines transport (deposition, migration, and plugging) is taken into account during low salinity brine injection as follows:
∂Vt k = β1 c fk − β2 Vdepo (viw − νcr ) + β3 (1 + β4 Vtn ) vsw c fk ∂t
3. LSW coreflooding simulation with fines transport 3.1. Experimental description
(8)
Hussain et al. [25] investigated effects of fines migration and fines plugging on LSW performance by conducting different LSW’s corefloodings with Berea sandstone. The experiments were performed with synthetic Soltrol oil that excluded wettability alteration due to geochemistry. Thus this experiment has been selected to validate the fines transport model presented in this paper. The core plug dimension was 2.58 cm in diameter and 5.4 cm in length. Berea sandstone’s absolute permeability and porosity were 495 mD and 23%, respectively. The first experiment was used for single phase water injection, whereas the second and third experiments were used for actual low salinity brine injection. The third coreflood was only for confirming the reproducibility of the laboratory measurements; therefore, coreflooding 2 was selected to match the simulation results. HSW containing 40 g/l NaCl and LSW (fresh water) was injected at the initial water saturation (Swi) with the injection rate of about 15 cm3/hr. The experiment consisted of two sequential injections as shown in Fig. 3. The oil recovery and
where β1; β2 ; β3 ; β4 are the deposition, migration, plugging, and snow ball coefficients, respectively, vsw , viw , and vcr are the superficial, interstitial, and critical water velocities, respectively, Vt is the total fines volume fraction, and Vdepo is the volume fraction of deposited fines. In this model, fines deposition and migration are reversible, whereas fines plugging is irreversible. Initially, fines particles are in the reversible deposited form and these particles can be mobilized and plugged in small pore throats by changing water velocity and water salinity. As reported from the laboratory and field scales, salinity shock due to low salinity water injection can mobilize deposited fines. Therefore, the critical velocity for fine mobilization is modeled as a function of salinity. Additionally, fines plugging after migration can also cause a 3
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1 Oil injection until
3 Oil injection until
2 HSW until
4 LSW injection until
Fig. 3. Laboratory experimental procedures.
pressure change were monitored and the endpoints of relative permeability were measured and shown in Table 1.
a. Hussain’s pressure drop history ma A)
3.2. LSW coreflooding simulation The simulation was performed with a 1D model that consists of 50 gridblocks. The important physics for LSW mechanisms with fines transport have been included in the model, for example:
• Fines deposition • Fines migration • Fines plugging • Salinity dependence of critical velocity • Permeability reduction dependence of volumetric fines plugging • Relative permeability modification dependence of volume fines plugging • Geochemistry including ion exchanges between injection fluids and reservoir rock, mineral dissolution and deposition, and intra-aqueous reactions.
b.
As measured from laboratory data, relative permeability endpoints were slightly changed between HSW and LSW even though a synthetic oil was used in the experiment. This observation is similar to the measurements conducted by Yu et al. [48]. Both studies showed that fines were migrated and produced during LSW. A sharp increase in the pressure drop in all coreflooding experiments indicated that fines were mobilized and plugged into porous media under the effects of a low salinity environment. Additionally, laboratory studies also indicated that fines plugging can affect the relative permeability endpoints and curvatures. The measured relative permeability endpoints shown in Table 1 were used in the 1D coreflooding simulation. Hussain et al. [25] also attempted to match this coreflooding experiment using an analytical model originally proposed by Zeinijahromi et al. [52]. This model is primarily based on maximum retention of movable in-situ fines by LSW. As reported in Hussain et al. [25], this model gave an adequate match against laboratory production profile of LSW with fines transport. However, it cannot capture the trend of the laboratory data around the breakthrough points, as shown in Fig. 4a. More importantly, Hussain’s matching approach used an unusual relative permeability curve for LSW in which water relative permeability tended to increase with water saturation, and then decreased when water saturation reached the value of approximately 0.58 (Fig. 4b). This behavior was obtained from a history matching
Fig. 4. Hussain’s (2014) LSW coreflooding matching.
procedure by minimizing the differences between measured and predicted data; however, such a relative permeability behavior has been rarely reported in the past studies. Thus, one of the objectives in this study was to use the newly proposed model to reproduce the trend of lab data with conventional relative permeability behavior. Note that the endpoints of relative permeability were fixed as similar as the measured data, while fines transport coefficients and relative permeability exponents were history matching parameters against laboratory data. The first validation was conducted to compare LSW simulation results without modeling fines transport against laboratory measured data. As shown in Fig. 5, simulation results were not matched with the trend of the experimental data in this case. Yu et al.’s [48] experimental studies indicated that a relative permeability modification occurred when fines particles were plugged in porous media by injecting low salinity brine into non-polar saturated oil cores. This phenomenon is not represented without fines transport modeling; therefore, the
Table 1 Laboratory measured relative permeability endpoints for HSW and LSW [25]
HSW LSW
Initial water saturation (Swi)
Residual Oil Saturation (Sor)
Oil Relative Permeability at Swi
Water Relative Permeability at Sor
0.293 0.293
0.415 0.402
0.76 0.757
0.13 0.074
4
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Fig. 5. Experimental and calculated oil recovery factor for Hussain’s coreflooding without fines transport modeling.
Fig. 6. Experimental and calculated pressure drop for Hussain’s coreflooding without fines transport modeling.
Fig. 7. Experimental and calculated oil recovery factors for Hussain’s coreflooding with fines transport modeling.
normal trend of high salinity waterflooding in which pressure increases until the water breakthrough point and then decreases afterwards. As observed from the past studies and in this particular coreflooding data, the pressure drop significantly increased during LSW due to a
simulation cannot capture the incremental oil recovery by LSW. In addition, without modeling fines migration and fines plugging, the simulation was also not able to capture the change in pressure drop (Fig. 6). The pressure drop profile from LSW simulation follows the 5
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Fig. 8. Experimental and calculated pressure drops for Hussain’s coreflooding with fines transport modeling.
permeability reduction by fines migration and pore throats plugging [53], 2010, [46,25,48]. Unsurprisingly, the pressure drop observed from simulation without fines transport was much lower than the one measured in the laboratory. In other words, it is not possible to capture an increase in oil recovery and pressure drop during the course of LSW for this coreflood experiment without considering fines transport. In the second validation, the proposed model equipped with fines transport phenomena was applied to match the production profile of LSW for Hussain’s coreflooding experiment. The modification of relative permeability was modeled as a function of volumetric plugged fines concentration. As shown in Fig. 7, an excellent agreement with the coreflooding data for oil production and the final recovery factor was achieved by including fines transport in the simulation. Additionally, the proposed fines transport model successfully captured the trend of around the breakthrough period in which the pressure drop slightly decreased before increasing sharply (Fig. 8). This behavior of pressure drop was reproduced in a sister core plug and it was important that the proposed model efficiently improved the matching results for the pressure drop compared to the past studies. Additionally, history matching results were obtained with the conventional relative permeability curves, as shown in Fig. 9, without requiring an unusual drop of water relative permeability at the high values of water saturation. This is more consistent with the majority measurements of relative permeability for LSW reported in the literature.
Fig. 9. Relative permeability curve used for matching Hussain’s coreflooding with fines transport modeling. Table 2 Porosity and permeability of each geological unit.
