SPE 142960 Analysis of Field Responses to Low-salinity Waterflooding in Secondary and Tertiary Mode in Syria H. Mahani, T.G. Sorop, D. Ligthelm, and A.D. Brooks, SPE, Shell Global Solutions International; P. Vledder, SPE, Petroleum Development Oman; and F. Mozahem, Y. Ali, Al-Furat Petroleum Company Copyright 2011, Society of Petroleum Engineers This paper was prepared for presentation at the SPE EUROPEC/EAGE Annual Conference and Exhibition held in Vienna, Austria, 23–26 May 2011. This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.

Abstract Low-salinity water injection is an emerging IOR/EOR technique, applicable to mixed-to-oil-wet sandstone reservoirs. This paper describes the field response for two large fields: Omar (secondary flood) and Sijan (tertiary flood). The data were analyzed using analytical and numerical modelling tools. This included evaluation of scaling numbers, mixing and dispersion and calibration. Insight was obtained on relevant drive mechanisms. The responses to low-salinity flooding differ for the two fields: • In Omar, a dual-step water-cut development was observed, which is characteristic for a change in wetting state. Our interpretation is that in this field, viscous forces provide the dominant drive mechanism, which is favorable to low-salinity flooding. We were able to history match the low-salinity response using a simple conceptual model. • In Sijan, the low-salinity flood appears to be still immature and breakthrough of low-salinity water has not (yet) been observed. The reasons for the muted response thus far are explored, including a rather strong buoyancy effect caused by the higher permeability of the block, and the significant effect of injectant mixing with the highly saline aquifer. A proposal is made for a workflow on how to apply this analysis to future low-salinity flooding implementation in field cases. Introduction Low-salinity (LS) waterflooding (in general Designer WaterTM flooding (DWF)) is a novel IOR/EOR technology which has been developed to improve microscopic sweep efficiency with reduction in remaining oil saturation. Low-salinity flooding is defined as injection of a low-saline brine with well-manipulated ionic composition into a sandstone reservoir (with appropriate clay mineralogy such as kaolinite and illite) that contains relatively saline formation water, particularly containing a fair concentration of bivalent cations. LS flooding into mixed-wet to oil-wet reservoirs causes wettability modification towards a more water-wet state. LS waterflooding is a low-cost, low-CO2 footprint IOR/EOR technology and is operationally (virtually) identical to conventional waterflooding. Additional oil recovery gain from LS water flooding varies depending on the history of water injection in the field and reservoir complexities. Recent field evidences indicate that the estimated incremental oil recovery of LS flooding can be as much as 5-15% of STOIIP in field scale in Syrian fields (Vledder et al. 2010). Recent publications reporting both laboratory (Boussour et al. 2009, Yousef et al. 2010) and field experiments (Seccombe et al. 2010, Skrettingland et al. 2010) indicate that this technology is gathering momentum in the oil industry. In Shell there is an intensive program to deploy this technology in many potential candidate fields. Spontaneous imbibition and unsteady-state core flow experiments for a number of Syrian oil fields as well as one single-well Log-Inject-Log experiment (Vledder et al. 2010) have shown that LS water may improve on oil production by wettability modification. Consequently, some of these fields may be considered for implementing LS waterflooding. In addition there are other fields, such as Omar and Sijan, in which LS water injection was not originally intended as IOR technique, but rather used due to other operational reasons. Obviously these fields provide a good opportunity to compare the field data with the laboratory results and test the current understanding of the theoretical models used to describe low-salinity.

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For the Omar field an evidence of wettability alteration due to LS water injection over a period of 10 years (1992-2002) was recently presented in Vledder et al. 2010. The work used a combination of SCAL, NMR, imbibition and open-hole log data around producers at virgin, intermediate and final conditions, and suggested that wettability alteration from the initial oilwet state to a rather water-wet state may have occurred. From the changes in the remaining oil saturation between these conditions, an incremental recovery of 10-15% of STOIIP was estimated using an analytical assessment. In addition, a successful Log-Inject-Log experiment in a watered-out producer has been performed in the Isba field (which is analog in terms of initial wetting state and salinity) showed a reduction in remaining oil saturation by 14% pore volume (PV). Sijan is also a field analog to Isba, in terms of the very high-salinity of the formation water (TDS in excess of 200,000 ppm), low connate water saturation, and similar geology, and is, therefore, currently thought to be oil-wet as well. In this field in 2005, after re-injecting produced water for more than 10 years, low-salinity water injection was started, with TDS of less than 500 ppm, in one of the main producing blocks in Sijan field, the block Sijan 105. While the need for starting a low salinity waterflood has been dictated by operational issues (namely not having enough produced water available to support the pressure in the reservoirs) an obvious question was whether this operation change would have also lead to the expected increase in recovery factor. The current work builds upon our recent laboratory and experimental studies, and aims to model, simulate and, eventually, history match the field responses on LS flooding in two particular blocks from the above mentioned fields: one in Omar (block OMA103) and the other in Sijan (block SIJ105). As part of the study, analytical and simulation models were built to understand the impact of in-situ mixing and dispersion on the LS flooding performance. The outcome of this study can be used as a guideline and recipe in the design and simulation of future field scale LS waterflooding. Geological models At the time of this study, detailed geological models were not available for these fields and therefore our first step in the study was to construct conceptual models. Omar field. A schematic cross section of Omar field is shown in Figure 1. Omar field comprises two main sandstone formations: Cretaceous Lower Rutbah (RUL) and Triassic Mulussa-F (MUF). The RUL is formed of high net-to-gross ratio (NTG) of ≈ 73%, sheet-like, shallow marine sands locally with tidal channels. In contrast, the underlying low NTG MUF consists of good quality single and stacked fluvial channels within shales. In the OMA103 block investigated in this study, on the west of the structure, the majority of STOIIP is contained within the RUL sands, with minor disruption from faulting and igneous bodies in comparison with the complexity of the Omar main field. The aquifer is however inactive. Furthermore, the open-hole logs indicate that this sand has fairly uniform permeability over the whole thickness, leading to a good lateral connectivity between wells. Moreover, it has been observed that in the central block of Omar field, all drainage points have high water-cut (see Neidhardt et al. 2008 and Vledder et al. 2010), which is also a good indication of continuity. These field evidences support the idea of describing OMA103 block with the concept of a single geological unit of the type non-uniform, homogeneous (as defined in Greenkorn and Kessler 1969 and Allen Alpay 1972). From the fluid flow point of view, this conceptual model assumes that the uniformity of flood/saturation front is not severely affected by small scale heterogeneities and good vertical sweep is achieved. Therefore, this model can be described by only one equivalent permeability, porosity, net-to-gross, and rock curves. However heterogeneities are still considered in numerical flow simulations by introducing dispersivity.

