immiscible co2 injection for enhanced oil recovery and...

31st Annual Workshop & Symposium IEA Collaborative Project on Enhanced Oil Recovery

Improved Polymer Flood Management Simulation using Streamlines

Joseph ABDEV1, Torsten CLEMENS1, Marco THIELE2 1OMV E&P

Trabrennstr. 6-8, 1020 Vienna, Austria [email protected]

2STREAMSIM Technologies 865 25th Avenue San Francisco, CA94121, USA

Abstract

Recently, modern streamline simulation has been extended to polymer flooding. In this work, we extended the applicability of streamline simulation further to efficiently manage polymer injection projects. We first present our methodology and then demonstrate the applicability of our approach using a Romanian field as an example. Owing to the price of polymers, an optimised injection strategy is crucial to minimise costs, maximize sweep, and thereby ensure profitability of a polymer flood.

The biggest advantage of streamline simulation compared to traditional finite-difference modelling is that polymer floods can be optimised on a well pattern basis. By being able to plot cumulative oil produced as a function of polymer injected on an individual well pattern basis, under performing patterns—injectors and associated offset producers—can easily be indentified and rates, concentration, and slug size modified to improve the local efficiency of the flood. In this paper we introduced a new metric: the polymer injection efficiency as a function of time for each pattern and show how it is central to contain costs while retaining or even improving oil recovery.

We demonstrate our methodology on a Romanian oil field that has been operating since 1961 with 20 injectors and 136 producers. We show that the utility factor of polymers injected versus incremental oil produced can be reduced while sustaining oil recovery. We also show how the study was aided by overall faster simulation times and well allocation data available on a per pattern basis. We conclude with a discussion on streamline simulation as an enabling technology for reservoir management decisions for polymer floods and floods in general.

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mailto:[email protected]


Introduction One key parameter for the gauging the efficiency of waterflooding projects is the mobility ratio of the injected water and the oil in the reservoir (Craig 1971, Dake 1978, Willhite 1986). This ratio describes the relative mobility of the displacing phase to that of the displaced phase. Floods with a mobility ratio less than one are considered stable, those with a mobility ratio greater than one are considered unstable. Waterflooding of reservoirs having mobility ratios around one, yields substantial incremental oil recovery compared to primary production. Oil fields exhibiting mobility ratios far above one have also been subjected to waterflooding, but due to the high mobility of water compared to the oil, water will generally break through early and yield smaller incremental oil recovery. A summary of waterfloods of viscous oils has been described by Belivau (2009). To improve recovery from such reservoirs, close well spacing, sufficient throughput of injected water, production at high water cuts and proper reservoir surveillance are all required. Feng et al. (2009) showed that good reservoir characterisation and dynamic adjustment of injection and production schedule is very important to achieve better water-injection efficiency. Polymer flooding has been used to further improve recovery from viscous oil fields. In this case, a water-soluble polymer is mixed with the injected water to increase the water viscosity and thereby lower the mobility ratio. A number of successful polymer floods have been reported in the literature (Needham and Doe 1987, Littman 1988, Putz et al. 1994, Dong et al. 2008). More than 250,000 bbl/d have been produced by polymer injection from the Daqing field and incremental oil recovery of up to 14 % has been reported (Chang et al. 2006, Yupu & He 2006). Although the general concept of polymer flooding is fairly straightforward, the detailed physics of the polymer-water-rock system is complex (Seright et al. 2010). A number of reservoir simulation programs have been developed able to calculate the flow behaviour of polymers (e.g. Zeito 1968; Slater & Ali 1970; Hirasaki & Pope 1974, Scott et al. 1987, Verma et al. 2009). The first approach using 2D streamlines to simulate the displacement of viscous oil by polymers was presented by Lake et al. (1981). Thiele et al. (2010) extended streamline-based polymer flooding to 3D within a more modern streamline simulation infrastructure. Al Sofi & Blunt (2009) used streamline simulation to investigate the effects of non-Newtonian polymer rheology. In this paper, we first describe the general aspects of streamline simulation for polymer flooding. Then, the field properties of a Romanian oil field are presented which we used to evaluate the benefits of streamline simulation to improve the management of a polymer flood by using new, per-pattern performance metrics. We conclude with a discussion of our methodology. Modern Streamline Simulation Modern streamline-based flow simulation differentiates itself from cell-based simulation techniques such as Finite Differences (FD) and Finite Elements (FE) in that components are transported along streamlines (SLs)

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rather than moved from cell-to-cell. This difference allows SLs to be computationally efficient in solving large, field-scale, and geologically complex models. For a recent overview on SL simulation see Thiele et al. (2010)

The basic element of SL simulation is its dual-grid approach. The traditional “static” (Eulerian) grid is used to specify petrophysical properties, well locations and rates, and initial conditions, and to solve for the spatial pressure distributions using an Implicit Pressure Explicit Saturation (IMPES) formulation. The dynamic (Lagrangian) grid represented by the SLs, on the other hand, is used to solve the hyperbolic equations that govern the spatial and temporal transport of chemical species.

