MSc Reservoir Evaluation and Management

Project Report 2013/2014

Nestor Danilo Vasconez Noguera

 

1D core flood modelling of impact of brine composition and distribution of 

exchanger sites on optimisation of low salinity waterflooding 

 

 

 

 

Heriot-Watt University

Institute of Petroleum Engineering

Supervisor – Professor Eric Mackay

 

 

 

 

 

 

 

 

  

 

DECLARATION: 

I………………………………………………………………………………………………  confirm  that  this work  submitted  for 

assessment  is my own and  is expressed  in my own words. Any uses made within  it of the works of 

other  authors  in  any  form  (e.g.  ideas,  equations,  figures,  text,  tables,  programs)  are  properly 

acknowledged at the point of their use. A list of the references employed is included. 

Signed…………………………………………… 

Date……………………………………………… 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

  

 

ACKNOWLEDGEMENTS 

The  author  is  thankful  to  the  Institute  of  Petroleum  Engineering  at  Heriot‐Watt  University  for 

providing me all  the necessary  facilities  to  successfully develop  the present project. Thank you  to 

Professor Eric Mackay for his guidance, support and knowledge shared through all the steps of the 

project. Finally, the author is grateful to CMG for providing the licenses and last version of GEM.  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

  

 

SUMMARY

The present report contains a literature review of the key and latest publications on Low

Salinity Waterflooding (LSWF) since its real development in the 1990`s to explain the

dominant mechanisms, modelling processes and requirements to apply this Enhanced Oil

Recovery (EOR) technique. Up-to-date, there is no general agreement of the dominating

mechanism that rules the LSWF effectiveness. Currently, wettability alteration to a more

water-wet state of the rock as a result of ion exchange and/or double layer expansion

mechanism are the two most feasible and supported pore scale mechanisms. However, most

of the last published modeling methods are trying to represent the wettability changes only as

a result of Multi-Ion Exchange (MIE) processes and geochemical reactions. These methods

cross-check their results with observed laboratory data and with the chemical reactions

obtained from a recognized geochemical simulator, PHREEQ-C (Kharaka et al., 1988). The

present project is oriented to reproduce the experimental results obtained in the IPE laboratory

at Heriot Watt University with standard industry reservoir simulation software and validate

the outcomes with PHREEQ-C to later define correlations between grid size, cation exchange

capacity (CEC), selectivity coefficients, injection rates, and oil recovery in a MIE modelling

process in a single phase and two phase system.

  

 

TABLE OF CONTENTS 

INTRODUCTION ............................................................................................................................... 1 

LSW REQUIREMENTS ....................................................................................................................... 2 

LSW MECHANISMS .......................................................................................................................... 4 

Multi‐Ion Exchange ............................................................................................................................. 4 

Electrical Double Layer Expansion ...................................................................................................... 8 

ADDITIONAL OBSERVED MECHANISMS ......................................................................................... 11 

Fines migration .................................................................................................................................. 11 

pH variation ....................................................................................................................................... 12 

MODELLING LOW SALINITY WATER INJECTION .............................................................................. 14 

Modelling considerations .................................................................................................................. 18 

RESULTS ........................................................................................................................................ 20 

Data summary ................................................................................................................................... 20 

Methodology ..................................................................................................................................... 21 

Results – Single Phase ....................................................................................................................... 21 

Results – Two Phase System ............................................................................................................. 28 

DISCUSSION .................................................................................................................................. 45 

Results – Single Phase ....................................................................................................................... 45 

Results – Two Phase System ............................................................................................................. 47 

CONCLUSIONS ............................................................................................................................... 51 

SUGGESTION FOR FURTHER WORK ................................................................................................ 52 

REFERENCES .................................................................................................................................. 53 

  

 

NOMENCLATURE

BP British Petroleum

CEC Cation Exchange Capacitty

CMG Computer Modelling Group

EOR Enhanced Oil Recovery

EDL Electrical Double Layer

DLVO Derjaguin and Landau, Verwey and Overbeek

HSF High Salinity Waterflooding

IPE Institute of Petroleum Engineering / Heriot Watt University

IFT Interfacial Tension

LSW Low Salinity Waterflooding

MIE Multi-Ion Exchange

1D One dimensional model

SWCTT Single Well Chemical Tracer Test

Sor Residual Oil Saturation

  

 

AIMS

The project is focused on producing a literature review of the published work on Low Salinity

Waterflooding to identify the key achievements of this EOR technique in sandstones and

identify the main modelling processes. Laboratory observed data and a geochemical simulator

will be used to validate the reservoir simulation results provided by standard industry

software. This validation will allow to identify if reliable modelling tools are available and

what the current limitations are to capture LSW mechanisms. From the results obtained,

general correlations are presented based on comparison with experimental observations and

known facts.

1  

 

INTRODUCTION

Low Salinity Water Flooding as EOR method started to gain popularity after the results

obtained during 90`s (Jadhunandan et al., 1990, Morrow and Yildiz et al., 1996, Tang and

Morrow et al., 1999) where many experiments reported that reduction in the brine salinity

resulted in the increase of the oil recovery triggering the research around this technique due to

its more economic and apparently simplistic application and methodology.

Among the many mechanisms related to the LSW success (Sheng et al., 2014), Multi-Ion

Exchange and Electrical Double Layer (EDL) expansion are the two most supported by

experimental observation. Lager (Lager et al., 2006) identified cation exchange as the primary

mechanism responsible for the LSW efficiency where coreflood experiments supported their

observations in secondary and tertiary mode. Sorbie (Sorbie et al., 2010) supported MIE as

the mechanism responsible for LSW effectiveness causing the expansion of the EDL as an

effect not as a mechanism of LSW.

On the other hand, Ramez (Ramez et al., 2011; Ramez et al., 2014) reports that Multi-Ion

exchange was not part of the mechanisms leading to an increase in oil recovery as MIE was

observed after the injection of more than 1PV during core flooding experiments and the

improve in recovery is ascribed to EDL expansion. In addition, Austad (Austad et al., 2010)

describes that polar oil components can be attached to clay surfaces without a divalent cation

bridge which sparks even more the controversy.

Additional phenomena, such as pH increase and fines migration are also considered important

and related to the enhancement in oil recovery (Tang and Morrow et al., 1999; McGuire et al.,

2005, Webb et al., 2005; Lager et al., 2006, Ramez et al., 2014). However, the last

publications are addressing their attention toward MIE and EDL supported by observations.

Moreover, MIE is the only mechanism that is being implemented in current reservoir

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simulators to capture and reproduce the experimental observations at core, well and field

scale.

The present report uses the MIE capabilities of a compositional reservoir simulator, GEM®,

to initially match experimental effluent concentrations in a 1D single phase system to later

perform sensitivities and evaluate the oil recovery factor against the required pore volume to

infer also an economical feasibility. The results provide more insights to model LSW with the

chosen simulation tool in a 1D single phase and two phase system. In general, the 2 phase

system outcomes reflect a change in the wetting state of the rock from oil wet to a more water

wet state in different proportions supported by documented observations and known

phenomena.

LSW REQUIREMENTS

One of the most important parts of the early LSW research (Tang & Morrow et al., 1999) was

to recognize the components for a successful Low Salinity Waterflooding:

Connate water: Presence of connate water as no additional oil recovery was observed when

flooding the cores after 100% oil saturation.

Oil composition: Presence of polar components in the oil which is supported by their

observation using refined oil giving no additional recovery.

Mobile fines: Presence of mobile fines. However, this is not fully supported in their

observations and contradictory evidence has been documented (Lager et al., 2006; Ramez et

al., 2014)

Clay: Presence of clays in the connate water as their observations reported an important

reduction in the oil recovery increase with low clay content and initially oil flooded cores.

This was confirmed when as part of their experiments to stabilize the fines, a core was fired

3  

 

and flooded with HCl prior to performing the core flooding to evaluate oil recovery. Neither

oil recovery increase nor pH variation was observed in the cycle of injections. This is

explained due to the elimination of the cation exchange capacity of the rock as confirmed by

other authors (Lager et al., 2006; Sheng et al., 2014), and the results presented in this report

when the CEC was set to zero.