Figure 1: Schematic cross section of Omar Field

Sijan field. The Sijan field comprises multiple isolated fault-dip closures with different fluid levels and slightly different crude characteristics, but sharing a common aquifer. The block of interest is SIJ105, in the centre of the field, but believed to be largely isolated from the other blocks. Production dominates from the Lower Rutbah (RUL). This reservoir is a sandstone-

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dominated succession, with minor siltstones and mudstones. Controls on reservoir quality are primarily depositional, with the best reservoirs consisting of distributary and tidal channel deposits, with very high permeabilities (up to 10 Darcy) within other, poorer quality lower coastal plain sediments. The average NTG of the whole interval is around 70% with average porosities of 13%. Open-hole logs in the wells drilled in SIJ105, show fairly uniform permeability over the reservoir, implying that a good lateral connectivity exists between wells. Similar to the Omar case, we use the same approach of performing the analysis on a single non-uniform, homogeneous geological unit. We should mention, however, that SIJ105 contains also a significant water-leg at the bottom of the reservoir and that the wells are distributed in a line-drive configuration. Analytical study: Force Balance and Scaling Numbers Evaluation It is well-established that oil-water displacement is influenced by the inter-play between three forces: viscous, gravity and capillarity. The viscous force is caused by the pressure gradient between injector and producer, the gravity force by the density-difference between fluids, while the capillary force is caused by the interfacial tension between fluids. While at the core scales (including lamina scale) the main forces impacting displacement are viscous and capillary forces, at reservoir scales viscous and gravity forces often dominate. The main reason for that is that at reservoir scales the capillary gradient along the flow direction is often negligible compared to the viscous and gravity gradients. Nevertheless, the capillary effect can still play a role in the vertical direction (i.e. the direction perpendicular to the flow direction). The main purpose of this analytical study is to estimate what are the major forces that are likely to be dominating in the reservoirs and what would their likely impact be on the recovery process. Furthermore, this analysis lays the basis for numerical modelling by defining the conceptual models. We argue below why this analysis is of particular importance for LS flooding. LS flooding has been described using the fractional flow/Buckley-Leverett method, based on the idea of wettability modification. However, this type of analysis is, strictly speaking, only valid if the flow in the reservoir mainly in the along dip direction, i.e. of 1-D type. If the flow is of 2-D type, because the water-oil gravity drainage may play an important role in the system, then the predictive power of fractional flow analysis can decrease significantly. At the extreme, if gravity would dominate over the other two forces, water/oil gravity drainage behind the displacement front is expected to occur causing fluid segregation. This would lead to accumulation of upswept oil at the top of the layer, thus leaving oil at a saturation close to the residual saturation in the swept zone behind the displacement front. Our in-house experience suggests that in such cases, given the high efficiency of the gravity assisted displacement, the LS induced wettability change may not lead to a significant additional gain in recovery, while in a viscous dominated flow regime, the incremental oil recovery due to introduction of LS flooding can be considerable. Scaling numbers definition To assess the impact of crossflow on the vertical saturation distribution of reservoir fluids (which primarily move in one lateral direction, mostly horizontally) one needs to compare two timescales: the time needed for a fluid particle to move in the horizontal direction over a length L (along the flow direction) vs. the time needed for the same fluid particle to move in the vertical direction over a distance H (perpendicular to the flow direction). Based on this comparison three scaling numbers are defined, following the approach of Lake 1989, and Wellington and Vinegar 1985: • the gravity number (gravity force/viscous force), Nρ/μ , • the capillary number (capillary force/viscous force), NPc /μ, and • the vertical equilibrium number ((gravity +capillary forces)/viscous forces), NVE. These scaling numbers were estimated for the geological units defined with the parameters as given in Tables 1 and 2 for both Omar block 103 and Sijan block 105. The calculations were performed assuming a typical flood front velocity of ≈1 ft/day in the reservoir. In absence of reliable core flow SCAL data, analog relative permeability curves for both HS and LS were inferred from history matching of the single well Log-Inject-Log experiment in the Isba field, while making use of in-house SCAL correlations which related relative permeability characteristics to a.o. wettability. It was implicitly assumed that these relative permeability data are equally applicable to both Sijan and Omar fields, given the close analogy with Isba field (e.g. similar type of formation and brines). The objective of this study was to find out to what extent the field observations may be explained by the use of these analog relative permeability curves. Subsequently a major SCAL program has been initiated to qualify these data, to reduce uncertainty and obtain improved HS and LS relative permeability curves, although the results are not yet available. Omar field. For Omar 103 the scaling numbers analysis suggests the relative dominance of viscous forces over buoyancy and capillary forces. In fact, for the base case scenario the viscous force is estimated to be more than two times larger than the buoyancy force, while both are much larger than the capillary force. A parametric/sensitivity analysis shows that under a reasonable range of uncertainty, the largest force in the system remains the viscous force. Only for certain cases, such as when the well distance is beyond 2km, or kv/kh is unrealistically high (≈1.0), the gravity force may become the dominating force in the system. Since those cases are not likely for Omar, this implies that displacement takes place under diffuse flow condition and, therefore, fractional flow calculations are quite relevant. Indeed the results of these calculations show that a high-salinity flooding would be expected to leave a large amount