The speed of SL simulation and novel solution data such as well allocation factors and well drainage/irrigation zones have made SLs an important, complementary approach to traditional simulation approaches in reservoir engineering workflows such as sensitivity runs, evaluating upscaling algorithms (Samier et al. 2002; Nair and Al-Maraghi 2006), flow-field visualization, full-field simulations, ranking and uncertainty quantification, surveillance (Grinestaff 1999; Batycky et al. 2008), flood management (Thiele and Batycky 2006; Ibrahim et al. 2007), and history matching (Milliken et al. 2001; Fenwick et al. 2005; Batycky et al. 2007), just to mention a few. The modelling of complex systems as might be the case in compositional simulation (Gerritsen et al. 2007) and fractured reservoirs (Di Donato et al. 2003), where the primary displacement mechanism is the subtle interaction of local displacement efficiency and interwell channelling, has also been extended to SL simulation. Most recently, SL simulation has been shown to have potential applications for thermal problems (Zhu et al. 2009) as well.

It is useful to frame SL simulation using the concept of the overall displacement efficiency E, which is generally written as a product of the local displacement efficiency, ED, and the volumetric displacement efficiency, EV

VD EEE ×= . ……………………………….. (1)

Modern, 3D, SL simulators have eliminated the need to separate the volumetric sweep efficiency into an areal and vertical component because today’s SLs are fully 3D and are able to capture the volumetric sweep directly. The periodic updating of SLs accounts for the nonlinear dependence between ED and EV. The power of Eq. 1 rests in the characteristic that the physics governing the displacement efficiency is embedded in the ED term, which implies solving the 1D transport equations along the SLs with the proper, local fractional-flow effects.

The Displacement Efficiency of Polymer Flooding

In polymer flooding, the key effect is to increase the injected-water viscosity by adding a polymer. This can lead to a dramatically reduced frontal mobility ratio between the displacing water and the resident oil and thereby improve the local sweep significantly. The design of a polymer flood generally takes into account the following five physical phenomena:

(1) Increasing water viscosity as a function of increasing polymer concentration.

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(2) Loss of some polymer due to adsorption of the polymer on the rock surface resulting in a smaller increase in water viscosity than in the non-adsorbing case. (3) The shear-rate dependence of polymer causing a decreasing effect of polymer concentration on water viscosity with increasing phase velocity. This is usually a near well-bore effect. (4) The inaccessibility of a fraction of the pore volume to the polymer because of the size of the polymer molecules [inaccessible pore volume (IPV)]. (5) The reduction of the water-phase permeability owing to adsorption. For field-scale modelling, the two most challenging aspects of designing a polymer flood are determining slug concentration and slug size to ensure that mixing with resident water and adsorption do not excessively reduce the in-situ polymer concentration to levels leading to smaller water-viscosity changes than desired and consequently poor sweep and reduced oil recovery. On the other hand, overdesigning a polymer flood can be expensive and result in a suboptimal project payout.

Combining SLs and Polymer-Flooding Physics The extension of SL simulation to account for polymer flooding has been previously described by Thiele et al. (2010). The difference to regular waterflooding is that now there is an additional mass conservation equation along each streamline accounting for the polymer dissolved in water and given by

( ) ( )j

rjj

ow

wwpwpw

kfCfCS

t μλ

λλλ

τ=

+==

∂∂

+∂∂ ;;0 , ………….. (2)

where the Cp is the concentration of the polymer p in the aqueous phase, Sw and fw are the water saturation and fractional flow, respectively; λj is the mobility of phase j; krj is the relative permeability of phase j; and μj is the viscosity of phase j. There is now an additional dependency through the water viscosity being a function of polymer concentration Cp. Typically, such dependency is measured in the laboratory and reported as a multiplier to the pure-water viscosity as a function of concentration, so that

( ) pure,wpw CA μμ ×= ………………………. (3)

Extensive testing of streamline-based polymer flood simulation and comparison with conventional FD simulation is described in Thiele et al. (2010). Field Properties of the Romanian Field The field is located in the Romanian part of the Moesian platform. The part of the field investigated here covers an area of about 13 km². The lithology of the reservoir is heterogeneous, and composed of sandstones, slightly consolidated or unconsolidated sands, and some limestone. A limited number of faults have also been

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interpreted. The structure was formed in an extensional tectonic regime. It is a (paleo) horst striking in an east-west direction. The reservoir is divided into two zones, representing two distinct progradational cycles. Table 1 gives average parameters for the two layers.