The presence of clay as a requirement was sustained by Lager (Lager et al., 2006) who

reported improvement in oil recovery in clay rich cores in the absence of fines migration and

permeability reduction. A more drastic change was observed in recovery between HSW &

LSW in clay rich reservoirs by Zhang (Zhang et al., 2007). Following the mentioned

publications, Seccombe (Seccombe et al., 2010), found that clay was correlatable with

additional oil recovery during LSW.

Divalent Cations in the formation water: Lager (Lager et al., 2006) documented what

occurred when all the Ca and Mg ions are removed from the rock surface before using

LSW in tertiary mode. No additional recovery was observed when they switched to LS as all

the divalent cations were previously took out by flooding NaCl. They concluded that due to

the absence of divalent cations, no organo-metallic complexes were present to be desorbed

from the clay surface.

Temperature: Austad (Austad et al., 2010) mentions that temperature is not a limitation

reaching a top documented temperature of 100 ºC.

Even though all these requirements are met, improved in oil recovery is not always observed;

this preserves the discussion of the main mechanisms of LSWF as documented by

Skrettingland (Skrettingland et al., 2010)

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LSW MECHANISMS

Among all the LSW mechanisms (Sheng et al., 2014), MIE and DLE are the two most

supported by observations. Modelling tools, however, only can capture the MIE process and

use this mechanism as the main factor that increases the oil recovery factor. Nevertheless, the

two mechanisms are related with the occurrence of EDL facilitating the ion exchange

mechanisms which enhances oil recovery (Lager et al., 2006; Omekeh et al., 2011)

Multi-Ion Exchange

The MIE mechanism states that oil molecules are bonded to the clay surfaces by divalent ions

and when LSW is injected into the reservoir, the EDL expands which produces a favourable

state for ion-exchange between complexed and uncomplexed ions detaching previously

bonded oil molecules, thereby improving the oil recovery (Omekeh et al, 2011) (See Figure

1).

Figure 1. Schematic representation of multi-ion exchange process. (From Collins, 2014)

In their experimental results Lager (Lager et al., 2006) reported that during a Low Salinity

waterflood at core scale the Ca and Mg concentration fell below the injected values

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inferring that these divalent ions were adsorbed onto the rock. The ion adsorption was

mentioned in association with the DLVO theory (Amarson Keil et al., 2000), which defines

four mechanisms of organic matter adhesion onto the rock that are influenced by LSW: cation

exchange, ligand-bonding and cation and water bridging. During LSW, MIE will occur when

organic oil polar components bonded to a clay surface (organo-metallic complexes) are

removed and replaced by uncomplexed cations which will result in a more water wet surface.

Sorbie (Sorbie et al., 2010) stated that MIE is the mechanism behind the liberation of the

organic oil components bonded to the rock surface when LS water is injected and the EDL

expands. The exchange results in a variation of the wettability from an oil wet to a more water

wet surface which enhances the oil recovery. The authors linked MIE with a self-freshening

zone where there is a drop in salinity and ion concentration even below the injected levels as

observed by Lager (Lager et al., 2006) and the experimental results obtained at Heriot-Watt

University. According to the authors, this zone which grows linearly with time is located

between the HS and LS fronts. The LS front moves slower than the HS front because at low

ionic strength and low Na concentration, there are more Mg ions going onto the rock which is

depicted by a higher gradient of ion exchange versus ion concentration resulting in a slower

velocity front using velocity chromatography expressions (Pope et al., 1978). Sorbie describes

that in the “fresh zone” a stable water film is formed inside pores due to the negative charges

at fluid-fluid and fluid rock interfaces and the electric double layer expansion which at the end

turns the rock into a more water wet surface. This statement is supported by Ramez (Ramez et

al., 2011; Ramez et al., 2014) in their experimental observations.

Ramez (Ramez et al., 2011) analysed the effluent concentration of a core saturated with oil

and formation brine after injecting NaCl at 5%. The effluent showed low concentrations of

Ca and Mg compared to the formation brine concentration after 8 PV of NaCl injection

interpreting this as cation exchange due to Ca replacement by Na at the rock surface.

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To support the previous observations and assess cation exchange between injected brine and

rock, the authors injected NaCl at 5% in a dry core . The effluent showed a concentration of

Ca and Mg which were not present in the injected brine. This was interpreted as cation

exchange between the rock and the injected brine observing as well a plateau in the divalent

cations concentrations interpreted as a saturated state where no additional replacement was

taking place.

Omekeh (Omekeh et al., 2011) performed static and dynamic experiments in a single phase

system to assess divalent cation release from core samples. The authors reported ion exchange

in the dynamic test while flooding the core with formation brine and low salinity brine. The

static test showed higher liberation of calcium ions with low salinity brine compared to

formation brine and sea brine.

This mechanism has also been observed at inter-well scale as reported by Lager (Lager et al.,

2011). During a LSW, MIE was observed between two wells separated 1040 feet when Fe

concentration sharply increased at the effluent. It must be mentioned that no Fe had been

observed before in any part of the tested field, and as soon as the low salinity slug finished,

the Fe concentration decreased with an increase in salinity. Based on these observations the

authors proposed a geochemical model with organo-metallic complexes bonded to the clay

surface through Fe ions. However, the complexes are considered chelated to avoid any

subsequent interaction with the mineral surface which could explain why Fe was only

observed during LSW and not in HSW.

The present report follows the work presented by Dang (Dang et al., 2013) who coupled

geochemical reactions and ion-exchange into a compositional simulator describing MIE as the

mechanisms responsible for the enhancement in oil recovery during LSW.

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In their work, Ion-Exchange models the disruption of the chemical equilibrium when a brine

with different composition from the connate brine is injected in the system. Dang (Dang et al.,

2013), explaining the underlying process with two typical reactions:

0.5 ↔ 0.5 (1)

0.5 ↔ 0.5 (2)

The reversible reactions, where X represents a clay mineral, show that when LSW is injected

and are retained by the exchanger and is liberated. These processes are a

function of selectivity coefficients as operational variables to represent the exchange on the

clay surfaces.

The results obtained by Dang (Dang et al., 2013) are comparable with PHREEQ-C using a 1D

model. The brine used in the comparison was composed of , , ions and the

comparison shows a good match of concentration of , versus injected pore volume.

In addition, the model was tested against the experimental observations of Fjelde (Fjelde et

al., 2012) where a 1D model was built to represent dissolution of calcite. The

concentration in the effluent as well as the pH increase were matched using Ion-

Exchange and geochemical reactions.

The authors showed that by using the equivalent fraction of an excellent match of oil

saturation versus injected pore volume is obtained. They relate the

adsorption/desorption onto and from the rock with wettability alteration although a minor

increase in oil recovery is shown in the experimental data.

An additional benchmark used Rivet’s (Rivet et al., 2009) experimental results, the

incorporation of Ion – Exchange produced a better match of pH and average oil saturation

versus injected pore volume. According to the authors, the higher efficiency in LSW

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compared with Fjelde (Fjelde et al., 2012) experiments is due to a superior injected brine in

terms of divalent cation concentrations.

As reported by Dang (Dang et al., 2013) the use of the Equivalent Fraction of as

relative permeability interpolant is related only to the analysed cases and additional

investigations are carried out to test . Up to the date that this report is written, there are

no publications testing the model against additional experimental data.

Electrical Double Layer Expansion

The presence of a negatively charged clay surface and a surrounding solution originate an

electrical potential which decreases its variability with the increase in distance from the clay

surface (De Bruin et al., 2006). See Figure 2:

 

Figure 2. Electrical double layer representation (From De Bruin et al., 2006)

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As displayed in Figure 2, between the bulk fluid and the clay surface there exist a Zeta

Potential and a Stern potential (Grahame et al., 1947) which define a different degree of

attraction of the ions toward the clay surface under equilibrium conditions. When LSW is

injected into the formation the EDL expands due to the low content of electrolytes in the

injected solution and as a result additional oil can be removed with more facility (Bruin et al.,

2006).