Average porosity(%) Average permeability(mD) Thickness(m)
U1
U2
U3
15 300 10
12 75 10
10 15 10
4. Concept of LSW assisted CO2 flooding Previous studies reported that LSW can cause formation damage by
a. Porosity
b. Permeabbility
Fig. 10. 3D Porosity and permeability distributions. 6
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Fig. 11. 3D reservoir model with Well Pattern.
showed that a combination of LSW with CO2 WAG (water alternating gas) yielded about 4.5% incremental original oil in place over the conventional WAG. Al-Shalabi et al. [2] reported that the combined CO2 flooding and LSW promotes the EOR synergies, leading to a higher oil recovery factor in carbonate reservoirs. However, LSW’s fines transport was normally not included in the simulation workflow; this section aimed to provide an evaluation of LSW assisted CO2 flooding with presence of fines migration and fines plugging. CO2 flooding has been widely applied in a tertiary recovery stage; however, the ultimate recovery factor is reduced by the high mobility of the injected gas, especially in heterogonous permeability reservoirs [35,37]. Viscous fingering caused by unfavorable mobility of CO2 can significantly reduce the volumetric sweep efficiency leading to a poor performance of a CO2-EOR project. On the contrary, LSW can work as a mobility control agent by mobilizing fines particles and reducing the gas mobility; therefore, LSW followed by CO2 flooding promotes the synergy between these two EOR methods. The concept of LSW assisted CO2 flooding can be summarized below:
Table 3 Oil Property used in Reservoir Simulation. Components
Mole Fraction
CO2 N2 to CH4 C2H to NC4 IC5 to C07 C08 to C12 C13 to C19 C20 to C30
0.01183 0.11702 0.194538 0.220253 0.280506 0.0939775 0.0808749
Table 4 Reservoir and Injected Water Salinity Molalities.
Na
+
Formation
LSW
0.4892
0.01196
mobilizing and plugging fines in porous media; however, LSW can also be used for mobility control in heterogeneous reservoirs [12,15,17,43,22,25,27,29,38,44,52,49]. Nguyen et al. [33] indicated that fines mobilization with a permeability decline by LSW help to enhance gas recovery during reservoir depletion. Dang et al. [19]
• Low salinity brine injection to promote favorable wettability alteration and to mobilize fines particles, • Fines particles will be moving and plugging in high conductivity zones, and
Fig. 12. LSW vs. HSW at field scale. 7
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Fig. 13. Role of fines transport in LSW at the field scale.
Fig. 14. LSW + CO2 flooding vs. HSW + CO2 flooding at field scale.
• The following injected CO
injectors at the corners of the reservoir and 10 vertical producers inside the reservoir as shown in Fig. 11. Important physics for fines transport, geochemistry, and wettability alteration were incorporated in the model to evaluate LSW performance. Critical velocity and permeability reductions were modeled as functions of salinity and volumetric plugging fines, respectively. Oil compositions and fluid prperties are shown in Tables 3 and 4.
2 slug will be partially diverted into unswept layers and improve the final oil recovery factor.
4.1. Geological and reservoir modeling A geological model consists of 3 geological units with 15 vertical layers. The porosity and permeability of each unit were geostatistically generated and controlled by field histograms and variograms data. The average porosity and permeability in each geological unit are indicated in Table 2. Fig. 10 shows the 3D distributions of the porosity and permeability for the generated geological model. The grid size of 50x30x15 in the I-, J-, and K-directions, respectively, was used in reservoir simulation. The model consists of 4 vertical
4.2. LSW versus conventional waterflooding In the first study, HSW and LSW were separately injected for about one year in 4 the injectors. The simulation captures the crucial roles of both wettability alteration and formation damage during LSW process. 8
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Fig. 15. Oil saturation maps after LSW + CO2 flooding vs. HSW + CO2 flooding at the field scale.
a. Layer 1
Fig. 16. Facies distribution in a channeling model (yellow – channels; grey – background). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
b. Layer 15 Fines particles were mobilized with the propagation of low salinity brine slugs and plugged into the high permeability geological layers, and consequently decreased permeability as observed in both laboratory and pilot tests. Yu et al. [48] reported that absolute permeability reduced approximately 6 times when the injected brine salinity decreased from 40 g/l to fresh water. To the best of our knowledge, our study is the first LSW modeling workflow that can comprehensively evaluate both positive and negative impacts, favorable wettability alteration versus formation damage, of LSW at the field scale. Fig. 12 shows that LSW provided about 4.6% incremental oil recovery over HSW due to more water-wetness alteration. This result is similar to the observations obtained from pilot tests reported in the literature. With the new capabilities in modeling fines transport under low salinity brine injection, it is now possible to track movement paths and amounts of fines migration and fines plugging in a reservoir. Fig. 13 shows a cross section view where fines were mobilized around the
Fig. 17. Permeability distribution in the channeling model.
injectors and moved along the high permeability zones. A portion of these migrated fines were plugged in pore throats, and led to a reduction in permeability at the near injection wellbore regions. The simulation results are useful to monitor the formation damage caused by low salinity brine injection and effectively assist reservoir engineers to achieve a better design for LSW implementation. 4.3. Low salinity assisted CO2 flooding To evaluate the benefits of LSW assisted CO2 gas flooding, the simulation presented in the previous section was extended by continuously injecting CO2 after LSW or HSW. Fig. 14 shows that LSW followed by CO2 flooding provides higher oil recovery compared to the 9
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Fig. 18. Recovery factor of LSW + CO2 flooding vs. HSW + CO2 in channel reservoir.
HSW + CO2 Flooding
LSW + CO2 Flooding
Mid-CO2 Injection – Layer 5
Mid-CO2 Injection – Layer 5
End-CO2 Injection – Layer 5
End-CO2 Injection – Layer 5
Fig. 19. Gas saturation maps after LSW + CO2 vs. HSW + CO2 in channel reservoir.
one by HSW plus CO2 flooding. Although LSW and CO2 flooding experiences a short delay in the early stage due to a permeability decline, this hybrid method helps to produce more oil by blocking the high permeability zones near the 4 injection wells. The advantages of LSW assisted CO2 flooding is clearly illustrated from the oil saturation cross section shown in Fig. 15. In the conventional high salinity CO2 flooding approach, it was only possible to produce oil in the top layers with high permeability values, and most of the reservoir was left behind after CO2 flooding with high remaining oil saturation. Additionally, further CO2 injection was ineffective as there was a very slight change in the oil saturation maps at the middle and the end of the CO2 flooding project. On the other hand, oil saturation was significantly lower in the case of LSW followed by CO2 flooding as the result of a permeability reduction in the top reservoir layers. Injected gas flow was successfully diverted into lower permeability layers and consequently led to a higher recovery factor. This demonstrates the merits of combining LSW with the conventional CO2 flooding to
overcome the existing technical challenges. 5. LSW assisted CO2 flooding with presence of channels The new hybrid recovery process combining LSW and CO2 flooding was applied in a channelling reservoir which is one of the most difficult reservoir types for EOR implementation. These reservoirs usually consist of complex channel systems with very high permeability and the injection fluids can quickly move along these channels, leading to a poor performance of an EOR project [34]. As a mobility control agent, LSW was applied to reduce the permeability of the channels, and therefore, improved the sweep efficiency of the following CO2 slug. A geological model consisted of 15 layers with two facies as shown in Fig. 16. Channels were geostatistically distributed in the model based on field data. These channels have significantly higher permeability values than the one in background. The average permeability for the channels and background are 500 mD and 75 mD, respectively 10
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HSW + CO2 Flooding
LSW + CO2 Flooding
Mid-CO2 Injection – Layer 5
Mid-CO2 Injection – Layer 5
End-CO2 Injection – Layer 5
End-CO2 Injection – Layer 5
Fig. 20. Oil saturation maps after LSW + CO2 flooding vs. HSW + CO2 in channel reservoir.