Tab. 1-Reservoir parameters of zone 1 and zone 2.

Zone Gross thickness in m

Net pay in m Avg Porosity Avg. Perm in mD

1 4.5 2.8 0.3 688 2 9.3 7.5 0.31 782

The reservoir dips with about 3 degrees. Top structure is in the south at a depth of 675 m subsea and the original oil/water contact at 720 m subsea. A plan view of the reservoir top and base is shown in Fig. 1.

Fig. 1 Reservoir top and base of the Romanian oil field.

The oil density is 947 kg/m³ and in-situ oil viscosity at current conditions is 100 cP. The water viscosity is 0.57 cP, water salinity 23,000 ppm, reservoir temperature 53 °C and the initial pressure 69 bar. The Oil Originally in Place (OOIP) of the compartment of the field investigated was 18.5 mn m³ (116 mn bbl).

Production History and History Match of the Romania Field Oil production from this section of the field started in 1961. Peak production of 600 m³/d (3774 Bbl/d) was reached in 1965 (Fig. 2), and water injection started in 1968. Waterflooding and increased drilling activity lead to a second peak in oil production around 1971, but afterwards oil production steadily declined. Current oil production is only 30 m³/d (190 Bbl/d). The cumulative oil recovery from the field is 3.03x106 m³ (19x106 Bbl), which translates into a recovery factor of approximately 16.5 %.

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Fig. 2-Historical oil and water production from and water injection into the Romanian field.

As can be seen in Fig. 2, water injection was stopped in 1997. However, the recovery factor is still relatively low. To improve oil production and ultimate recovery from this field, the water flood could be revitalised. Polymer flooding can further increase ultimate oil recovery and the focus of this paper is about how to design a polymer flood for this field. We first start with improving the injection and production rates for a strict waterflood, and then use a systematic workflow to find the best slug size/concentration values on a per-pattern basis using the waterflood rates as starting point. The next sections describe how to improve the design of the polymer flood using per-pattern information extracted from the streamlines. Designing an Optimal Polymer Flood In polymer flooding, capital expenditures are usually not the cost driver. The reason is that the polymer mixing facilities are only a fraction of the polymer costs. Rather, to optimise polymer flooding, the focus is on the amount of polymer required per incremental oil volume produced. This ratio is referred to as the utility factor (UF) and defined as (notice the mixed units):

[bbl] produced oil lincrementa cumulative[kg] injectedpolymer mass cumulative

=UF

The UF gives a good indication of the incremental operating expenditures for a polymer injection project. As in waterflooding projects, the goal is to use the injected volumes as efficiently as possible. In polymer floods,

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however, the optimisation is even more important due to the high cost of the injected polymer and the UF is a key economic metric used to assess the viability of a project. When using conventional FD simulation, the optimization of a polymer flood is necessarily done on a field basis as the UF is readily calculated for the entire field. Streamlines, on the other hand, allow to assess a UF on a per pattern (injector and associated producers) basis and therefore offer a much more granular approach to the design and management of a polymer flood. The following paragraph outlines the workflow, which we developed to design a polymer flood using information generated by the streamlines. In the subsequent sections of the paper, we describe the individual steps in more detail. Workflow for improving polymer injection using streamline simulation We followed five steps to improve the design of a polymer flood. These five steps are illustrated in Fig. 3 and summarised below. A more detailed description is given in the following sections.

1. The first step is to improve the currently ongoing waterflood, as polymer injection is best measured as the incremental oil compared to an optimised waterflood. Additionally, an optimal water allocation is necessarily a good starting point for a polymer flood, since the polymer resides completely in the injected water phase. In our work, we use the waterflood management approach described by Thiele and Batycky (2006), but any injection/production rate optimization procedure could be used.

2. The next step is to perform a forward polymer simulation based on the optimal injection/production rates used for the waterflood and using the same slug size and injection concentration for all patterns. Based on this single simulation, poor patterns are identified using the pattern performance plot. Patterns that fall below a certain threshold are disregarded and not considered for polymer injection.

3. Next, a sensitivity analysis of the remaining patterns is performed by changing operating parameters such as slug size and polymer concentration. Here the speed of the streamlines is key to be able to span a range of parameter combinations efficiently. We do not use a classical sensitivity analysis here. Instead, we rely on the ability of the streamlines to determine the ultimate utility factor on a per-pattern basis.