Initially, Tang and Morrow (Tang and Morrow al et., 1999) mentioned that attention should

be focused on the equilibrium between mechanical forces and DLVO theory (Israelachvili al

et., 1991) to define the mechanisms responsible for the detachment of mixed wet fines bonded

with polar oil components. Tang and Morrow mentioned that the injection of low salinity

brine promotes the expansion of the EDL due to the increase in repulsive forces as the balance

between Van der Wall attractive forces and electrostatic repulsion is broken. The unbalance

would encourage the removal oil coupled particles increasing the oil recovery producing a

local change in wettability.

Lager linked the electrical double layer expansion with MIE (Lager et al., 2006) stating that

when salinity is reduced, the double layer will expand permitting the divalent cations to be

exchanged.

In their initial work, Ramez (Ramez et al., 2011) performed coreflood experiments to find the

optimum brine in function of the cation type measuring the Z-Potential at the rock-brine and

oil-brine interfaces using Berea sandstones. Different concentrations of NaCl, CaCl2 and

MgCl2 were used as injection brine to evaluate the Z-potential in cores saturated with oil and

initial water saturation. The results showed that the lowest concentrations of the injected

brines produced higher negative potentials resulting in higher repulsive forces and

consequently a more stable water film which is translated into a more water wet surface

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supporting the mechanism explained by Sorbie (Sorbie et al., 2010). The tests also showed

that NaCl brine produced higher negative charges compared to the other cations. The

procedure was also tested using clays instead of sandstone. Chlorite, kaolinite, illite and

montmorillonite were used to measure the Z-Potential and as before, NaCl with the lowest

concentration produced the highest negative charges at the measured interfaces.

Following their work, Ramez (Ramez et al., 2014) measured the Z-Potential at the oil/brine

and sandstone/brine interfaces after injecting low salinity brine observing a stronger negative

charges which results in increasing repulsive forces. The authors performed core flooding

experiments showing a correlation between the pH variation and the rock wettability (contact

angle measurements) which is ascribed to the high repulsive forces between the fluids and the

sandstone. This repulsive forces destabilize the water film producing a thicker double layer

expansion effect which facilitates the oil removal by water flooding resulting in an increase in

oil production.

Ramez (Ramez et al., 2014) also tested the impact of the cation type by flooding the core with

NaCl and CaCl2. The analysed interfaces became more negative when using NaCl than with

CaCl2 at the same concentration. The cation type effect on rock wettability was also analysed

by flooding the core with the mentioned solutions. A solution of NaCl produce a lower

contact angle and is concluded that a monovalent cation is preferred in the injected brine. This

effect added to the Z-potential effect results in a less stable water film which is translated into

a more water wet system. This observation was corroborated with an oil flooding experiment

using NaCl and CaCl2 where the CaCl2 inhibited the oil recovery compared with the NaCl

solution.

To find more concluding evidence Ramez (Ramez et al., 2014) reduced the original pH in the

HS and LS brines. The results showed that by reducing the pH the oil/brine and

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brine/sandstone interfaces became less negative reducing the Double Layer Expansion effect.

This will have a different impact in the wettability alteration and consequently a smaller

change toward a water wet system. This statement is supported by the contact angle

measurements which show less decrease when the pH is decreased from its original value.

This observations were confirmed when oil displacements using low salinity brines with

different pH (7.3 and 4.8) produced different recoveries. The brine with reduced pH resulted

in less recovery factor as a result of less negative interfaces resulting in a more compressed

Double Layer compared to low salinity brine with pH of 7.3.

ADDITIONAL OBSERVED MECHANISMS

Several mechanisms have been proposed as responsible for LSW efficiency. Some of them

have lost credibility due to the lack of observations or contradictory evidence. Next,

additional mechanism to EDL expansion and MIE are partially explained as these have been

observed with more frequency.

Fines migration

One of the first experimental floodings with low salinity water (i.e. fresh water) was

performed by Bernard (Bernard al et., 1967). He observed additional oil recovery which was

interpreted as an effect of clay swelling and pore throat plugging resulting in an improved

microscopic efficiency. However, in early 90`s the work performed by University of

Wyoming on LSW (Jadhunandan, and Morrow al et., 1991; Yildiz and Morrow al et., 1996;

Tang and Morrow al et., 1997) resulted in important observations by Tang and Morrow (Tang

and Morrow al et., 1999) who documented the increase in oil recovery linked to fines

migration, where fine particles and attached polar oil components are removed when

performing LSW. In their experiments, the authors reported fines in the effluent identified as

kaolinite as well as changes in the pressure drop which correlates with the change in the

injected brine composition and pH variation in the effluent.

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As supporting evidence, in the last stage of LSWF experiments, Fjelde reported (Fjelde al et.,

2012) an increase in ∆P which was attributed to clay swelling and/or fines mobilization as a

result of a prolongated exposition of the clay minerals to low salinity water. However, no

fines were detected in the effluent. On the other hand, a reduction in the pressure drop was

ascribed to oil redistribution as result of a favourable LSW.

Part of the contradictory evidence is mentioned by Lager (Lager al et., 2006) who did not

observe fines migration in the effluent or formation damage in their successful core flooding

experiments. This is supported by Ramez (Ramez al et., 2014) who during secondary

recovery core flooding experiments reported an increase of 12% of oil compared to high

salinity brine without increase in pressure drop, fine migration or damage. An additional

increase in pressure drop during tertiary LSW was attributed to fines migration when flooding

the core with low salinity brine. However, no fines were documented in the effluent.

pH variation

Tang and Morrow (Tang and Morrow al et., 1999) associated the pH increase with MIE and

carbonate dissolution. However, the authors did not mention the impact of MIE on oil

recovery. In 2005, McGuire (McGuire al et., 2005) linked the improve in oil recovery with

an increase in injected water pH which caused in-situ saponification reducing oil water IFT as

well as the reduction of the residual oil saturation. However, contradictory evidence from

Lager (Lager al et., 2006) who observed increase in recovery factor without an increase in pH

using oil of low acid number (AN<0.05) has also been documented. Lager and co-authors

mentioned that the even though the increase in pH can be responsible for surfactant

generation in situ, the levels needed to observe a link between the increase in the Hydrogen

Potential and oil recovery are not supported due to proton buffering and the common presence

of CO2 in most oils.

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Austad (Austad al et., 2010) who concludes that MIE is the dominant mechanisms behind the

increase in oil recovery, also suggested a limited increase in pH which can produce cation

desorption releasing bonded polar oil components resulting in additional recovery. Sorbie and

co-authors (Sorbie al et., 2010) support this increase in pH based on calcite dissolution and

cation exchange which is explained by the following mineral reaction and ion-exchange:

⇔ (3)

⇔ (4)

0.5 2 ⇔ 0.5 (5)

0.5 2 ⇔ 0.5 (6)

They mentioned that the present in the liquid phase will exchange with the previously

adsorbed divalent cations. As decreases in the liquid phase due to the exchange, the pH

increases. However, the authors conclude that the pH change is not enough to generate

sufficient surfactant to be the cause of oil increase supporting Lager observations (Lager al

et., 2006).

Fjelde (Fjelde al et., 2012) also reported an increase in the effluent pH as a result of calcite

dissolution but accompanied by minimal oil recovery when using LSW in tertiary mode.

When using LSWF in secondary mode, an increase in the effluent pH was reported ascribed

to carbonate minerals dissolution. This increase in pH coincides with the reduction in residual

oil saturation and a consequent increase in oil recovery but no direct link was documented and

the oil acid number was not reported.

Even after 24 years of the LSW research, there is no general agreement in the mechanism

responsible for the increase in oil recovery which can be the result of many effects such as

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clay swelling, fines migration, wettability alteration, dissolution of cementing material and

variation of the Z-potential at the interfaces fluid-fluid and fluid-rock. (Ramez al et., 2014)

MODELLING LOW SALINITY WATER INJECTION

Several authors have proposed different approaches to reproduce the LSW effects observed in

experiments performed at core, well and field scale with different degree of complexity.

However, most of the published algorithms are oriented toward MIE as the mechanism

responsible for the LSW effectiveness.