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of oil behind (the average oil saturation behind the shockfront is estimated to be =0.59), even though the displacement is under favourable mobility ratio (mobility ratio based on SPE definition (Craig 1975) is 0.39). Accepting HS and LS relative permeability curves as given in Table 3, and applying LS water injection in secondary mode, the same analysis suggests that an additional oil recovery of 20% of STOIIP (at 99% water cut) compared to HS (secondary mode) is gained. Obviously this analysis is based on 1D analytical calculations which ignore the effects of mixing and dispersion and streamline pattern on the oil recovery. However the scaling numbers indicate that the oil recovery process is mainly affected by the areal sweep effects, and significantly less by the vertical sweep effects. Therefore, based on this analysis, a 2D areal model (as opposed to a vertical model) is selected as our conceptual numerical model for Omar 103 block. Table 1: Input parameters used for scaling numbers analysis for Omar 103 and Sijan 105 (base case scenarios) Parameter Expected average permeability Average porosity Vertical to horizontal permeability (kv/kh) Dip angle Injector-producer distance Average thickness of formation Oil viscosity at reservoir condition Formation brine viscosity at reservoir condition Formation water density Oil density Interfacial tension oil-water at reservoir condition

Unit mD Radian m m mPa.S mPa.S 3 Kg/m 3 Kg/m N/m

Omar 42 0.1 0.1 0.12 1000 100 0.3 0.24 1070 628 0.025

Sijan 1000 0.13 0.1 0 1000 100 1.50 0.39 1100 850 0.02

Table 2: Input Relative permeability curves used in the analysis (Omar 103 & Sijan 105) Relative permeability curves HS brine-oil Residual oil saturation (Sorw) Connate water saturation (Swc) Krocw Krwro nw no Relative permeability curves LS brine-oil Residual oil saturation (Sorw) Connate water saturation (Swc) Krocw Krwro nw no

Omar 0.22 0.08 0.7 1.0 2.0 6.0

Sijan 0.17 0.025 0.7 1.0 2.0 6.0

0.19 0.08 1.00 0.33 3.62 2.88

0.17 0.025 1.00 0.33 3.60 2.90

Sijan field. Fractional flow/Buckley-Leverett analysis, performed on the geological unit representative for Sijan block 105, suggests a potentially high benefit for low-salinity, even for the case of tertiary mode. However, as mentioned above this analysis ignores the effect of gravity and capillary forces, being only valid if the diffuse flow conditions would be met. In order to check the flow conditions, a scaling numbers analysis was performed in a similar manner as for Omar field. The main differences when compared to Omar field are the high permeability (1000 mD compared to 42 mD) and the higher viscosity (1.5 cp compared to 0.3 cp). The results show that in Sijan block 105 it is likely that the gravity plays a (very) important role, with the buoyancy force being stronger than the viscous force and much stronger than the capillary force. The base case estimated NVE is around 2.1. The parametric analysis performed supports the robustness of the finding. For the whole range of sensitivities the gravity remains the dominating force with two notable exceptions. Namely, NVE is significantly lowered below 1 (and, therefore, under viscous force dominated diffuse flow) only if the horizontal permeability is significantly smaller and the front velocity is much larger than the values used for the base case scenario. It requires an average horizontal permeability (kh) significantly lower than 100 mD, or a front velocity, , well in excess of 1 m/day. However, field data from Sijan have indicated that a layer of 60 - 100 m in thickness has an average permeability of around 1000 mD, likely extending all the way to the producers (at more than 1 km away) and that the front velocity would be rather of the order of 0.3 m/day. If this scenario is correct, then the main assumption made in the fractional flow calculations, of diffusive flow, is not expected to hold. Instead, one expects a partially segregated flow, with NVE values not much higher than 1. While for the case for a fully segregated flow (where NVE >> 1), the additional increase of the sweep efficiency provided by the low-salinity may, actually, be very small or even non-existent, it is not yet well established what will happen in cases where NVE 1, i.e the buoyancy is dominant, but the flow is only partially segregated. The answer to this question can be explored by performing numerical simulations on 2D cross-sectional (i.e. vertical) models that will be further discussed in the next section.