4. Using the sensitivity of the UF, the best parameter set (slug size and injection concentration) for each pattern is determined to achieve the desired utility factor and incremental oil recovery. This per-pattern analysis is the uniqueness offered by the streamlines.

5. Finally, the full-field is simulated using the desired slug size and concentration parameters of the individual patterns. To further improve the field recovery, the steps 1 through 5 should necessarily be repeated as the system is nonlinear. However, in this work we simply go through the workflow once.

Using the above workflow enabled us to improve the individual pattern efficiency over a generic field-wide approach. More details on the individual steps are given next.

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Improving waterflood

Improving initial polymer injection by disregarding poor patterns

Cumulative polymer injected - (kg)

Cum

ulat

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oil p

rodu

ced

- (rm

³)

12 years9 years6 years3 years

Different slug sizes at a fixed concentration

Cumulative polyme r injected - (kg)

Cum

ulat

ive

oil p

rod

uced

- (r

m³)

c. 500kg/m³ s. 6yc. 750kg/m³ s. 6yc. 1000kg/m³ s. 6yc. 1250kg/m³ s. 6yc. 1500kg/m³ s. 6y

Different concentrations at a fixed slug size

Sensitivity analysis for individual patterns

Incremental oil (bbl)

Util

ity F

acto

r (kg

/bbl

)


Util

ity F

acto

r (kg

/bbl

)

Optimising individual patter polymer injection based on utility factor and incremental oil production

Simulation of the full field using the optimised individual pattern strategies

Improving waterflood

Improving initial polymer injection by disregarding poor patterns


Cum

ulat

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oil p

rodu

ced

- (rm

³)



Cumulative polyme r injected - (kg)

Cum

ulat

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oil p

rod

uced

- (r

m³)



Sensitivity analysis for individual patterns


Util

ity F

acto

r (kg

/bbl

)


Util

ity F

acto

r (kg

/bbl

)

Optimising individual patter polymer injection based on utility factor and incremental oil production

Simulation of the full field using the optimised individual pattern strategies

Fig. 3-Workflow for improving polymer floods using streamline simulation.

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Improved Waterflooding for the Romanian Oil Field It is recognized that revitalizing the waterflood, which was halted in 1997, can potentially recover unswept oil. By adding 31 injection and 32 production wells and following the old line drive, can lead to substantial incremental oil production. Fig. 4 shows injector (blue) and producer (red) locations and the incremental oil production possible through a redevelopment plan of the current waterflood.

Fig. 4-(left) Production (red) and injection (blue) well locations for improved waterflood period. (right) Historical oil

production (red), do nothing forecast (orange) and optimal waterflood response (blue).

For the case in which the current production is maintained, only about 39000 m³ (246000 BBL) of additional oil are expected to be recovered. Improving the waterflood by favouring water injection in more efficient injector-producer pairs and demoting less efficient pairs (Thiele and Batycky, 2006) plus adding additional wells can lead to a cumulative production increase of approximately 560000 m³ (3.54 mn bbl) over 20 years, increasing the ultimate recovery factor from 16.5 % to 19.5 %. This optimal waterflood injection schedule, is used as the start of a polymer injection schedule. Evaluation of the Pattern Polymer Efficiency As in waterflooding, the great advantage of streamlines is the ability to identify injector-producers pairs and additionally to relate injected and produced volumes. By being able to relate injected polymer quantities to off-set oil produced, we are able to determine the efficiency of an individual injector-producer pair.

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Flow pattern and flux maps Flow pattern diagrams extracted from the streamlines can be plotted for polymer injection (Fig. 5) in exactly the same way as for waterflooding. The flow patterns are used to determine strong versus weak injector-producer pairs. The data behind each injector-producer pair shown in Fig. 5 are instantaneous injected (water rate and polymer mass) and produced (water, oil, and polymer) quantities.

Fig. 5-(left) Well locations and associated streamlines coloured by injector-producer bundles and (right) associated

flux pattern map.

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Focussing on one of the injector-producer bundles, Fig. 6 shows how the total injection rate flow from the injector is split among the individual producer. The plot shows that the best connection of the injector I-2166 is towards producer 616 (69 rm3) and the poorest towards producer 3071 (6.5 rm3). We also know how much oil, water, and polymer is produced by each connection since the streamline simulator calculates these quantities along each streamline, and the connection is simply the sum of all the streamlines between an injector-producer pair. We use this information to construct a pattern performance plot as described next.