Jerauld (Jerauld et al., 2006) presented a modified version of VIP® simulator used in BP® to

simulate LSW which defines salinity as a component in the aqueous phase to be tracked and

used as interpolant to calculate the relative permeability curves at intermediate salinities. The

modification allowed the residual oil saturation to be set as function of salinity for more

flexibility and connate water banking was represented using inaccessible volumes.

Wu and Bai (Wu and Bai et al., 2009) developed a 1D numerical model to capture the low

salinity effect as function of the wettability alteration with changes in the relative permeability

to oil, contact angle, and the residual oil saturation assuming a linear relationship between salt

concentration (i.e. NaCl only) and the mentioned parameters. The proposed model treated oil,

water, gas and salt as mass components in their respective phases where salt is only

transported in the water phase by advection and diffusion. A single phase model was tested

against the analytical solutions for salt diffusion presented by Javandel (Javandel et al., 1984)

and Wu (Wu et al., 1996). In addition, a two phase model was set up being able to produce a

reduction in the residual oil saturation but no comparison against observed data or analytical

solutions was shown.

Sorbie and co-authors (Sorbie et al., 2010) first illustrate the LSW effect with Pore Network

theory in a water wet and a mixed wet system. In a water wet system, LSW will not be

15  

 

effective because to reach the same capillary pressure at different residual oil saturations (i.e.

HS and LS Sor), only the values of the interfacial tension and the contact angle might change.

However, the expected changes in interfacial tension are very small (Lager et al., 2006) and

the cosine of the contact angle in a water wet system is approximately 1 which means that no

significant changes are possible. On the other hand, in a mixed wet system where the largest

pores are oil-wet, a reduction in the residual oil saturation (i.e. at the same capillary pressure )

as result of LSW is possible and feasible if a reduction in the contact angle is observed which

translates into a more water wet surface. Following the previous description Sorbie (Sorbie et

al., 2010) presented a quantitative model to predict the effect of LSW. The proposed model

calculates a reduction in the residual oil saturation by using the expected (i.e. measured)

contact angles, pore size distribution and spread. The results of the quantitative model show

that for small changes in the contact angle an important reduction in the residual oil saturation

is observed. These observations are partially supported by Ramez (Ramez et al., 2014)

Omekeh and co-authors (Omekeh et al., 2011) proposed an extension of a 1D two phase

model to account for the transport, attachment and release of divalent cations onto and from

the rock. The released cations (i.e calcium and magnesium) are used as a limiting function

(i.e. maximum release F=0 and minimum release F=1) to define the high salinity and low

salinity relative permeability curves and interpolated shapes when 0<F<1. Even though the

authors presented results of the proposed model, the outcomes are not compared directly

against observed data.

Dang and co-authors (Dang et al., 2013) presented an ion-exchange and geochemical

reactions model incorporated into a compositional simulator GEM®. They focus on the

resulting common effect of the LSW: wettability alteration from oil-wet to water wet which is

captured by intra-aqueous reaction, mineral precipitation/dissolution and/or ion-exchange

processes suggesting that these mechanisms are the most important during LSW. The model

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is benchmarked against experimental data documented by Fjelde (Fjelde et al., 2012) and

Rivet (River et al., 2009) and also compared with PHREEQ-C, a recognized 1D geochemical

simulator.

The change in wettability is modelled by interpolating between high and low salinity relative

permeability curves which are previously defined from experimental data suggesting to use

the equivalent fraction of on the rock as the interpolant between the relative

permeability curves claiming that it provides a better match when it comes to represent

experimental data.

Kazemi (Kazemi et al., 2013) presented an algorithm to model LSW using geochemical

reactions and ion exchange integrating a 1D geochemical simulator, IPHREEQ-C, with

UTCHEM, a Chemical Simulator from the University of Texas at Austin. The proposed

workflow is based on an Implicit Pressure Explicit Concentration scheme where once

pressure and concentrations have been defined, the saturations of the components are

calculated for the next grid in a subsequent time step. The calculated concentrations (e.g. ion

concentration) will be used by the integrated IPHREEQ-C code to define an interpolating

parameter to modify the relative permeability and capillary pressure curves at each time step.

Even though the proposed model was validated against PHREEQ-C 1D simulations, it has not

been tested against experimental data and the authors mention that additional validations are

needed. In addition, tertiary LSW was modelled using different injected brines in a 1D model

assuming the concentration as the main ion responsible for wettability alteration and as

the relative permeability/capillary pressure interpolating parameter. The results obtained are

highly variable varying from an important reduction in the residual oil saturation to zero

effect mentioning that more research is needed using laboratory and field data as benchmark.

17  

 

Following their previous work and initial ideas, Kazemi and co-authors (Kazemi et al., 2014)

modified their previous algorithm to incorporate IPHREEQ-C with ITCOMP, a compositional

reservoir simulator created by the University of Texas at Austin to improve the initial results

obtained with UTCHEM. The authors took the suggestions of the work documented by

Zhang and Villegas (Zhang and Villegas et al., 2012) to capture the effect hydrocarbon

solubility in water and the geochemical effect on rock and aqueous phase to model

hydrocarbons as another geochemical component rather than as a phase allowing to model

acid/basic hydrocarbon components.

As in their previous work (Kazemi et al., 2013), the model solves pressure implicitly and

hydrocarbons molar components explicitly. The compositional simulator solves the mass

conservation equation to calculate the transport of geochemical components to subsequently

update the molar components of aqueous phase. Fugacities and hydrocarbon molar

components are then updated using IPHREEQ-C by calculating a new equilibrium condition

defining whether hydrocarbon components are solved in or evolved from the aqueous phase.

Then, the compositional simulator updates the molar composition performing flash

calculations until the process reaches convergence. At this point saturations are calculated and

geochemical outputs are used to calculate relative permeability and capillary pressure curve

modifiers.

As before, a 1D single phase model using UTCOMP-IPHREEQ-C was verified against a 1D

PHREEQ-C model injecting Endicott Field water chemistry with an excellent agreement

between modelled and observed data. In addition, the model was tested against coreflood

displacements performed by Kozaki (Kozaki et al., 2012) where the authors mention Sor,

Krw@Sor and relative permeability exponents as history matching parameters in a high

salinity water flooding. The LSW experiments were matched as well using the matched

parameters in the HSW, the total-ionic strength which is used as relative permeability

18  

 

interpolant, end points of the LSW curve, the minerals saturation index and the CEC of the

exchanger. The author does not provide final values or ranges of variation to judge the match.

The Endicott Field interwell results presented by Lager (Lager et al., 2011) were reproduced

as well using a cross-sectional model with 4 layers. The results show a very good agreement

with the measured ion concentration except for Barium and the pH, where the diminishing

trend was captured. The author does not mention history matching parameters as in the

previous case. Finally the author, tested the fraction of organo-metallic components on the

exchanger as interpolant parameter following the idea proposed by Lager (Lager et al., 2011)

obtaining different results to be analysed in more detail.

Modelling considerations

Jerauld (Jerauld et al., 2006) expressed that when low salinity brine is injected in the

reservoir, the injected brine can displace, mix or by-pass the formation brine stating that there

is no piston like displacement.

Salter and Mohanty work (Salter and Mohanty et al.,1982) identified dispersivity as a major

issue when modelling brines with different salinities especially in multiphase flow where

displacement and mixing occurs. This is observed by Jerauld (Jerauld et al., 2006) when

modelling LSW in a 1D system using different grid resolutions. Their results state that the

higher the dispersivity, the latter the recovery is observed and conclude that to match

displacement experiments at core and field scale the dispersion levels have to be properly

captured.

By using experimental data from Mahadevan (Mahadevan et al., 2003) where a linear trend of

dispersion versus distanced travelled was observed at core, well and field scale, Jerauld

(Jerauld et al., 2006) identified the optimum number of grid blocks between an injector and a

producer to be 10 to 25. However, they mention the difficulties associated with capturing

19  

 

mixing effects at field scale and the impact that heterogeneities may have in 3D systems.

Using the mentioned observations where the dispersion at core and well scale is only a

fraction (i.e. 2-5% ) of the respective length, they state that core displacement tests can be

directly used at field levels in a secondary low salinity water flooding when negligible water

influx from an aquifer is expected. A more detailed scaling procedure is recommended if the

low salinity effect is reduced in the reservoir as result of a higher degree of mixing.