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Remarks about the validity of the analytical results: The approach is based on the important assumption that the flow related heterogeneities can be described with the concept of the single (non-uniform, homogeneous) geological unit. We note that this concept implicitly assumes separation of length scales, which is a simplifying assumption that will not be satisfied in general. Moreover, it is important to realize that the scaling numbers are estimated at a single fixed point in the reservoir at a water saturation level Sw. When changes in these values occur in the reservoir (and they do occur during flooding) the ratios may change as well. Therefore, while very useful in understanding the balance of forces and the displacement mechanism, the scaling numbers only give rough indications on the relative time-scales associated with the various forces. Furthermore, the analytical models presented here neglect viscous fingering, mixing/dispersion and streamline pattern effects. A detailed understanding of the impact of these factors requires performing numerical simulations, of the type shown in the following section. Numerical Modelling using Conceptual models Simulation approach The numerical simulations presented in this paper have been performed using an in-house Shell reservoir simulator. The method of simulating low-salinity flooding and the effect of wettability change consist of defining an extra "slug phase", which is assigned to the LS (injection) water, in addition to the "water phase", which is assigned to the HS (formation) water. Two sets of relative permeability functions are used: the "oil-water phase" is oil-wet and the "oil-slug phase" is water-wet. The wettability change is described as following: when the "slug" of LS water invades a certain grid block in the reservoir the wettability changes suddenly if the volume fraction of the slug within the grid block is higher than a predefined volume fraction threshold value, f, which we call the "mixing factor". This factor is defined as:

Mixing factor = f =

TDSformation water − TDS * TDSformation water − TDSinjection water

where TDSformation water = salinity level of formation water (ppm) TDSinjection water = salinity level of injection water (ppm) TDS * = Threshold salinity level for wettability change

The threshold salinity level (TDS*) is based on the laboratory experiments and it is estimated to be of the order of 3000 5000 mg/l, in Omar and Sijan. Given the very high contrast in salinity between the injection water and formation water in both Omar and Sijan, f is estimated to be above 0.9, which means that a very significant dilution of HS formation water is required before any wettability modification can occur. Note that the no mixing case can be modeled correctly by assigning f = 0.5. Mixing and dispersion of LS water into HS formation water is mimicked in our numerical simulator by making use of the numerical dispersion effect. Following the approach proposed by Lantz (1971), we equate numerical dispersion (which is the result of truncation error) to the physical dispersion. For miscible flooding and Implicit numerical simulation scheme, the , where ∆ is the grid block size, , the flood front velocity and ∆ formula for numerical dispersion is is the timestep size. The calibration was performed by adjusting ∆ and ∆ in order to match the physical dispersion for the field cases. Prior to history matching, the models were calibrated for dispersion effect and benchmarked against the Buckley-Leverett fractional flow results. The simulation models were chosen according to the scaling number evaluation results. In Omar field, the viscous forces appear to be dominating and, therefore, we can assume that gravity effects are of less importance in this field. This implies that areal sweep pattern is mainly determining efficiency of LS flood, rather than vertical sweep. This forms the basis of selecting a 2D areal model for the numerical simulations. On the other hand, in Sijan field, the gravity force appears to be the dominant force in the system and the vertical sweep effect on the efficiency of LS process is significantly higher than the areal sweep. This coupled with the fact that the injectors and producers are arranged in a line-drive configuration led us to select a 2D vertical model for the numerical simulations. In what follows we will discuss/present separately the results of the numerical simulations for each field. Numerical modelling for Omar field Field data. General information about Omar field is given in Table 3. The block of interest, Omar 103 in the east of Omar field, is shown in Figure 2. This block consists of one injector, OMA1361N in the south and three producers, OMA1391N, OMA1032N, and OMA1251T in the north of the block. The injector is supporting all the producers, with all wells being vertical. LS water injection (TDS ≈ 500 mg/l) in Omar 103 (see injection profile in Figure 3) commenced in 1994 and lasted for 4 years. In 1998, since sufficient water was produced from the fields to the Omar Central Processing Facilities (CPF), it was decided to mix this produced water (of high salinity of the order 100,000 mg/l or higher) with the low-salinity water. The

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resulting salinity of the combined injection water was more than 10,000 mg/l, well above the threshold salinity for wettability modification.

Figure 2: Omar 103 block, cumulative oil production and water injection volumes are indicated per 1.7.2008.

Table 3: General information about Omar field Formation

Lower Rutbah, Recovery factor ~ 50% STOIIP

Gross thickness NTG Porosity In-situ Oil viscosity

Up to 120m 70-75% 10-18% 0.3 cp

Bo

2.0

Formation water salinity (TDS) LS water injection in Omar 103 block (injection into oil leg, no aquifer support) TDS of injected water