Acknowledgments
(Fig. 17). The model consists of 4 vertical injectors at the corners of the reservoir and 10 vertical producers inside the reservoir. Fines transport, geochemistry, and wettability alteration were incorporated into the model similarly to the previous case. Either low salinity brine or high salinity brine was injected for about 1 year and followed by a continuous CO2 injection. Similar to the previous case, LSW is effective in mobilizing fines and reducing permeability of the channels. The hybrid LSW plus CO2 flooding provided about 7.5% incremental oil production over the conventional CO2 flooding implementation (Fig. 18). Figs. 19 and 20 show that the injected gas was quickly produced and left behind a large un-swept zone in the conventional recovery method, while the hybrid method efficiently helps to improve the sweep efficiency of CO2 flooding. This demonstrates the advantages of low salinity CO2 flooding over the conventional approach and this new recovery strategy can be applied to unlock hydrocarbon reserves in reservoirs with a complex channel system.
The authors would like to acknowledge Dr. Vijay Shrivastava of Computer Modelling Group Ltd. for many fruitful technical discussions. The authors also thank Emerson-Paradigm for providing the GOCAD™ software for this research. The compositional simulator GEM™ and the optimization software CMOST™ from Computer Modelling Group Ltd. were used in this paper. Part of the research is supported by NSERC/ Energi Simulation and Alberta Innovates Chairs. References [1] Ahmetgareev V, Zeinijahromi A, Badalyan A, Khisamov R, Bedrikovetsky P. Analysis of low salinity waterflooding in bastrykskoye field. Pet Sci Technol 2015;33:561–70. [2] Al-Shalabi E, Sepehrnoori K, Pope G. Numerical modeling of combined low salinity water and carbon dioxide in carbonate cores. J Petrol Sci Eng 2016;137:157–71. [3] Austad T. “Smart Water’ for enhanced oil recovery: a comparison of mechanisms in carbonates and sandstones. NPD, Stavanger: The presentation presented at the FORCE seminar on Low Salinity; 2008. [4] Austad T., Rezaeidoust A., Puntervold T., 2010. Chemical mechanism of low salinity water flooding in sandstone reservoirs. In: SPE 129767 presented at the SPE IOR Symposium, Tulsa, OK, USA, 24-28 April. [5] Bedrikovetsky P, Zeinijahromi A, Badalyan A, Ahmetgareeve V, Khisamov R. Finesmigration-assisted low-salinity waterflooding: field case analysis. SPE 176721 presented at the SPE Russian Petroleum Technology Conference held in Moscow. 2015. [6] Borazjani S, Behr A, Genolet L, Kowollik P, You Z, Bedrikovetsky P. Low-salinity fines-assisted waterflooding: multiscale analytical and numerical modeling. SPE 186070 presented at the SPE Reservoir Characterisation and Simulation Conference held in Abu Dhabi. 2017. [7] Borazjani S, Chequer L, Russell T, Bedrikovetsky P. Injectivity decline during waterflooding and PWRI due to fines migration. SPE 189521 presented at the SPE international conference and exhibition on formation damage control held in. 2018. [8] Chen Z., Huan G. and Ma Y., 2006. Computational Methods for Multiphase Flows in Porous Media. Computational Science and Engineering Series, Vol. 2, SIAM, Philadelphia. [9] Chen Z., 2007. Reservoir Simulation: Mathematical Techniques in Oil Recovery. CBMS-NSF Regional Conference Series in Applied Mathematics, Vol. 77, SIAM, Philadelphia. [10] Chen Z, Ewing RE. From single-phase to compositional flow: applicability of mixed finite elements. Transp Porous Media 1997;27:225–42. [11] Chen Z, Ewing RE, Jiang Q, Spagnuolo AM. Error analysis for characteristics-based methods for degenerate parabolic problems, SIAM. J. Numer. Anal. 2002;40:1491–515.
6. Conclusions This paper presents a new LSW simulation workflow that captures important physics of the LSW process including fines transport, geochemistry, and wettability alteration. With the new capabilities of modeling different fines transport phenomena (deposition, migration and plugging), it is now possible to achieve a better understanding and evaluation of LSW and hybrid processes in terms of formation damage and low salinity brine flow in porous media. The simulation results are strongly in agreement with the ones obtained from the laboratory. Numerical simulation results show that low salinity assisted CO2 flooding is an emerging attractive EOR technology in which LSW can help to mobilize fines and reduce permeability in high conductivity zones and significantly improve the sweep efficiency of CO2 flooding. This hybrid method outperformed the conventional CO2 flooding in terms of higher ultimate recovery factor for different types of reservoirs.
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[12] Chequer L, Al-Shuaili K, Genolet L, Behr A, Kowollik P, Zeinijahromi A. Optimal slug size for enhanced recovery by low-salinity waterflooding due to fines migration. J Petrol Sci Eng 2019;177:766–85. [13] Chequer L, Vaz A, Bedrikovetsky P. Injectivity decline during low-salinity waterflooding due to fines migration. J Petrol Sci Eng 2018;165:1054–72. [14] Coronado M, Di’az-Viera M.A.,. Modeling fines migration and permeability loss caused by low salinity in porous media. J Petrol Sci Eng 2017;150:355–65. [15] Dang C., Nghiem L., Chen Z., Nguyen N., 2013. State-of-the art smart low salinity waterflooding for enhanced oil recovery. In: SPE 165903 presented at the SPE Asia Pacific Oil & Gas Conference and Exhibition, Jakarta, Indonesia, 22-14 October. [16] Dang C., Nghiem L., Nguyen N., Chen Z., 2015. New insights into the critical role of geology in modeling and prediction of low salinity waterflooding. In: Paper SPE 174294 presented at the SPE EUROPEC conference, Madrid, Spain, 1 -4 June. [17] Dang C, Nghiem L, Nguyen N, Chen Z, Yang C, Nguyen Q. A framework for assisted history matching and robust optimization of low salinity waterflooding under geological uncertainties. J Petrol Sci Eng 2017;152(2017):330–52. [18] Dang C, Nghiem L, Fedutenko E, Gorucu E, Yang C, Mirzabozorg A. Application of artificial intelligence for mechanistic modeling and probabilistic forecasting of hybrid low salinity chemical flooding. Paper SPE 191474 presented at the SPE Annual Technical Conference and Exhibition. 2018. [19] Dang C, Nghiem L, Nguyen N, Chen Z, Nguyen Q. Evaluation of CO2 low salinity water-alternating-gas for enhanced oil recovery. J Nat Gas Sci Eng 2016;35:237–58. [20] Dang C, Nghiem L, Nguyen N, Chen Z, Nguyen Q. Mechanistic modeling of low salinity waterflooding. J Petrol Sci Eng 2016;146:191–209. [21] Fjelde, I., Asen, S.V., Omekeh, A. 2012. Low salinity water flooding experiments and interpretation by simulations. In: Paper SPE 154142 presented at the Eighteenth SPE Improved Oil Recovery Symposium, Tulsa, OK, USA, April 14-18. [22] Fogden A, Kumar M, Morrow NR, Buckley JS. Mobilization of fine particles during flooding of sandstones and possible relations to enhanced oil recovery. Energy Fuels 2011;25:1605–16. [23] Gorucu S.E., Dang C., Nghiem L., and Shrivastava V., 2019. Modeling and optimization of low salinity waterflood with fines transport. SPE 195901 presented at the SPE ATCE Conference and Exhibition, Calgary, AB, Canada, 30 September – 2 October. [24] Hughes D, Larsen S, Wright R. Review of low salinity water flooding. Final report. for DECC, www.snergyworld.com; 2010. [25] Hussain F, Zeinijahromi A, Bedrikovetsky P, Badalyan A, Carageorgos T, Cinar Y. Enhanced oil recovery through low salinity fines-assisted waterflooding: laboratory and mathematical modeling. SPE 171525 presented at the SPE Asia Pacific Oil & Gas Conference and Exhibition held in. 2014. [26] Kulkarni MM, Rao DN. Experimental investigation of miscible and immiscible wag process performance. J Petrol Sci Eng 2005;48(2005):1–20. [27] Kumar M, Fogden A, Morrow NR, Buckley JS. Mechanisms of improved oil recovery from sandstone by low salinity flooding. Petrophysics 2011;52:428. [28] Lager A., Webb K.L., Black C.J.J., Singleton, 2006. Low salinity oil recovery – an experimental investigation. In: SCA2006-36 presented at the International Symposium of the Society of Core Analysts, Trondheim, Norway, 12-16 September. [29] Li Y. Oil recovery by low salinity water injection into a reservoir: a new study of tertiary oil recovery mechanism. Transport Porous Media 2011;90:333–62. [30] Ligthelm D.J., Gronsveld J., Hofman J., Brussee N., Marcellis F., and van der Linde H., 2009. Novel waterflooding strategy by manipulation of injection brine composition. In: SPE 119835 presented at the EUROPEC/EAGE Conference and Exhibition held in Amsterdam, The Netherlands, 8-11 June. [31] Nghiem L., Sammon P., Grabenstetter J., Ohkuma H. 2004. Modeling CO2 storage in aquifers with fully-coupled geochemical eos compositional simulator. In: Paper SPE 89474 presented at the SPE Fourteenth Symposium on Improved Oil Recovery, Tulsa, OK, USA, April 17-21. [32] Nghiem L, Shrivastava V, Kohse B. Modeling aqueous phase behavior and chemical
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[35]
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[47]
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reactions in compositional simulation. Paper SPE 141417 presented at the SPE Reservoir Simulation Symposium, The Woodlands, TX, USA. 2011. Nguyen P, Zeinijahromi A, Bedrokovetsky P. Fines-migration-assisted improved gas recovery during gas field depletion. J Petrol Sci Eng 2013;109:26–37. Nguyen N, Chen Z, Nghiem L, Dang C, Yang C. A new approach for optimization and uncertainty assessment of surfactant-polymer flooding. Paper SPE 172003 presented at the SPE Abu Dhabi International Petroleum Exhibition and Conference, Abu Dhabi. 2014. Nguyen N., Dang C., Chen Z., Nghiem L. 2015. Effects of Lithofacies and Reservoir Heterogeneity on Improved Oil Recovery Processes. In: Paper SPE 174473 presented at the SPE Canada Heavy Oil Technical Conference, Calgary, AB, Canada, June 9 – 11. Nguyen N, Dang C, Nghiem L, Chen Z. Geochemical Interpretation and Field Scale Optimization of Low Salinity Waterflooding. Paper SPE 180107 presented at the SPE EUROPEC conference, Vienna, Austria. 2016. Nguyen N, Dang C, Chen Z, Nghiem L. Geological uncertainty and effects of depositional sequence on improved oil recovery processes. J Pet Explor Prod Technol 2016;6(4):801–13. Oliveira MA, Vaz AS, Siqueira FD, Yang Y, You Z, Bedrikovetsky P. Slow migration of mobilised fines during flow in reservoir rocks: laboratory study. J Petrol Sci Eng 2014;122:534–41. Pang S, Sharma M. A model for predicting injectivity decline in water-injection wells. SPE Form Eval 1997;12(03):194–201. Rivet SM, Lake LW, Pope GA. A coreflood investigation of low salinity enhanced oil recovery. Paper SPE 134297 presented at the SPE ATCE, Florence, Italy. 2010. Rivet SM. Core flooding oil displacements with low salinity brine. MSc: Thesis at The University of Texas; 2009. Russell T, Pham D, Neishaboor MT, Badalyan A, Behr A, Genolet L, et al. Effects of kaolinite inrocks on fines migration. J Gas Sci Eng 2017;45:243–55. Sarkar AK, Sharma MM. Fines migration in two-phase flow. J Petrol Technol 1990;42:646–52. Tang GQ, Morrow NR. Influence of brine composition and fines migration on crude oil/brine/rock interactions and oil recovery. J Petrol Sci Eng 1999;24:99–111. Webb K.J., Lager A., Black C.J.J. 2008. Comparison of high/low salinity water/oil relative permeability. In: Presented at the International Symposium of the Society of Core Analysts, Abu Dhabi, UAE, 29 October – 2 November. Winoto W, Loahardjo N, Xie X, Yin P, Morrow N. Secondary and tertiary recovery of crude oil from outcrop and reservoir rocks by low salinity waterflooding. SPE 154209 presented at SPE IOR Conference, Tulsa, OK, USA. 2012. Yu M, Hussain F, Arns JY, Bedrikovetsky P, Genolet L, Behr A, et al. Imaging analysis of fines migration during water flow with salinity alteration. Adv Water Resour 2018;121:150–61. Yu M, Zeinijahromi A, Bedrikovetsky P, Genolet L, Behr A, Kowollik P, et al. Effects of fines migration on oil displacement by low-salinity water. J Petrol Sci Eng 2019;175(2019):665–80. Yuan B, Moghanloo RG. Nanofluid pre-treatment, an effective strategy to improve the performance of low-salinity waterflooding. J Petrol Sci Eng 2018;165:978–91. Yuan H, Shapiro AA. Induced migration of fines during waterflooding in communicating layer-cake reservoirs. J Petrol Sci Eng 2011;78(3):618–26. Zeinijahromi A, Lemon P, Bedrikovetsky P. Effects of induced migration of fines on water cut during waterflooding. SPE 139239 presented at the SPE Middle East Oil and Gas Show and Conference held in Manama, Bahrain. 2011. Zeinijahromi A, Nguyen TKP, Bedrikovetsky P. Mathematical model for fines migration-assisted waterflooding with induced formation damage. SPE J. 2013;18(03):518–33. Zhang Y., Morrow N., 2006. Comparison of secondary and tertiary recovery with change in injection brine composition for crude oil/sandstone combinations. In: SPE 99757 presented at the SPE IOR Conference, Tulsa, OK, 22-26 April.
Contents lists available at ScienceDirect
Fuel journal homepage: www.elsevier.com/locate/fuel
The role of fines transport in low salinity waterflooding and hybrid recovery processes ⁎
Ngoc Nguyena,b, Cuong Dangb, , Seyhan Emre Gorucub, Long Nghiemb, Zhangxin Chena a b
Department of Chemical and Petroleum Engineering, University of Calgary, Canada Computer Modelling Group Ltd., Canada
A R T I C LE I N FO
A B S T R A C T
Keywords: Low salinity waterflooding Fines migration Geochemistry EOR Hybrid EOR
Low Salinity Waterflooding (LSW) is a promising technology for improving oil recovery in secondary and tertiary stages over the conventional waterflooding (HSW). As widely reported in the literature, this emerging recovery approach has relatively low operating costs and is more environmentally-friendly than conventional Enhanced Oil Recovery (EOR) methods. In fact, LSW is a multi-physics recovery process; however, most of the previous studies focused on wettability modification as the sole mechanism for the incremental oil recovery by LSW. Unfortunately, these studies often ignored other crucial factors that importantly affect the performance of LSW, e.g., fines transport under a low salinity injection environment. This paper introduces a robust modeling workflow to capture important recovery mechanisms occurred in LSW by integrating fines migration, wettability alteration, and geochemistry. The results showed that fines transport including fines deposition, fines migration, and fines plugging plays an important role in LSW process. Fines plugging by LSW can cause formation damage, but it also can be used as a mobility control agent. The LSW model equipped with fines transport shows an excellent agreement for incremental oil recovery and pressure drop against laboratory coreflooding. Finally, a novel concept of hybrid low salinity assisted gas flooding was proposed to overcome the existing technical challenges associated with the conventional CO2 flooding.