Jerauld (Jerauld et al., 2006) performed 1D simulations using water slugs to simulate

conditions of limited low salinity brine supply to define the size of the slug. They

demonstrated that oil recovery is very dependent on physical dispersion and the smaller the

cells the optimum and smaller the slugs. When performing the simulations in a 2D quarter

spot system, they used numerical dispersion present in a coarse grid to simulation physical

dispersion with good agreement between fine and coarse grid recovery.

Jerauld (Jerauld et al., 2006) proposed the use of the high correlation observed between

incremental oil recovery and clay content at core and field scale to define a set of relative

permeability curves. The authors also stated that Single Well Chemical Tracer Test (SWCTT)

can be used to define real mixing at well scale event though the travelled distances are very

small.

Ramez (Ramez et al., 2011) mentioned that at reservoir conditions the presence of connate

water and/or high salinity formation brine saturation with divalent cations can diminish the

effect of injecting low salinity brine, and several pore volumes will be needed to produce the

potentials needed for enhanced recovery.

20  

 

RESULTS

Data summary

The present report used experimental data obtained at Heriot-Watt University which are

composed of measured effluent ion concentrations during a single phase core flooding

experiment (Table 1). In addition, a PHREEQ-C model which reproduces the behaviour of the

ion concentration variation is used to compare the results obtained by modelling LSW with

GEM® from CMG (Figure 3):

Brine / Ions (ppm) TDS

Formation 22000 238 90 230 35500 ~58058

Injection 588 14 1.75 19 941 ~1563

Table 1. Initial ion concentration in the formation and injection brine.

Figure 3. Effluent concentration matched with Phreeq-C (From Sabyrgali, 2012)

For the two phase system, the relative permeability curves were taken from CMG-GEM dat

file gsmso056 (see Figure 11). In an additional scenario, the model presented by Dang (Dang

0

5000

10000

15000

20000

25000

30000

35000

0

5

10

15

20

25

0 10 20 30 40 50 60

Na+

 Concentration

Ca++, K+, M

g++ concentration, ppm

Injected Pore Volume

PHREEQ‐C VS EXPERIMENTAL DATA

Ca+ Lab

Ca++ Phreeq‐C

K+ Lab

K+ Phreeq‐C

Mg++ Lab

Mg++ Phreeq‐C

Na+ Lab

Na+ Phreeq‐C

21  

 

et al., 2013) to reproduce Fjelde results was used (Fjelde al et., 2012) to further confirm some

of the observations.

Methodology

A compositional simulator with Ion Exchange and Geochemical reactions modelling

capabilities was used to match the measured effluent concentrations using a 1D single phase

model with 10, 20 and 40 cells. Sensitivities on grid resolution, rate injection, CEC, and

selectivity coefficients were carried out to optimize the match and identify critical parameters

while modelling LSW. Moreover, a 1D two phase model was set up in order to evaluate the

oil recovery factor with the matched core characteristics and sensitivities on the injected water

concentration were used to improve the LSW efficiency. Different relative permeability

interpolating functions as well as capillary pressure curves were used to observe the impact on

two-phase fluid flow against injected pore volume in secondary and tertiary recovery mode.

The model presented by Dang (et al., 2013) which reproduces the experimental observations

obtained by Fjelde (Fjelde et al., 2012) was used to support and verify the observations and

conclusions obtained by the initial approach in the two phase system.

For reference all the results have the HSW recovery to observe any benefit with LSW.

Results – Single Phase

To reproduce the experimental data, the following Ion-Exchange processes were set up in the

simulator to represent the ions present in the formation and injection brine:

(Na+) + 0.5(Ca-X2) = 0.5(Ca++) + (Na-X) (1)

(Na+) + 0.5(Mg-X2) = 0.5(Mg++) + (Na-X) (2)

(Na+) + (K-X) = (K+) + (Na-X) (7)

22  

 

The initial selectivity coefficients were 0.4 for the three ion exchange equations and the rock

CEC was 50 meq/L. The rock CEC will be defined by the results presented in the report as

direct measurements are not available.

Experimental data match

The results of the match obtained by using GEM® are shown in Figure 4. The concentration

of Ca, Mg, Na, is well captured by the simulator. However, the change in K concentration was

not reproduced between 1 and 10 Pore Volumes. The match was obtained by using a CEC of

350 meq/L and a selectivity coefficient of 0.12 for the sodium potassium exchange sites.

Figure 4. Ion concentration results from GEM® versus experimental data.

The increase of the CEC of the rock delays the ion detachment from the rock. On the other

hand, the selectivity coefficient increases the amount of ions available for exchange and

consequently affects the amount of ions that are detached and attached from and onto the rock

as shown in Figures 5 and 6

0

5000

10000

15000

20000

25000

30000

35000

0

5

10

15

20

25

30

0 10 20 30 40 50

Na+

 Concentration

Ca++, K

+, M

g++ Concentration, ppm

Injected Pore Volumes

GEM VS EXPERIMENTAL DATACEC 350 ‐ STCH 0.1 

Ca+ Lab

Ca++ GEM CEC 350STCH 0.12

K+ Lab

K+ GEM CEC 350STCH 0.12

Mg++ Lab

Mg++ GEM CEC 350STCH 0.12

Na+ Lab

Na+ GEM CEC 350STCH 0.12

23  

 

Figure 5. Cation Exchange Capacity sensitivities

Figure 6. Selectivity coefficient sensitivities

0

5000

10000

15000

20000

25000

30000

35000

0

5

10

15

20

25

30

0 10 20 30 40 50 60

Na+

 Concentration

Ca++, K+, M

g++ Concentration, ppm

Injected Pore Volumes

GEM VS EXPERIMENTAL DATACEC 300 vs 350 ‐ STCH 0.1

Ca+ Lab

Ca++ GEM CEC 300

Ca++ GEM CEC 350

K+ Lab

K+ GEM CEC 300

K+ GEM CEC 350

Mg++ Lab

Mg++ GEM CEC 300

Mg++ GEM CEC 350

Na+ Lab

Na+ GEM CEC 300

Na+ GEM CEC 350

0

5000

10000

15000

20000

25000

30000

35000

0

5

10

15

20

25

30

0 10 20 30 40 50 60

Na+

 Concentration

Ca++, K+, M

g++ Concentration, ppm

Injected Pore Volumes

GEM VS EXPERIMENTAL DATACEC 350 STCH 0.1 VS STCH 0.4

Ca+ Lab

Ca++ GEM CEC 350 STCH 0.1

Ca++ GEM CEC 350 STCH 0.4

K+ Lab

K+  GEM CEC 350 STCH 0.1

K+ GEM CEC 350 STCH 0.4

Mg++ Lab

Mg++ GEM CEC 350 STCH 0.1

Mg++ GEM CEC 350 STCH 0.4

Na+ Lab

Na+ GEM CEC 350 STCH 0.1

Na+ GEM CEC 350 STCH 0.4

24  

 

Grid resolution

The sensitivities displayed in Figures 7 and 8 show the dependence of the CEC on the grid

resolution. The CEC doubles if the cell size is decrease by a factor of two. This statement was

confirmed by using 10, 20 and 40 cells between the injection and the effluent site observing

that the CEC is 175, 350, and 700, respectively, using the same selectivity coefficient, 0.12.

To set up the model 40 cells model, the injection rate was reduced from 3 cc/hr to 1.5 cc/hr

due to the high throughput in the cells when using cells of reduced ∆X between the injection

and production sites.