>90,000 ppm 1994-1998 500 ppm

During the period of LS water injection, clear oil bank signals have been observed from dual step water-cut development, and from saturation logs: in the whole Omar field in 14 wells and in Omar 103 block clear responses in 2 producers. All these water-cut dual-step behaviors were observed before 1998, whereas after switching to mixed water injection, the water breakthrough showed only a single step. The average first and second water-cut from all these wells during the LS water flood are 16% (range 5-42%) and 38% (range 24-66%), respectively. The average step change in water-cut is 22% which is quite significant and we do not believe that it can be attributed to an operational error. The average oil saturation around producers drilled at different times in the field has also been logged and analyzed (Vledder et al. 2010). According to Vledder et al. 2010, these field data indicate a high-initial-oil saturation around 0.92, an average oil saturation during connate water bank between 60-80% and an average oil saturation in fully watered-out producers around 15-35%. Based on these average oil saturations, the oil recovery factor is around 65% which is 13-22% more than that expected from HS injection (simple BL calculations would ideally result in 45% oil recovery at 99% abandonment water-cut for such a HS injection). These field evidences indicate that a significant change in wettability from a rather oil-wet to a rather water-wet state is likely to have occurred in the Omar field. In the next section, we will check whether these field observations can be qualitatively explained by numerical simulations which are based on the analog relative permeability curves given in Table 3. History-matching of Omar 103 block In Figure 3 the water-cut development of OMA125IT and OMA1391N is shown. The dual-step water-cut observations for these wells were before 1998. After 1998 when the mixed water injection period started, water breakthrough showed only single step. The first step occurs when the HS formation water bank arrives at the producer, while the second step occurs at the time of LS water injection breakthrough. However in absence of reliable salinity measurements in the field, this cannot be confirmed. One observation in the water-cut profile of OMA125IT is that both water-cuts steps are rather sharp, indicating that

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the flow between OMA1361N and OMA125IT could be close to linear. In OMA1391N, however, the second water-cut increase is more gradual which could imply some 2D (areal sweep) effects. 1.0

OMA1251T

Watercut [-]

0.8

0.6

0.4

0.2

Nov-95

Aug-95

May-95

Feb-95

Nov-94

Aug-94

Feb-94

May-94

0.0

1.0

OMA1391N

Watercut [-]

0.8

0.6

0.4

0.2

Sep-00

Mar-00

Aug-99

Jan-99

Jul-98

Dec-97

Jun-97

Nov-96

May-96

Oct-95

Apr-95

0.0

Figure 3: OMA1391N and OMA1251T water-cut profiles (top-hole data)

Based on these observations and the results of the scaling numbers analysis (which show that the viscous forces are dominant) we chose for Omar a 2D areal model for the reservoir. The simulations were performed using a single grid block in the vertical direction (basically implying that the gravity effects are neglected) and a uniform grid areally. However, the effect of heterogeneities in the field (and the associated dispersion effect on the flood front profile) was captured by adapting grid resolution according to the choice of dispersivity (Lantz 1971). Furthermore, the model included all injectors and producers in the block and was initialized according to the initial fluid distribution in the block. Injection rate and total liquid (oil plus water) production rates were used as well constraints. To history match the response, the original rock and fluid properties were used. HS and LS relative permeability curves as given in Table 3 were used and were not altered during the history matching process. Dispersivity and mixing factor were used as the matching parameters. Water-cut values from numerical simulations were compared with field responses as a measure for the quality of match. Produced water salinity data was not available to include for history matching. The best matches to the field water-cut fractions are shown in Figure 4 for two of the producers. One can see that the simulations fit reasonably well the field data (note that given the simplicity of the conceptual model used here we do not

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expect a perfect fit; a better fit may be achieved by adding geological complexities to the model). This supports the idea that, indeed, low salinity water causes wettability change in the reservoir. The mixing factor extracted from the simulations is ≈ 0.90. This high value is caused by the high-contrast in salinities between LS and HS water for the Omar field. The dispersivity obtained from the match is around 4m, which is in agreement with data from the field correlation charts (Gelhar 1992, Schulze-Makuch 2005, John et al. 2008), which suggests dispersivities of the order of 5-10m for a 1000m reservoir scale. The Peclet number (well distances/dispersivity= L/αl) calculated based on the matched dispersivity and well distances is ≈ 180 for OMA1251T, and 400 for OMA1391N. These high Peclet numbers imply sharp displacement profiles (between LS injection water and HS formation water) in which mixing and dispersion would not smear out significantly the displacement front. This is important, since it implies that less additional pore-volumes of LS are required to dilute and displace the in-situ HS formation brine compared to the case where Peclet number is significantly lower. It is noteworthy that mixing and dispersion can be significant if the connate HS formation water saturation is high or if there has been a pre-HS waterflood in the field (i.e. tertiary LS injection mode). Due to the facts that in Omar field, connate water saturation is low (< 0.1) and injection was at secondary mode, mixing and dispersion (in the reservoir away from the well) are not expected to be significant at this stage of injection from 1994-1998. This makes application of LS more economically attractive in this field.

OMA1251T

OMA1391N

Figure 4: Comparison of real and simulated water-cut data for OMA125T and OMA139N

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Numerical modelling for Sijan block 105 General information about the block of interest in Sijan field, block 105, is given below in Table 4. Table 4: General information about Sijan block 105 Formation

Lower Rutbah

Gross thickness NTG Porosity In-situ Oil viscosity

≈100m 70-75% ≈13% 1.5 cp

Formation water salinity (TDS) LS water injection (injection into oil leg & the supporting aquifer) Injection water salinity (TDS)

>200,000 ppm 1994-2005 500 ppm

The block consists of two vertical injectors (SIJ-113 and SIJ-122) and 4 producers (two horizontal, SIJ-126 and SIJ-1130, and two vertical SIJ-105 and SIJ-135), all aligned in a line-drive configuration. The injector SIJ-122 is perforated only in the water-leg while, SIJ-113 is perforated both in the oil-leg and the water-leg. Half-way between the injector SIJ-113 and the producer SIJ-126 there is a non-sealing fault.