1. Introduction Previous evaluations and implementations reported that Low Salinity Waterflooding (LSW) provided incremental oil recovery over the conventional high salinity waterflooding from both laboratory coreflooding and pilot tests worldwide, e.g., the Bastrykskoye and Zichebashskoe oil fields in Russia [1,5], the North Sea oil field [24], the Endicott oil field in Alaska, the Omar oil field in Syria, and the Clair oil field in Shetland Islands. Although LSW can increase oil production in both sandstone and carbonate reservoirs, the recovery mechanisms of LSW are complex and numerous hypotheses have been suggested by several research groups [44,28,3,41,30,4,25,16,36]. Up until now, three main mechanisms are considered as the principal mechanisms of a LSW process:
• Wettability alteration: multiple ion exchanges and pH changes were considered as the reason for wettability alteration during low salinity brine injection. Based on laboratory measurements, wettability often alters toward more water-wet or mixed-wet conditions. Wettability alteration was observed in both sandstone and carbonate
⁎
• •
reservoirs. Fines migration: clay particles are detached at a low salinity condition leading to changing the rock wettability from oil-wet to more water-wet. The fines migration and fines plugging can block high conductivity zones and consequently improve sweep efficiency. Interfacial tension (IFT) reduction: A pH change during LSW can decrease the oil–water IFT, consequently forming in-situ soap and increasing recovery factor.
Although LSW is a multi-physics process, most of the previous studies have focused on wettability alteration without addressing other important phenomena such as formation damage by fines transport. Besides, fines migration is important due to its effect on the flow in porous media; e.g., issues such as how much fines are detached and reattached to the rock, and their impact on the sweep efficiency in field applications must be addressed. Studies conducted by Tang et al. [44] and Yuan and Shapiro [50] showed that the fines migration was the reason for changing rock surfaces from mixed-wet or oil-wet to waterwet and diverting water flow path by fine straining in pore throats. Numerous studies have indicated that clay particles, especially
Corresponding author. E-mail address: [email protected] (C. Dang).
https://doi.org/10.1016/j.fuel.2019.116542 Received 12 July 2019; Received in revised form 9 October 2019; Accepted 29 October 2019 0016-2361/ © 2019 Elsevier Ltd. All rights reserved.
Please cite this article as: Ngoc Nguyen, et al., Fuel, https://doi.org/10.1016/j.fuel.2019.116542
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Nomenclature Aj D fij kj Kj ′ Ca KNa nc Ni p Pcog Pcwo q Qj rj t
Tj V Vdepo Vt Yij β1 β2 β3 β4 α γ δ vsw viw vcr Δt
reactive surface for reaction j depth fugacity of component I in phase j rate constant for reaction j equilibrium constant for reaction j ion-exchange selectivity coefficient number of components moles of component I per unit block volume pressure oil – gas capillary pressure water – oil capillary pressure injection/production rate activity product for reaction j reaction rate time
transmissibility of phase j gridblock volume volume fraction of deposited fines total fines volume fraction mole fraction of component I in phase j fines deposition coefficient fines migration coefficient fines plugging coefficient fines snow ball coefficient activity specific gravity ion-exchange equivalent fraction superficial water velocity interstitial water velocity critical water velocity timestep
implemented into GEMTM, a robust Equation-of-State compositional reservoir simulator developed by Computer Modelling Group Ltd. The important features of LSW modeling with fines transport are summarized as follows:
kaolinite, were often produced in LSW coreflooding experiments. A study by Russel et al. [42] showed that a permeability reduction after LSW for rocks containing high kaolinite was higher than the one in lowkaolinite content rocks. Zeinijahromi et al. [51] studied the effects of the movement of fines particles on water cut using an analytical model. The results showed that fines migration caused a delay in water breakthrough, and reduced the water cut during LSW in heterogeneous reservoirs. A higher heterogeneous reservoir has a larger reduction in injected water volume between high salinity waterflood and LSW. Yu et al. [47] confirmed fines detachment and migration during LSW using micro-CT images; their results showed that permeability was reduced by 50% during low salinity brine injection. Chequer et al. [13] developed an analytical model to predict a well injectivity decline during LSW based on the data obtained from laboratory experiments and concluded that the well injectivity was affected only in the near injector’s wellbore regions by permeability reduction. Moreover, the formation damaged area grew when the injection rate was higher or the salinity was lower. Yu et al. [48] investigated impacts of fines migration during LSW by performing corefloodings with different rock types at different salinities. Laboratory measurements showed that water relative permeability is reduced by 50% and an oil recovery factor is increased by 3% with the non-polar oil. These effects were solely attributed to fines transport. Several analytical and mathematical models have been proposed to simulate fines transport in porous media [51,25,5,6,14,7,13,12]. However, these analytical models often ignore other important factors that could lead to a biased evaluation of LSW at the field scale. This paper presents a comprehensive modeling workflow that allows simulating LSW with important physics including fines transport (deposition, migration, and plugging), geochemical reactions, multiple ion exchanges, and wettability alteration. The proposed model provides excellent matching results with laboratory core flooding. Additionally, the model developed can be efficiently extended to simulation of complex hybrid recovery processes, e.g., low salinity assisted CO2 flooding with presence of fines.