Figure 7. Grid resolution sensitivities on CEC. 10 cells vs 20 cells

0

5000

10000

15000

20000

25000

30000

35000

0

5

10

15

20

25

30

0 10 20 30 40 50

Na+

 Concentration

Ca++, K+, M

g++ concentration, ppm

Injected Pore Volumes

GEM VS EXPERIMENTAL DATAGRID RESOLUTION:10 CELLS CEC 175 ‐ 20 CELLS CEC 350

Injection Rate: 3 cc/hr

Ca+ Lab

Ca++ 10 CELLS ‐ CEC 175 STCH 0.12

Ca++ 20 CELLS ‐ CEC 350 STCH 0.12

K+ Lab

K+ 10 CELLS ‐ GEM CEC 175 STCH 0.12

K+ 20 CELLS ‐ GEM CEC 350 STCH 0.12

Mg++ Lab

Mg++ 10 CELLS ‐ GEM CEC 175 STCH 0.12

Mg++ 20 CELLS ‐ GEM CEC 350 STCH 0.12

Na+ Lab

Na+ 10 CELLS ‐ GEM CEC 175 STCH 0.12

Na+ 20 CELSS ‐ GEM CEC 350 STCH 0.12

25  

 

Figure 8. Grid resolution sensitivities on CEC. 20 cells vs 40 cells

0

5000

10000

15000

20000

25000

30000

35000

0

5

10

15

20

25

30

0 10 20 30 40 50 60 70 80

Na+

 Concentration

Ca++, K+, M

g++ concentration, ppm

Injected Pore Volumes

GEM VS EXPERIMENTAL DATAGRID RESOLUTION 20 CELLS CEC 350 ‐ 40 CELLS CEC 700

Injection Rate: 1.5 cc/h

Ca+ Lab

Ca++ 20 CELLS ‐ CEC 350 STCH 0.12

Ca++ 40 CELLS ‐ CEC 700 STCH 0.12

K+ Lab

K+ 20 CELLS ‐ GEM CEC 350 STCH 0.12

K+ 40 CELLS ‐ CEC 700 STCH 0.12

Mg++ Lab

Mg++ 20 CELLS ‐ GEM CEC 350 STCH 0.12

Mg++ 40 CELLS ‐ CEC 700 STCH 0.12

Na+ Lab

Na+ 20 CELSS ‐ GEM CEC 350 STCH 0.12

Na+ 40 CELLS ‐ CEC 700 STCH 0.12

26  

 

Rate sensitivities

The rate sensitivies results are shown in Figure 9. Two injection rates, 1.5 and 3 cc/h were

used with the model of 20 cells to assess the effect of the injection rate on the ion exchange

process and therefore on the effluent concentration. The results indicate that there is no effect

of the injection rate on the ion exchange process and that the ion exchange processes

modelled is at equilibrium.

Figure 9. Injection rate sensitivity results on MIE.

0

5000

10000

15000

20000

25000

30000

35000

0

5

10

15

20

25

30

0 10 20 30 40 50 60 70 80 90 100

Na+

 Concentration

Ca++, K+, M

g++ concentration, ppm

Injected Pore Volumes

GEM VS EXPERIMENTAL DATA20 CELLS ‐ CEC 350 ‐ STCH 0.1

Injection Rates: 1.5 cc/hr vs 3 cc/hr 

Ca+ Lab

Ca++ 20 cells 3.0 cc/hr

Ca++ 20 cells 1.5 cc/h

K+ Lab

K+ 20 cells 3.0 cc/hr

K+ 20 cells 1.5 cc/hr

Mg++ Lab

Mg++ 20 cells 3.0 cc/hr

Mg++ 20 cells 1.5 cc/hr

Na+ Lab

Na+ 20 cells 3.0 cc/hr

Na+ 20 cells 1.5 cc/hr

27  

 

Phreeq-C comparison

The calculations obtained by GEM were compared with PHREEQ-C calculations as general

benchmark (Dang al et,. 2013, Kazemi al et., 2014). The results show that GEM produces a

better match in the calcium concentration compared with PHREEQ-C. This is an important

conclusion compared to previous publications that only show agreements but not

improvements with respect to the geochemical simulator. See Figure 10.

Figure 10. Comparison of Phreeq-C results vs GEM

0

5000

10000

15000

20000

25000

30000

35000

0

5

10

15

20

25

30

0 10 20 30 40 50 60 70 80

Na+

 Concentration

Ca++, K+, M

g++ concentration, ppm

Injected Pore Volumes

GEM VS PHREEQ‐C VS EXPERIMENTAL DATA

Ca+ Lab

Ca++ 20 CELLS ‐ CEC 350 STCH 0.12

Ca++ 40 CELLS ‐ CEC 700 STCH 0.12

Ca++ Phreeq‐C

K+ Lab

K+ 20 CELLS ‐ GEM CEC 350 STCH 0.12

K+ 40 CELLS ‐ CEC 700 STCH 0.12

K+ Phreeq‐C

Mg++ Lab

Mg++ 20 CELLS ‐ GEM CEC 350 STCH 0.12

Mg++ 40 CELLS ‐ CEC 700 STCH 0.12

Mg++ Phreeq‐C

Na+ Lab

Na+ 20 CELSS ‐ GEM CEC 350 STCH 0.12

Na+ 40 CELLS ‐ CEC 700 STCH 0.12

Na+ Phreeq‐C

28  

 

Results – Two Phase System

With a matched effluent concentration, a 1D two-phase model was set up with oil of the

following characteristics using a 3 parameters Peng Robinson Equation of State :

Parameter Unit Value Pressure PSIa 4500 Temperature F 160 Viscosity @ 4500 PSIa cP 0.42 Volumetric oil factor @ 4500 PSIa Ad 1.17 Density lb/cu.ft 45

Oil Composition C1 0.2 C4 0.2 C10+ 0.6

Table 2. Oil composition for the two phase system.

The water injection rate is 3 cc/h with the model at initial water saturation using equilibrium

conditions for pressure and saturation. The relative permeability curves used for the different

sensitivities are shown in Figure 11.

Figure 11. High salinity and low salinity relative permeability curves (From CMG-GEM)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

RELATIVE PER

MEA

BILITY

WATER SATURATION

HS & LS RELATIVE PERMEABILITY CURVES

HS KRW

HS KRO

LS KRW

LS KRO

29  

 

Before starting the sensitivities two general QCs were carried out. First the CEC was switched

off to observe if no Ion-Exchange was taking place. Second, formation brine was injected to

look for changes in the residual oil saturation. The results are shown in Figure 12.

Figure 12. General QC of the MIE process.

Interpolant Sensitivities

Equivalent Fraction: The equivalent fraction of Na, K, Mg and Ca were used as interpolants

to modify the input LS and HS relative permeability curves and to observe the effect on

recovery factor versus injected pore volume as shown in Figure 13 and Figure 14. The

equivalent fraction of K produces the early recovery followed by Mg, Na and Ca. Figure 13

displays the total injected pore volume needed to achieve equilibrium conditions while Figure

14 shows the oil recovery factor between 0 and 10 injected pore volumes to take into account

a more realistic and economic scenario.

30  

 

Figure 13. Effect of the Equivalent Fraction as Interpolant on Recovery Factor

Figure 14. Effect of the Equivalent Fraction as Interpolant on Recovery Factor

31  

 

Aqueous Concentration: As before, the aqueous concentration of Na, K, Mg, Ca, and Cl

were used as interpolants to observe the effect on recovery factor versus injected pore volume.

As shown in Figure 15, there is no important difference when the aqueous concentration is

used to calculate the intermediate relative permeabilities and the recovery factor goes straight

to the highest recovery values after 0.55 injected pore volumes.

Figure 15. Effect of the aqueous concentration as Interpolant on Recovery Factor

CEC Sensitivities

The CEC of the model was changed diminishing its value from 350 to 50 eq/m3. This

sensitivity was carried out using the equivalent fraction of Ca and Mg as interpolant. The

results shown in Figures 16 and 17 indicate that the lower the CEC, the earlier the oil

recovery.

32  

 

Figure 16. Effect of CEC on Recovery Factor. EQVFRAC Ca.

Figure 17. Effect of CEC on Recovery Factor. EQVFRAC Mg.

33  

 

To support these observations, the model presented by Dang which reproduces the

experimental results obtained by Fjelde was used. The model shows the same behaviour as

the CEC is changed from 50 to 300 meq/L but in a much lesser extent due to its different

configuration as shown in Figure 18.

Figure 18. Effect of CEC on Recovery Factor. Fjelde Experiment

Capillary pressure sensitivities

The results presented so far do not consider capillary pressure. The sensitivities performed

include the use of a single capillary pressure set for HS and LS using different equivalent

fractions as interpolants. In addition, 3 sets of synthetic capillary pressure curves (Figure 19)

were used with the equivalent fraction of Ca as interpolant.