INJECTOR 2 INJECTOR 1

500 m

919 m

1432 m

SIJ-126

SIJ-105

1858 m

3065 m

2316 m

Non-sealing f ault

SIJ-113

2760 m

SIJ-130

SIJ- 122

SIJ-135

2767m 2794m

2830 m 2840 m

2864 m

OWC

TVD = 2882 m

TVD = 2926 m

2930 m

SIJ-113 injecting in SIJ-122 is injecting oil column+ water column only in water column Figure 5: Cross-sectional view of SIJ105 block, indicating the various wells and their distance from the injectors.

Between 1994 and 2005 high-salinity (TDS > 200,000 mg/l) produced water was re-injected in this block. In 2005, the asset team decided to switch to low-salinity water injection with TDS ≈500 mg/l, just like in Omar. The modelling of LS flooding was performed in a similar way as in the case of Omar. There are two main differences, however. First, as already mentioned, for Sijan we chose a 2D vertical conceptual model (compared to the 2D areal model for Omar). Secondly, low-salinity is applied in tertiary mode (vs. secondary mode in Omar). Consequently, the expected field signature is not a double-step water-cut, but rather a temporary decrease in water-cut when the oil bank mobilized by the LS would reach the producer (Pope, 1980). The numerical simulations were performed in two steps: 1. The first step was to construct a simple 2D conceptual model, which ignores the presence of the water-leg in form of an aquifer. 2. In the second step, we improve the geological model by taking into consideration the presence of a significant water-leg in the reservoir block. The main reasons for having the intermediate step (1) in the process is that it allowed us to immediately compare the results with the 1D simulations and to separate the effects caused purely by buoyancy, from the loss of efficiency caused by injecting in the water-leg.

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We first discuss the main results obtained for step 1. First, the model predicts that, in presence of buoyancy, as expected, significant water under-running would occur in Sijan block 105. Consequently, the flood front is less sharp, leading to earlier breakthrough. As a result, the water-cut fraction changes are no longer sharp, instead, they change gradually. Higher NVE values also result in an (overall) increase in the recovery factor. This is in agreement with the well established effect of segregation during displacement, expected to lead to a more effective sweep of oil. However, since this process occurs irrespective of salinity of the water, the question was whether the LS flooding in tertiary mode would add any additional gain in cumulative oil to the process. While for segregated displacement (NVE >> 1) there are evidences that this gain is minimal (if any), as mentioned already in the previous section, it was not clear whether the same is true for systems (such as Sijan 105) where NVE 1. Numerical simulations were, therefore, performed to address this issue. 1.00

Water cut [-],

0.80

0.60 Nve = 0.2 Nve= 2.1 0.40

Nve = 205

0.20

Water‐cut drop due  to arrival of oil  bank at producer

0.00 0.0

1.0 2.0 Water injected (in PV)

3.0

0.80

Cumulative Oil (in PV)

0.60

Nve = 0.2 0.40

Nve= 2.1 Nve = 205

0.20

0.00 0.0

1.0 2.0 Water injected (in PV)

3.0

Figure 6: Water-cut fraction and cumulative oil, as estimated in the step (1) in Sijan simulations, as function of the vertical equilibrium number.

The results of these simulations (see in Figure 6 the simulated producer water-cut and cumulative oil responses for one of the producers) show that, in the absence of an aquifer, LS in tertiary mode in Sijan block 105 was expected to still produce a significant additional gain (compared to the HS flooding only), even for large values of NVE (>100). This is more clearly illustrated in Figure 7, where the LS additional gain is plotted as function of NVE.

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0.30

LowSAL gain ( in PV)

0.25 0.20 0.15

Additional gain due to low  salinity in tertiary mode  decreseas  if the strength  of the  buoyancy force  increases 

0.10 0.05 0.00 0.1

1

10

100

1000

Nve (vertical equilibrium number) Figure 7: Additional LS gain in tertiary mode compared to HS waterflooding as function of vertical equilibrium number in SIJ105 (simulations step (1)-no aquifer)

This seems somewhat surprising, but we believe that the high gain in cumulative oil (LS vs. HS) at NVE = 0, and the slow decrease when increasing NVE may be related to the nature of the analog relative permeability curves used in the simulations (see Table 3). These curves reflect an initial (very) oil-wet state and a subsequent change to almost fully water-wet state. Indeed, this would imply that a high value of NVE (> 1000) would be required to reduce the gain below 5%. Further we discuss below the results of the step 2. In this step we have included an aquifer in the model as shown in Figure 5. However, the fraction of water injected in the aquifer (faquifer) at injector 1 is not known. We, therefore, performed a parametric study as a function of this parameter. 0.30

Additional gain in cumulative oil (in PV oil)

Nve = 2.1

0.20

0.10

0.00 0.00

0.20

0.40

0.60

0.80

1.00

fraction of LS water injected in aquifer

Figure 8: Additional LS gain in tertiary mode for SIJ105 as function of water injected in the aquifer.