2.1. Flow equations: The material balance for the components in the oil and gas phases and for the water component are, respectively [8,9]: m ΔTom yiom (Δpn + 1 − γom ΔD) + ΔTgm yigm (Δpn + 1 + ΔPcog − γgm ΔD) + qim −
[Nin + 1 − Nin] = 0i = 1, ⋯, nc m Twm (Δpn + 1 − ΔPcwo − γwm ΔD) + qnmc + 1 −
V Δt (1)
V [Nnnc++11 − Nnnc + 1] = 0 ΔT
(2)
where Ni (i = 1, ⋯, nc ) denotes the moles of component i per unit of gridblock volume, and Nnc + 1 denotes the moles of water per unit of gridblock volume. The superscripts n and n + 1 denote the old and
2. Modeling LSW with fines transport The key elements in LSW modeling with the presence of fines are indicated in Fig. 1. In fact, LSW combines different mechanisms in a single recovery process, e.g., complex geochemistry, wettability alteration, and fines transport. These phenomena are related and can occur simultaneously. Nghiem et al.’s geochemical model [31,32] and Dang et al.’s [20] were applied to capture important physics that occur in LSW and a miscible CO2 flooding process, and Gorucu et al., [23] was utilized for fines transport modeling. All features have been
Fig. 1. Key elements for modeling LSW with fines transport. 2
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current time levels, respectively. The superscript m refers to n for explicit gridblocks and n + 1 for fully-implicit gridblocks. 2.2. Phase behavior The phase equilibria are based on the equality of the component fugacities:
fig − fio = 0
(3)
fig − fiw = 0
(4)
where fig is determined from the Peng-Robinson equation and fiw can be calculated using Henry’s law. Fig. 2. Permeability reduction versus injected salinity (data from [25]
2.3. Geochemical reactions Geochemistry plays an important role in LSW and the modeling workflow takes into account different geochemical reactions including multiple ion-exchanges, intra-aqueous reactions, and mineral dissolution and precipitation. In LSW, a low salinity brine slug is normally injected into a reservoir, consequently resulting in different ion-exchange reactions between the injected water and reservoir rock surfaces. This reaction is reversible and can be modeled using ion-exchange selectivity coeffi' cient KNa Ca [20]:
Na+ +
1 1 (Ca − X2 ) ↔ (Na − X ) + Ca++ 2 2
′ KNa
=
Ca
δNa [αCa ]0.5 [δCa ]0.5 αNa
permeability reduction, or formation damage, as shown in Fig. 2. Pang and Sharma [39] indicated that a permeability reduction is linearly proportional with plugging fines. Therefore, a permeability reduction was modeled as a function of volumetric plugged fines. In consistence with the laboratory observations, a permeability reduction is higher with increased amounts of plugged fines during the course of LSW. 2.5. Wettability alteration Numerous studies have reported that wettability alteration, e.g., from oil wetness to water wetness, was frequently observed in LSW for both sandstone and carbonate reservoirs. Laboratory measurements indicated that wettability alteration by LSW was associated with the modifications of a relative permeability’s endpoints and curvatures [45,26,40,21]. From these studies, several important factors, e.g., salinity and composition of the formation water and injection brine, reservoir rock lithology, original wettability condition, fines migration and plugging, can affect the changes in oil and water relative permeabilities and ultimately the incremental oil recovery by LSW. To model wettability alteration by LSW, multiple tables of relative permeability measured from the laboratory associated with different injected salinities are interpolated based on selected interpolant parameters. The modeling approach [10,11] allows a wide range of the interpolant parameters for relative permeability include aqueous phase molality (salinity), an ion-exchange equivalent fraction, mineral, and plugging fines. Additionally, artificial intelligent based multi-dimensional interpolation [18] can be utilized when several factors simultaneously affect the relative permeability modification.
(5)
(6)
where δNa and δCa are the equivalent ion-exchange fractions for sodium and calcium ions, respectively, and α denotes the activity. Equilibrium chemical reactions for the aqueous phase is based on the equality between the equilibrium constant (K) and the activity product (Q), whereas mineral rate reactions are calculated as [31]:
Qj ⎞ r j = Aj kj ⎜⎛1 − ⎟ Kj ⎠ ⎝
(7)
where, r j , Aj and kj are the reaction rate, reactive surface area, and rate constant, for reaction j, respectively. 2.4. Fines transport Fines transport was modeled using Gorucu et al.’s [23] approach in which fines transport (deposition, migration, and plugging) is taken into account during low salinity brine injection as follows:
∂Vt k = β1 c fk − β2 Vdepo (viw − νcr ) + β3 (1 + β4 Vtn ) vsw c fk ∂t
3. LSW coreflooding simulation with fines transport 3.1. Experimental description
(8)
Hussain et al. [25] investigated effects of fines migration and fines plugging on LSW performance by conducting different LSW’s corefloodings with Berea sandstone. The experiments were performed with synthetic Soltrol oil that excluded wettability alteration due to geochemistry. Thus this experiment has been selected to validate the fines transport model presented in this paper. The core plug dimension was 2.58 cm in diameter and 5.4 cm in length. Berea sandstone’s absolute permeability and porosity were 495 mD and 23%, respectively. The first experiment was used for single phase water injection, whereas the second and third experiments were used for actual low salinity brine injection. The third coreflood was only for confirming the reproducibility of the laboratory measurements; therefore, coreflooding 2 was selected to match the simulation results. HSW containing 40 g/l NaCl and LSW (fresh water) was injected at the initial water saturation (Swi) with the injection rate of about 15 cm3/hr. The experiment consisted of two sequential injections as shown in Fig. 3. The oil recovery and
where β1; β2 ; β3 ; β4 are the deposition, migration, plugging, and snow ball coefficients, respectively, vsw , viw , and vcr are the superficial, interstitial, and critical water velocities, respectively, Vt is the total fines volume fraction, and Vdepo is the volume fraction of deposited fines. In this model, fines deposition and migration are reversible, whereas fines plugging is irreversible. Initially, fines particles are in the reversible deposited form and these particles can be mobilized and plugged in small pore throats by changing water velocity and water salinity. As reported from the laboratory and field scales, salinity shock due to low salinity water injection can mobilize deposited fines. Therefore, the critical velocity for fine mobilization is modeled as a function of salinity. Additionally, fines plugging after migration can also cause a 3
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1 Oil injection until
3 Oil injection until
2 HSW until
4 LSW injection until
Fig. 3. Laboratory experimental procedures.
pressure change were monitored and the endpoints of relative permeability were measured and shown in Table 1.
a. Hussain’s pressure drop history ma A)
3.2. LSW coreflooding simulation The simulation was performed with a 1D model that consists of 50 gridblocks. The important physics for LSW mechanisms with fines transport have been included in the model, for example:
• Fines deposition • Fines migration • Fines plugging • Salinity dependence of critical velocity • Permeability reduction dependence of volumetric fines plugging • Relative permeability modification dependence of volume fines plugging • Geochemistry including ion exchanges between injection fluids and reservoir rock, mineral dissolution and deposition, and intra-aqueous reactions.
b.
As measured from laboratory data, relative permeability endpoints were slightly changed between HSW and LSW even though a synthetic oil was used in the experiment. This observation is similar to the measurements conducted by Yu et al. [48]. Both studies showed that fines were migrated and produced during LSW. A sharp increase in the pressure drop in all coreflooding experiments indicated that fines were mobilized and plugged into porous media under the effects of a low salinity environment. Additionally, laboratory studies also indicated that fines plugging can affect the relative permeability endpoints and curvatures. The measured relative permeability endpoints shown in Table 1 were used in the 1D coreflooding simulation. Hussain et al. [25] also attempted to match this coreflooding experiment using an analytical model originally proposed by Zeinijahromi et al. [52]. This model is primarily based on maximum retention of movable in-situ fines by LSW. As reported in Hussain et al. [25], this model gave an adequate match against laboratory production profile of LSW with fines transport. However, it cannot capture the trend of the laboratory data around the breakthrough points, as shown in Fig. 4a. More importantly, Hussain’s matching approach used an unusual relative permeability curve for LSW in which water relative permeability tended to increase with water saturation, and then decreased when water saturation reached the value of approximately 0.58 (Fig. 4b). This behavior was obtained from a history matching
Fig. 4. Hussain’s (2014) LSW coreflooding matching.
procedure by minimizing the differences between measured and predicted data; however, such a relative permeability behavior has been rarely reported in the past studies. Thus, one of the objectives in this study was to use the newly proposed model to reproduce the trend of lab data with conventional relative permeability behavior. Note that the endpoints of relative permeability were fixed as similar as the measured data, while fines transport coefficients and relative permeability exponents were history matching parameters against laboratory data. The first validation was conducted to compare LSW simulation results without modeling fines transport against laboratory measured data. As shown in Fig. 5, simulation results were not matched with the trend of the experimental data in this case. Yu et al.’s [48] experimental studies indicated that a relative permeability modification occurred when fines particles were plugged in porous media by injecting low salinity brine into non-polar saturated oil cores. This phenomenon is not represented without fines transport modeling; therefore, the
Table 1 Laboratory measured relative permeability endpoints for HSW and LSW [25]
HSW LSW
Initial water saturation (Swi)
Residual Oil Saturation (Sor)
Oil Relative Permeability at Swi
Water Relative Permeability at Sor
0.293 0.293
0.415 0.402
0.76 0.757
0.13 0.074
4
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Fig. 5. Experimental and calculated oil recovery factor for Hussain’s coreflooding without fines transport modeling.