34  

 

 

Figure 19. Synthetic capillary pressure curves used for sensitivities on Recovery Factor

The first results show that when capillary pressure is used the water breakthrough is observed

a bit ealier but the oil recovery is faster. This behaviour is similar for all the equivalent

fractions except for K, where the water breakthrough is earlier and the recovery factor is

delayed in comparison with the zero capillary pressure case when the entire range of injected

pore volume to reach equilibrium conditions is used (see Figure 20). Figure 21 shows a

different scale to observe the impact of capillary pressure in a more realistic, economic range

where in all the cases capillary pressure brings forward the water breakthrough.

1, 01, 01, 00

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

CAPILLA

RY PRESSU

RE

WATER SATURATION

CAPILLARY PRESSURE 

PC 1

PC 2

PC 3

35  

 

 

Figure 20. Effect of capillary pressure on oil recovery factor (0-100 PV). (Blue Line considers capillary pressure)

 

Figure 21. Effect of capillary pressure on oil recovery factor 0 - 5 PV. (Blue Line considers capillary pressure)

36  

 

The second sensitivity shows that when using the equivalent fraction of Ca as interpolant, for

the same saturation, the higher the capillary pressure the more delayed the oil recovery (see

Figure 22).

 

Figure 22. Capillary pressure sensitivity results using Ca EQV FRAC as interpolant.

Injection brine optimization

To optimize the injection brine two general approaches were followed. First, the

concentration of Na, Ca, Mg, and K were decreased 5 and 10 times and simulated as

individual cases using its respective equivalent fraction as interpolant but keeping the TDS

concentration at ~1563 ppm adding Na ions when possible (e.g. All the injected brine

composition remain the same except for Ca, which is divided by 5 and 10 and its Equivalent

Fraction is used as interpolant). The objective of this scenario is to find the ion which has

more impact on MIE and consequently on oil recovery.

37  

 

Second, a different injection fluid was considered by diluting the formation brine

concentration 50 and 100 times which results in low salinity brine of 1161 and 580 ppm

respectively. The composition of the injection brines are detailed in Table 2:

Brine / Ions

(mol/L)

(mol/L)

(mol/L)

(mol/L)

(mol/L)

TDS

(ppm)

Formation 0.9892 0.006138 0.003826 0.00608 1.015 ~58058

Injection 0.027153 0.00371 7.6e-5 0.000516 0.028178 ~1563

Optimization 1 (8 Individual scenarios,

Cl- not considered)

5 0.0054 0.00742 0.0000152 0.0001032 0.028178 ~1563

10 0.00271 0.000371 7.6e-6 0.0000516 0.028178 ~1563

Optimization 2 (2 Scenarios)

50 0.019784 0.00012276 0.00007652 0.0001216 0.0203 ~1161

100 0.009892 0.00006138 0.00003826 0.0000608 0.01015 ~580

Table 3. Brine composition sensitivities

The results of the first scenario show that when the ion concentration of Ca, Mg and Na is

reduced 10 times with respect to the base case the recovery is brought forward, especially

when Mg is reduced. The opposite behaviour is observed when K is reduced as the oil

recovery is delayed. (see Figure 23, 24, 25, 26). A comparison of the cases where the ions

were diluted ten times is shown in Figure 27 for a general analysis.

38  

 

 

Figure 23. Injection Brine Optimization. Ca++ concentration reduced 5-10 times.

 

Figure 24. Injection Brine Optimization. Mg++ concentration reduced 5-10 times.

39  

 

 

Figure 25. Injection Brine Optimization. K+ concentration reduced 5-10 times.

 

Figure 26. Injection Brine Optimization. Na+ concentration reduced 5-10 times.

40  

 

 

Figure 27. Injection Brine Optimization. Comparison of 10 times diluted cases.

In the second scenario, the dilution of the formation brine 50 and 100 times delays the oil

recovery with decreasing concentration of ions in all the cases except with K Equivalent

Fraction as interpolant between 0 and 2 injected pore volumes (See Figures 28, 29, 30 and

31).

41  

 

 

Figure 28. Injection Brine Optimization. Formation brine diluted 50-100 times. Ca++ as Interpolant.

 

Figure 29. Injection Brine Optimization. Formation brine diluted 50-100 times. K+ as interpolant

42  

 

 

Figure 30. Injection Brine Optimization. Formation brine diluted 50-100 times. Mg++ as interpolant

 

Figure 31. Injection Brine Optimization. Formation brine diluted 50-100 times. Na+ as interpolant

43  

 

In this range, the 50 times diluted formation water produces the higher and earlier recovery. A

comparison of the cases with 50 and 100 times diluted formation brine with different

Equivalent Fractions are shown in Figures 32 and 33.

 

Figure 32. Injection Brine Optimization. Formation brine diluted 50 times. Comparison

 

Figure 33. Injection Brine Optimization. Formation brine diluted 100 times. Comparison

44  

 

LSW in Tertiary Mode

The same model was used to observe the impact of LSW in tertiary mode. Initially Formation

Brine concentration was injected and after 10 pore volumes when the model was at High

Salinity residual oil saturation, low salinity brine was injected. The results show that K and

Mg produce the earliest increase in recovery followed by Na and Ca. (See Figure 34)

 

Figure 34. Low salinity water injection in tertiary mode.

45  

 

DISCUSSION

Results – Single Phase

Experimental data match

The match was obtained by varying the rock CEC and the selectivity coefficient of the rock.

The final value for the CEC is 350 meq/L with 20 cells while the selectivity coefficients are

0.5, 0.4, and 0.12 for Na - Ca, Na - Mg and Na - K exchanges respectively.

The CEC of the rock can be translated into meq/(100 grams of rock) to relate its value to

petrophysical dimensions (Mian al, et., 1986):

CECQ ∙ 100 ∙ φ1 φ ∙ ρ

Where

CEC

;φ fractionalporosity;ρ sandgraindensity

By doing so, the CEC of the rock is 3.09 meq/(100 grams of rock) which is in the range of

rocks with kaolinite content (Ramirez al, et., 1990). However, no additional data is available

to confirm this.

The reduction in the selectivity coefficient for the Na-K ion exchange helps to reproduce the

K concentration in the effluent especially the increase after 10 PV to around 23 ppm of K

concentration. However, the sustained behaviour between 1 and 10 PV is not reproduced by

GEM or PHREEQ-C. Additional considerations in terms of mineral reactions will be needed

to capture this response from the system.

The selectivity coefficient of 0.12 is interpreted as the ratio of ions on the exchange site which

means that K is more preferred than Na on the clay surface. This could partially explain the

increase in K ions on the rock and the later detachment observed in the effluent.

46  

 

Grid resolution

The statement presented in the results suggests a method to upscale the CEC of the rock when

it comes to refine or coarsen a grid. As such, if the grid cell sizes between injector and

producer wells are reduced to a half, the CEC has to be doubled in order to maintain the ion

exchange process of the rock and do not alter the interpolated relative permeability and/or

capillary pressure curves used. However, this observation is only supported at core scale

based on experimental observations and only numerically at field scale due to the lack of data.

Rate sensitivities

The injection rates at core scale are highly dependent on the pore volumes of the cells to

which the injector well is connected. At core scale, the higher the rates, the more important

the compositional and molar changes per time step which can be translated in numerical

convergence issues. An additional statement related to the ion exchange independence of

injection rates is proposed as no variations in the matched effluent concentrations were

observed at core and field scale supported only by core scale observations. However, the

author is aware that additional heterogeneities together with diffusion and dispersion

phenomena have to be considered when modelling at larger scale.

Phreeq-C comparison

The results are comparable against Phreeq-C outcomes and show an improvement in the

calcium and magnesium concentration match compared to the proposed model by PHREEQ-

C. This gains significance as previously published simulation algorithms have not shown

improvement over PHREEQ-C representations.