The simulations show that the presence of the water-leg/aquifer changes very significantly the water-cut profile, the breakthrough times and the sweep efficiency. The results shown in Figure 8 clearly indicate that a higher faquifer reduces significantly the cumulative oil gain due to LS, to values well below 5% if faquifer is higher than 0.7. Note here that the cumulative oil gain was estimated at a water fraction cut-off of 95%.

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0.80

Nve = 2.1

Cum OIL (in PV oil)

0.60

0.40

no aquifer f(aquifer) = 0.2 f(aquifer) = 0.5

0.20

f(aquifer) = 0.7 Low‐salinity  injection (0.62 PV)

0.00 0.00

0.50

1.00

f(aquifer) = 0.9

1.50

2.00

Water injected (in PV) 1.00

0.80

Water-cut (-)

Nve = 2.1 0.60

no aquifer f(aquifer) = 0.2 0.40

f(aquifer) = 0.5 f(aquifer) = 0.7 f(aquifer) = 0.9

0.20 Low‐salinity  injection (0.62 PV)

0.00 0.00

0.50

1.00

1.50

2.00

Water injected (in PV)

Figure 9: Profiles of cumulative oil and water-cut fractions for LS in tertiary mode in SIJ105 as function of the different amount of water fraction in the aquifer.

At the same time, the breakthrough of the oil bank mobilized by the LS water in tertiary mode leads to a drop in water-cut, with its magnitude decreasing when faquifer is increased (Figure 9). For faquifer > 0.7, the water-cut drop is less than 5%, being reduced from almost 60% at faquifer = 0 (i.e. in the absence of the aquifer). Note that some field indications seem to suggest that faquifer for SIJ105 may be well in excess of 0.5. This has significant implications for the field, since it implies that the effect of low salinity is delayed and it may, in fact, be quite difficult to detect, requiring, amongst others, very stable operations (i.e. maintaining a steady state injection and production in the wells for a significant amount of time). Moreover, the breakthrough of this oil bank is very much delayed for high faquifer values (changing from 0.8 injected PV for faquifer = 0, to more than 1.2 injected PV in case of faquifer = 0.9). Even more problematic for the detection point of view is the fact that the breakthrough of the first flood front (the high-salinity front) appears to be masked by the water coning effect, which leads to water production almost immediately after the start of production. Based on these results we can clearly conclude that in case of Sijan block 105 the benefits of the low-salinity flooding are expected to be significantly reduced by: • the presence of a strong buoyancy force, and even more by • the injection of a significant amount of water in the water-leg. The main reason for this is that injecting LS water in the HS water leg, brings up the overall salinity of the injected fluid and reducing it below the threshold level (required to produce wettability change) would require very significant volumes of

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injectant (the volume fraction of the injectant should be locally more than 10 times more than the volume fraction of formation water in order to change wettability). Sofar no clear response of the producer seems to have been identified in the field. An initial drop in water-cut seen 1 year after the switch from high-salinity to low-salinity (appearing instantaneously in two of the four producers) has been interpreted as being caused by operational issues. Rather it seems possible that the oil bank has not yet arrived at the producers but this may take place in the future. Summary and Conclusions A combination of analytical and numerical modelling approach was used to analyze low-salinity waterflooding in two Syrian fields: Omar and Sijan. In both cases, the use of low-salinity water was dictated by operational reasons rather than as an IOR/EOR technique. Nevertheless these field cases provide very useful sets of data which we have used to compare with the current understanding of this technology. In the absence of static and dynamic models for these fields, the approach was to perform the analysis on 2D conceptual models. Field evidences from both Omar and Sijan suggested that the use of the geological unit concept (as proposed in Greenkorn and Kessler 1969, and Allen Alpay 1972) is a valid, at least as a starting point for a wider analysis. The analytical modelling consisted of a fractional flow calculation combined with a scaling numbers analysis. The results of this analysis were used to choose the appropriate numerical model, for instance the choice between the areal model (for the Omar case in which the viscous force was dominating) vs. vertical model (for the Sijan case in which the gravity force was dominating). In Omar, after calibrating the numerical model for both mixing and dispersion phenomena, a history matching of the field data (including producer water-cut fraction and cumulative oil production) was successfully performed for the block Omar 103. The simulations, indeed, show that the dual-step water-cut development observed in the producers is probably caused by wettability change in the reservoir, induced by the low-salinity flooding applied in secondary state. Furthermore, our interpretation is that, in this field, viscous forces provide the dominant driving mechanism, which appears to enhance the lowsalinity gain. The fact that the simple conceptual model could capture the main signatures of the low-salinity flood and provide a reasonably good history match of most of the data gives us confidence in the approach employed. Furthermore, important additional learnings could be extracted from this analysis. For instance, the high Peclet number, estimated from the dispersivity match, suggests that the flood front between the low-salinity and high-salinity brines is rather steep and the mixing effect in the reservoir is small. In Sijan block 105 no clear field response on the low-salinity flooding in tertiary mode has been identified so far. The conceptual simulations performed in the same way as for Omar, but using a 2D vertical model, clearly showed that two important factors were expected to significantly reduce the benefits of LS flooding. Firstly, the presence of a strong buoyancy force (significantly larger than the viscous force and much larger than the capillary force) is expected to lead to a partially segregated flow and, while it is likely to increase the overall recovery factory of the field during waterflooding, it is expected to significantly decrease the additional gain due to LS flooding. Secondly, due to the very high contrast in salinities, injecting large amounts of low-salinity water in the high-salinity formation water-leg leads to a significant decrease in efficiency of lowsalinity flooding. In fact, the simulations show that this effect has more impact than gravity on the water-cut, as it delays its arrival, and it ultimately reduces the cumulative oil gain to a value that is well below 0.05 PV. Although we cannot rule-out that such an effect may be seen in the near future, we expect to be small and, perhaps, difficult to detect in the field. Finally, we can conclude that in spite of their relative simplicity, the conceptual models employed in this work appear to capture the relevant physics of the systems and allow us to identify the main displacement mechanisms in these two fields. However, we realize that there is a need to acquire reliable HS and LS relative permeability curves in order to improve on the predictive power of the models. Currently a large SCAL program is in place to address this. Furthermore, the use of more detailed geological models and additional surveillance data (for instance, measurements of produced water salinity and chemistry) is expected to yield further improvement. We believe that the step-wise approach taken in this study, using a combination of analytical (scaling number analysis and Buckley-Leverett fractional flow analysis) and numerical modelling (constructing an appropriate conceptual geological model of the block and calibrating that model for mixing and dispersion effects for simulation purposes), can be applied to other fields as a workflow for screening of the fields for low salinity flooding. Acknowledgments The authors gratefully acknowledge Al-Furat Petroleum Company for permission to use their field data, and wish to thank Shell Global Solutions International B.V. for permission to publish this work. Nomenclature Bo = Oil Formation Volume Factor faquifer = fraction of water injected in the aquifer Krocw= End Point Relative Permeability for Oil at Connate Water Saturation Krwro= End Point Relative Permeability for Water at Residual Oil Saturation kh = Horizontal Permeability