Fig. 6. Experimental and calculated pressure drop for Hussain’s coreflooding without fines transport modeling.
Fig. 7. Experimental and calculated oil recovery factors for Hussain’s coreflooding with fines transport modeling.
normal trend of high salinity waterflooding in which pressure increases until the water breakthrough point and then decreases afterwards. As observed from the past studies and in this particular coreflooding data, the pressure drop significantly increased during LSW due to a
simulation cannot capture the incremental oil recovery by LSW. In addition, without modeling fines migration and fines plugging, the simulation was also not able to capture the change in pressure drop (Fig. 6). The pressure drop profile from LSW simulation follows the 5
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Fig. 8. Experimental and calculated pressure drops for Hussain’s coreflooding with fines transport modeling.
permeability reduction by fines migration and pore throats plugging [53], 2010, [46,25,48]. Unsurprisingly, the pressure drop observed from simulation without fines transport was much lower than the one measured in the laboratory. In other words, it is not possible to capture an increase in oil recovery and pressure drop during the course of LSW for this coreflood experiment without considering fines transport. In the second validation, the proposed model equipped with fines transport phenomena was applied to match the production profile of LSW for Hussain’s coreflooding experiment. The modification of relative permeability was modeled as a function of volumetric plugged fines concentration. As shown in Fig. 7, an excellent agreement with the coreflooding data for oil production and the final recovery factor was achieved by including fines transport in the simulation. Additionally, the proposed fines transport model successfully captured the trend of around the breakthrough period in which the pressure drop slightly decreased before increasing sharply (Fig. 8). This behavior of pressure drop was reproduced in a sister core plug and it was important that the proposed model efficiently improved the matching results for the pressure drop compared to the past studies. Additionally, history matching results were obtained with the conventional relative permeability curves, as shown in Fig. 9, without requiring an unusual drop of water relative permeability at the high values of water saturation. This is more consistent with the majority measurements of relative permeability for LSW reported in the literature.
Fig. 9. Relative permeability curve used for matching Hussain’s coreflooding with fines transport modeling. Table 2 Porosity and permeability of each geological unit.
Average porosity(%) Average permeability(mD) Thickness(m)
U1
U2
U3
15 300 10
12 75 10
10 15 10
4. Concept of LSW assisted CO2 flooding Previous studies reported that LSW can cause formation damage by
a. Porosity
b. Permeabbility
Fig. 10. 3D Porosity and permeability distributions. 6
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Fig. 11. 3D reservoir model with Well Pattern.
showed that a combination of LSW with CO2 WAG (water alternating gas) yielded about 4.5% incremental original oil in place over the conventional WAG. Al-Shalabi et al. [2] reported that the combined CO2 flooding and LSW promotes the EOR synergies, leading to a higher oil recovery factor in carbonate reservoirs. However, LSW’s fines transport was normally not included in the simulation workflow; this section aimed to provide an evaluation of LSW assisted CO2 flooding with presence of fines migration and fines plugging. CO2 flooding has been widely applied in a tertiary recovery stage; however, the ultimate recovery factor is reduced by the high mobility of the injected gas, especially in heterogonous permeability reservoirs [35,37]. Viscous fingering caused by unfavorable mobility of CO2 can significantly reduce the volumetric sweep efficiency leading to a poor performance of a CO2-EOR project. On the contrary, LSW can work as a mobility control agent by mobilizing fines particles and reducing the gas mobility; therefore, LSW followed by CO2 flooding promotes the synergy between these two EOR methods. The concept of LSW assisted CO2 flooding can be summarized below:
Table 3 Oil Property used in Reservoir Simulation. Components
Mole Fraction
CO2 N2 to CH4 C2H to NC4 IC5 to C07 C08 to C12 C13 to C19 C20 to C30
0.01183 0.11702 0.194538 0.220253 0.280506 0.0939775 0.0808749
Table 4 Reservoir and Injected Water Salinity Molalities.
Na
+
Formation
LSW
0.4892
0.01196
mobilizing and plugging fines in porous media; however, LSW can also be used for mobility control in heterogeneous reservoirs [12,15,17,43,22,25,27,29,38,44,52,49]. Nguyen et al. [33] indicated that fines mobilization with a permeability decline by LSW help to enhance gas recovery during reservoir depletion. Dang et al. [19]
• Low salinity brine injection to promote favorable wettability alteration and to mobilize fines particles, • Fines particles will be moving and plugging in high conductivity zones, and
Fig. 12. LSW vs. HSW at field scale. 7
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Fig. 13. Role of fines transport in LSW at the field scale.
Fig. 14. LSW + CO2 flooding vs. HSW + CO2 flooding at field scale.
• The following injected CO
injectors at the corners of the reservoir and 10 vertical producers inside the reservoir as shown in Fig. 11. Important physics for fines transport, geochemistry, and wettability alteration were incorporated in the model to evaluate LSW performance. Critical velocity and permeability reductions were modeled as functions of salinity and volumetric plugging fines, respectively. Oil compositions and fluid prperties are shown in Tables 3 and 4.
2 slug will be partially diverted into unswept layers and improve the final oil recovery factor.
4.1. Geological and reservoir modeling A geological model consists of 3 geological units with 15 vertical layers. The porosity and permeability of each unit were geostatistically generated and controlled by field histograms and variograms data. The average porosity and permeability in each geological unit are indicated in Table 2. Fig. 10 shows the 3D distributions of the porosity and permeability for the generated geological model. The grid size of 50x30x15 in the I-, J-, and K-directions, respectively, was used in reservoir simulation. The model consists of 4 vertical
4.2. LSW versus conventional waterflooding In the first study, HSW and LSW were separately injected for about one year in 4 the injectors. The simulation captures the crucial roles of both wettability alteration and formation damage during LSW process. 8
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Fig. 15. Oil saturation maps after LSW + CO2 flooding vs. HSW + CO2 flooding at the field scale.
a. Layer 1
Fig. 16. Facies distribution in a channeling model (yellow – channels; grey – background). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
b. Layer 15 Fines particles were mobilized with the propagation of low salinity brine slugs and plugged into the high permeability geological layers, and consequently decreased permeability as observed in both laboratory and pilot tests. Yu et al. [48] reported that absolute permeability reduced approximately 6 times when the injected brine salinity decreased from 40 g/l to fresh water. To the best of our knowledge, our study is the first LSW modeling workflow that can comprehensively evaluate both positive and negative impacts, favorable wettability alteration versus formation damage, of LSW at the field scale. Fig. 12 shows that LSW provided about 4.6% incremental oil recovery over HSW due to more water-wetness alteration. This result is similar to the observations obtained from pilot tests reported in the literature. With the new capabilities in modeling fines transport under low salinity brine injection, it is now possible to track movement paths and amounts of fines migration and fines plugging in a reservoir. Fig. 13 shows a cross section view where fines were mobilized around the
Fig. 17. Permeability distribution in the channeling model.
injectors and moved along the high permeability zones. A portion of these migrated fines were plugged in pore throats, and led to a reduction in permeability at the near injection wellbore regions. The simulation results are useful to monitor the formation damage caused by low salinity brine injection and effectively assist reservoir engineers to achieve a better design for LSW implementation. 4.3. Low salinity assisted CO2 flooding To evaluate the benefits of LSW assisted CO2 gas flooding, the simulation presented in the previous section was extended by continuously injecting CO2 after LSW or HSW. Fig. 14 shows that LSW followed by CO2 flooding provides higher oil recovery compared to the 9
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Fig. 18. Recovery factor of LSW + CO2 flooding vs. HSW + CO2 in channel reservoir.