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Results – Two Phase System

Relative Permeability Interpolant Sensitivities

Equivalent Fraction: The earliest oil recovery obtained when using K Equivalent fraction as

interpolant can be explained with the behaviour depicted in the effluent concentration which

translates in a very rapid attachment and detachment of K ions onto and from the rock

compared with other ions. This behaviour reaches very fast the extreme of the interpolant

function switching quickly to low salinity relative pemeabilities. This observation is part of

the sensitivity analysis and do not constitute an optimization procedure.

When it comes to model two phase flow, literature (Dang et al., 2013) on the other hand

suggest the use of Calcium EQVFRIEX as interpolant based on experimental observations for

two phase flow.

Because the MIE mechanism states that monovalent and/or multivalent cations will exchange

with divalent cations bonded to oil molecules and the clay surface (Lager al, et., 2006), the

modelled mechanism should use either Ca or Mg equivalent fraction as interpolant. However,

this should be confirmed by experimental coreflooding before upscaling and forecasting

improve in oil recovery by LSW.

The results are presented in two different scales to observe the total injected pore volume

needed to see the total LSW effect reaching new equilibrium conditions. However, in a more

realistic scenario only few pore volumes will be injected if water of low salinity is available

due to economical reasons.

Aqueous Concentration: The aqueous concentration on the other hand produced very similar

results in the range 0-2 PV due to the rapid fall in the ion concentration toward the injected

values. Even though the ion concentrations are used to depict ion exchange in a single phase

system, when used as interpolant parameters the variation in its values are independent from

48  

 

the other ions (i.e. not a fraction) which switches the curves to the low salinity side very

quickly.

CEC Sensitivities: By analysing the CEC capacity behaviour identified in a single phase

system, it is possible to infer the reason why the recovery is speeded up in comparison with its

initial value as the changes in the equivalent fraction of the divalent ions are brought forward

which swaps the relative permeability curves.

The results obtained by using the model proposed by Dang (Dang et. al., 2012) using Fjelde

experimental observations (Fjelde et. al., 2012) confirm this behaviour in a system with

calcite dissolution and ion exchange but in a much smaller magnitude as shown in Figure 18.

However, as confirmed by observations (Tang and Morrow et al., 1999; Sorbie et. Al., 2010;

Zheng at al. 2014), the CEC defines the amount at exchange sites on a rock which are a

fundamental part of the MIE mechanism. So far in the published literature a threshold value

which defines the minimum CEC needed to produce the LSWE has not been identified and

this correlation has not been observed experimentally and further analysis is required.

In addition, as documented by Basin and Labrid (Basin and Labrid et al., 1991) high CEC

values are related with potential damage during the injection of a low divalent cation

concentration brine compared with the formation brine where K-X is replaced by sodium and

divalent cations over a 10 PV range being determinant in smectite stability and consequently

permeability decrease.

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Capillary pressure sensitivities

The capillary pressure sensitivities bring a new consideration in terms of modelling as the

published workflows to capture the LSWE do not mention and/or take into account capillary

pressure effects.

The first results show that when capillary pressure is used, water breakthrough is observed a

bit earlier but the oil recovery is faster. This behaviour is similar for all the equivalent

fractions except for K, where the water breakthrough is earlier and the recovery factor is

delayed in comparison with the zero capillary pressure case, Figure 19 and 20.

The early water breakthrough is explained due to the diffuse front caused when capillary

pressure is applied in the system in the short injected pore volume range, 0-2 PV. By contrast,

the faster recovery (i.e. after 2PV) is caused due to the more exchange sites available as water

diffuses in the system which alters the equivalent fraction of the interpolants. The equivalent

fraction of K on the other hand, produces much less recovery compared with its base case as

the available exchange sites are reduced and taken by other ions.

The second sensitivities using Ca and Mg equivalent fractions as interpolants with synthetic

capillary pressure curves confirms the previous observations where a faster water break

through is observed. In addition, this case states that for higher capillary pressure at the same

saturation the earlier the water breakthrough. However, this has not been documented in LSW

experiments.

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Injection brine optimization

The results obtained by decreasing the ion concentration of Na, Ca, Mg, and K as individual

scenarios with their respective equivalent fractions as relative permeability as interpolants

showed that a decrease in Mg concentration produces the earliest recovery followed by Ca.

This coincides with the observations documented by Lager (Lager et al., 2006) where a low

divalent cation concentration is recommended in the injection brine for the MIE process to be

more efficient. A decrease in Na does not produce any change with respect to its base case

due to its higher concentration. By contrast, a decrease in the K concentration reduces

drastically the LSWE compared with its base case as this reduces the Ion – Exchange process

of K as observed by Sabyrgali (Sabyrgali et al., 2012).

On the other hand, the result obtained by diluting the formation brine 50 and 100 times did

not improve the oil recovery mainly due to the small amounts of Sodium ions which are

needed to replace and detach the oil bonded molecules to divalent cations (Lager et. al.,

2006). This behaviour was observed with all the cations equivalent fractions in the range 0-2

PV, except when K – Equivalent Fraction is used as interpolant. In this case, in the range of 0-

2 PV, the use of the K-Equivalent Fraction as interpolant gives earlier and higher recoveries

compared to the base case. However, after 2 injected pore volumes, there is a decrease in

recovery explained by a decrease in the slope of K exchange.

LSW in Tertiary Mode

The results obtained during LSW in tertiary mode confirm the secondary mode LSW results

where K produced the earliest recovery. This response is again ascribed to the preference of

the rock for K compared with other ions.

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This results show the high degree of variation in recovery even when the system is in residual

oil saturation and show the pore volumes needed to see a complete change from HS – LS

conditions.

CONCLUSIONS

The Ion- Exchange Model implemented in GEM can reproduce the experimental observations

performed at Heriot-Watt University by representing the MIE mechanism of LSW in a single

phase system.

The CEC and the selectivity coefficients were used to match the experimental data which

have different effects on the Ion-Exchange process.

A general scaling factor to modify the CEC of the rock is presented as well as the parameters

that do not interfere in the Ion-Exchange process at core and field scale in a 1D system.

A general dependence of the rock CEC against oil recovery was found supported by a

published simulation model based on experimental data.

The sensitivities on the equivalent fraction of different ions as interpolants result in important

ranges of recovery with different amounts of PV needed to reach new equilibrium conditions.

This suggests that before it is possible to make predictions with a LS model, laboratory tests

are needed to calibrate the simulation outcomes.

Economical considerations are vital when deciding to implement a LSW as many pore

volumes may be needed before observing a successful EOR mechanism which is supported

with the reported results.

The brine optimization process performed in the current report agrees with experimental

observations and requirements for LSWE.

52  

 

SUGGESTION FOR FURTHER WORK

Several LSW publications are available which can be used as general QC of LSW modelling

at core scale to understand and capture the final effect on oil recovery before applying the

process at field scale.

An important suggestion for further work would be the inclusion of mineral reactions together

with the Ion-Exchange model in order to identify the sustained behaviour of the K

concentration in the effluent which was not matched with the current approach.

Even though MIE is reported in many experimental observations, it is mentioned to be a

secondary mechanism which comes after several injected pore volumes (Nasralla et al., 2014).

A suggestion would be to reproduce those observations using the current simulation tools and

assess numerically the extent to which the MIE mechanism is involved in the increase of

recovery factor.

The inclusion of minerals such as Kaolinite which is present in most of the reported

observations is highly recommended as well as the analysis of additional variables such as

pH, which can locally contribute to an increase in oil recovery at core scale.

The experimental results documented by Fjelde (Fjelde al, et., 2012) and reproduced by Dang

(Dang al, at., 2013) can be used to perform sensitivities on the mineral content, CEC, pH to

support empirical correlations.

Analyse the effect of physical dispersion in the results as this could lead to a reduction in the

expected recovery factor which could be important at field scale (Secombe al, et., 2006).

The use of PHREEQ-C as an additional tool to capture the exchange of ions and the mineral

reactions occurring in the rock has shown interesting results as the software is an important

reference when it comes to represent laboratory data. The inclusion of the PHREEQ-C code in

open code standard reservoir simulators could lead to an important advance in terms of EOR

modelling as reported in literature (Korrani et al., 2013, Korrani et al., 2014)

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