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kv = Vertical Permeability Nρ/μ = Gravity to Viscous Number NPc /μ = Capillary to Viscous Number NVE = Vertical Equilibrium Number NTG = Net to Gross Ratio no = Oil Corey exponent nw = Water Corey exponent Sorw = Residual Oil Saturation to Water Sw = Water Saturation Swc = Connate Water Saturation = Average Oil Saturation behind Shock Front TDS = Total Dissolved Solids (Salinity) = Front Velocity References Allen Alpay, O., A Practical Approach To Defining Reservoir Heterogeneity, Journal of Petroleum Technology, July 1972, pp. 841 - 848. Boussour, S., Cissokho, M., Cordier, P., Bertin, and H., Hamon, G., Oil Recovery by Low Salinity Brine Injection: Laboratory Results on Outcrop and Reservoir Cores, SPE 124277, 2009. Craig, F.F., The Reservoir Engineering Aspects of Waterflooding, Volume 3, Society of Petroleum Engineers, 1975. Gelhar, L.W., A critical review of data on field-scale dispersion in aquifers, Water Resources Research, Vol. 28, No. 7, pp. 1955-74, 1992. Greenkorn, R.A. and Kessler, D.P., Dispersion in Heterogeneous Nonuniform Anisotropic Porous Media, Industrial and Engineering Chemistry, vol. 61, no. 9, September 1969, pp. 14 - 32. John, A.K., Lake, L.W., Bryant, S.L., Jennings, J.W., Investigation of Field Scale Dispersion, SPE 113429, presented at the 2008 SPE/DOE Improved Oil Recovery Symposium, Tulsa, Oklahoma, USA, 19-23 April 2008. Lake, Larry W., Enhanced Oil Recovery, Prentice Hall, 1989. Lantz, R.B., Quantitative Evaluation of Numerical Diffusion (Truncation Error), SPEJ, Vol. 11, No. 3, pp. 315-320, 1971. Neidhardt, J., Farran, H., Gonzalez, I., Vledder, P., Doughry, Y., The Omar Field (NE Syria) is overcoming its mid-life crisis, SPE-112940, 2008. Pope, G., The Application of Fractional Flow Theory to Enhanced Oil Recovery, SPE 7660, 1980. Schulze-Makuch, D., Longitudinal dispersivity data and implications for scaling behaviour, Ground Water, Vol. 43, No. 3, pp. 443-456, 2005. Seccombe, J., Lager, A., Jerauld, G., Jhaveri, B., Buikema, T., Bassler, S., Denis, J., Webb, K., Cockin, A., and Fueg, E, Demonstration of Low-Salinity EOR at Interwell Scale, Endicott Field, Alaska, SPE 129692, 2010. Skrettingland, K., Holt, T., Tweheyo, M.T., and Skjevrak, I., Snorre Low Salinity Water Injection - Core Flooding Experiments and Single Well Field Pilot, SPE 129877, 2010. Vledder, P., Fonseca, J.C., Wells, T., Gonzalez, I., Ligthelm, D., Low Salinity Water Flooding: Proof of Wettability Alteration on a Field Wide Scale, SPE 129564, presented at the 2010 SPE Improved Oil Recovery Symposium, Tulsa, Oklahoma, USA, 24- 28 April 2010. Yousef, A.A., Al-Saleh, S., Al-Kaabi, A., and Al-Jawfi, M., Laboratory Investigation of Novel Oil Recovery Method for Carbonate Reservoirs, CSUG/ SPE 137634, 2010. Wellington, S.L. and Vinegar H.J., CT Studies of Surfactant-induced C02 Mobility Control, SPE 14393, 1985