HSW + CO2 Flooding
LSW + CO2 Flooding
Mid-CO2 Injection – Layer 5
Mid-CO2 Injection – Layer 5
End-CO2 Injection – Layer 5
End-CO2 Injection – Layer 5
Fig. 19. Gas saturation maps after LSW + CO2 vs. HSW + CO2 in channel reservoir.
one by HSW plus CO2 flooding. Although LSW and CO2 flooding experiences a short delay in the early stage due to a permeability decline, this hybrid method helps to produce more oil by blocking the high permeability zones near the 4 injection wells. The advantages of LSW assisted CO2 flooding is clearly illustrated from the oil saturation cross section shown in Fig. 15. In the conventional high salinity CO2 flooding approach, it was only possible to produce oil in the top layers with high permeability values, and most of the reservoir was left behind after CO2 flooding with high remaining oil saturation. Additionally, further CO2 injection was ineffective as there was a very slight change in the oil saturation maps at the middle and the end of the CO2 flooding project. On the other hand, oil saturation was significantly lower in the case of LSW followed by CO2 flooding as the result of a permeability reduction in the top reservoir layers. Injected gas flow was successfully diverted into lower permeability layers and consequently led to a higher recovery factor. This demonstrates the merits of combining LSW with the conventional CO2 flooding to
overcome the existing technical challenges. 5. LSW assisted CO2 flooding with presence of channels The new hybrid recovery process combining LSW and CO2 flooding was applied in a channelling reservoir which is one of the most difficult reservoir types for EOR implementation. These reservoirs usually consist of complex channel systems with very high permeability and the injection fluids can quickly move along these channels, leading to a poor performance of an EOR project [34]. As a mobility control agent, LSW was applied to reduce the permeability of the channels, and therefore, improved the sweep efficiency of the following CO2 slug. A geological model consisted of 15 layers with two facies as shown in Fig. 16. Channels were geostatistically distributed in the model based on field data. These channels have significantly higher permeability values than the one in background. The average permeability for the channels and background are 500 mD and 75 mD, respectively 10
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HSW + CO2 Flooding
LSW + CO2 Flooding
Mid-CO2 Injection – Layer 5
Mid-CO2 Injection – Layer 5
End-CO2 Injection – Layer 5
End-CO2 Injection – Layer 5
Fig. 20. Oil saturation maps after LSW + CO2 flooding vs. HSW + CO2 in channel reservoir.
Acknowledgments
(Fig. 17). The model consists of 4 vertical injectors at the corners of the reservoir and 10 vertical producers inside the reservoir. Fines transport, geochemistry, and wettability alteration were incorporated into the model similarly to the previous case. Either low salinity brine or high salinity brine was injected for about 1 year and followed by a continuous CO2 injection. Similar to the previous case, LSW is effective in mobilizing fines and reducing permeability of the channels. The hybrid LSW plus CO2 flooding provided about 7.5% incremental oil production over the conventional CO2 flooding implementation (Fig. 18). Figs. 19 and 20 show that the injected gas was quickly produced and left behind a large un-swept zone in the conventional recovery method, while the hybrid method efficiently helps to improve the sweep efficiency of CO2 flooding. This demonstrates the advantages of low salinity CO2 flooding over the conventional approach and this new recovery strategy can be applied to unlock hydrocarbon reserves in reservoirs with a complex channel system.
The authors would like to acknowledge Dr. Vijay Shrivastava of Computer Modelling Group Ltd. for many fruitful technical discussions. The authors also thank Emerson-Paradigm for providing the GOCAD™ software for this research. The compositional simulator GEM™ and the optimization software CMOST™ from Computer Modelling Group Ltd. were used in this paper. Part of the research is supported by NSERC/ Energi Simulation and Alberta Innovates Chairs. References [1] Ahmetgareev V, Zeinijahromi A, Badalyan A, Khisamov R, Bedrikovetsky P. Analysis of low salinity waterflooding in bastrykskoye field. Pet Sci Technol 2015;33:561–70. [2] Al-Shalabi E, Sepehrnoori K, Pope G. Numerical modeling of combined low salinity water and carbon dioxide in carbonate cores. J Petrol Sci Eng 2016;137:157–71. [3] Austad T. “Smart Water’ for enhanced oil recovery: a comparison of mechanisms in carbonates and sandstones. NPD, Stavanger: The presentation presented at the FORCE seminar on Low Salinity; 2008. [4] Austad T., Rezaeidoust A., Puntervold T., 2010. Chemical mechanism of low salinity water flooding in sandstone reservoirs. In: SPE 129767 presented at the SPE IOR Symposium, Tulsa, OK, USA, 24-28 April. [5] Bedrikovetsky P, Zeinijahromi A, Badalyan A, Ahmetgareeve V, Khisamov R. Finesmigration-assisted low-salinity waterflooding: field case analysis. SPE 176721 presented at the SPE Russian Petroleum Technology Conference held in Moscow. 2015. [6] Borazjani S, Behr A, Genolet L, Kowollik P, You Z, Bedrikovetsky P. Low-salinity fines-assisted waterflooding: multiscale analytical and numerical modeling. SPE 186070 presented at the SPE Reservoir Characterisation and Simulation Conference held in Abu Dhabi. 2017. [7] Borazjani S, Chequer L, Russell T, Bedrikovetsky P. Injectivity decline during waterflooding and PWRI due to fines migration. SPE 189521 presented at the SPE international conference and exhibition on formation damage control held in. 2018. [8] Chen Z., Huan G. and Ma Y., 2006. Computational Methods for Multiphase Flows in Porous Media. Computational Science and Engineering Series, Vol. 2, SIAM, Philadelphia. [9] Chen Z., 2007. Reservoir Simulation: Mathematical Techniques in Oil Recovery. CBMS-NSF Regional Conference Series in Applied Mathematics, Vol. 77, SIAM, Philadelphia. [10] Chen Z, Ewing RE. From single-phase to compositional flow: applicability of mixed finite elements. Transp Porous Media 1997;27:225–42. [11] Chen Z, Ewing RE, Jiang Q, Spagnuolo AM. Error analysis for characteristics-based methods for degenerate parabolic problems, SIAM. J. Numer. Anal. 2002;40:1491–515.
6. Conclusions This paper presents a new LSW simulation workflow that captures important physics of the LSW process including fines transport, geochemistry, and wettability alteration. With the new capabilities of modeling different fines transport phenomena (deposition, migration and plugging), it is now possible to achieve a better understanding and evaluation of LSW and hybrid processes in terms of formation damage and low salinity brine flow in porous media. The simulation results are strongly in agreement with the ones obtained from the laboratory. Numerical simulation results show that low salinity assisted CO2 flooding is an emerging attractive EOR technology in which LSW can help to mobilize fines and reduce permeability in high conductivity zones and significantly improve the sweep efficiency of CO2 flooding. This hybrid method outperformed the conventional CO2 flooding in terms of higher ultimate recovery factor for different types of reservoirs.
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