Fig. 6-Pattern connections between injector I-2166 and off-set producers. The labels show total reservoir fluid rate of

each connection-

Pattern performance plot We define a pattern as being an injector and its off-set producers (Thiele and Batycky 2006). Note that a pattern can change in time, since the streamlines and thus the connections are a function of well rates. Given that we know the cumulative volume of water and polymer injected and we can calculate the off-set volume of oil produced, we are able to plot the cumulative oil produced as a function of cumulative polymer injected for each injector (pattern). We refer to this graph as the pattern performance plot shown in Fig. 7. Note that the slope of the line defines the instantaneous utility factor, which is given by the ratio of cumulative polymer injected to cumulative off-set oil produced. The plot has five key periods:

(1) Early time recovery: although polymer injection has started, the oil bank has not yet broken through. (2) Mid time recovery: the oil bank formed by the injected polymer is breaking through

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(3) Late time recovery: this period is characterised by decreasing incremental oil with additional polymer injection.

(4) Chase water recovery: polymer injection has stopped resulting in a vertical line; however oil production continues due to remaining polymer in the reservoir.

(5) Ultimate pattern recovery from polymer and chase water injection. Dividing the ultimate injected amount of polymer by the cumulative oil production from the pattern gives the cumulative efficiency factor for the pattern.

Fig. 7-The pattern performance plot with five periods: (1) early time recovery before oil-bank breakthrough (2)

mid-time recovery characterized by breakthrough of the oil bank (3) late time recovery where little additional oil is

recovered by polymer injection, (4) chase water recovery and (5) ultimate recovery of oil and ultimate injection of

polymer (efficiency factor).

The further the ultimate injection of polymer and offset ultimate oil recovery (point 5 in Fig. 7) are shifted to the top left corner, the more efficient the polymer injection process. Poor patterns require a large amount of polymer for a small amount of cumulative oil production from the pattern. Such patterns are characterized by being close to the x-axis. Plotting all the pattern performances for the same polymer injection concentration (here 1000 ppm) and polymer injection period (here 6 years) of the field in one plot (Fig. 8) represent a first screening of the polymer injection. Patterns such as the I-2166 are eliminated upfront as candidates for polymer injection as these patterns have minimal contribution to the oil production but use up a significant amount of polymer. On the other hand, I-300 is clearly a good pattern. Notice that different amounts of polymer injected per pattern results from the different injection rates for each pattern at equal concentration.

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We also plot the cumulative efficiency factor with time (Fig. 9) to screen patterns for further improvement. Note that the efficiency factor is similar to the utility factor. The only distinction between these two factors is that for the utility factor, the oil production of the base case (improved waterflood) is subtracted from the oil production of the polymer flood, hence, it is an efficiency factor based on the incremental oil recovery. For the reservoir used here, patterns with efficiency factors less than 20 kg polymer injected/m³ (3.18 kg/bbl) off-set oil produced were selected.

Fig. 8-Comparison of pattern performance plots for the Romanian field. Efficient patterns result in a large

incremental oil production using a small amount of polymers.

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Fig. 9-Cumulative pattern efficiency factors. In the first period, the oil bank did not reach the producers, hence the

efficiency is decreasing. After the oil bank reached the producers and injection of chase water, the efficiency is

improving.

Sensitivity Analysis of the Individual Patterns Once inefficient patterns are discarded and promising patterns (injectors) retained, the individual patterns are further investigated in order to individually find the best polymer concentration and slug size. Fig. 10 shows the sensitivity of the variation of the length of the injection period and the polymer concentration for one pattern.

0

50000

100000

150000

200000

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300000

350000

0 500000 1000000 1500000 2000000


Cum

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³)



0

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100000

150000

200000

250000

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0 200000 400000 600000 800000 1000000


Cum

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- (rm

³)



Fig. 10- Sensitivity analysis for one pattern, period of polymer injection (left) and polymer concentration (right).

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The endpoints of each line (point 5 in Fig. 7) give the ultimate efficiency factor for the pattern (4 slug sizes times 5 concentrations=20 ultimate efficiencies). Furthermore, the cumulative oil production by water injection (polymer concentration zero) can be determined and used to calculate the incremental oil recovery for each pattern. In addition to the sensitivity analysis of Fig. 10, the diagram showing the cumulative efficiency factor with time can be utilised to analyze the individual pattern efficiency. Fig. 11 shows a case where this factor first decreases, indicating that more and more offset oil is produced with time until it decreases again (at about 20000 days in Fig. 11). This is a sign of injecting polymers for too long a period of time.

0

4

8

12

16

20

0 5000 10000 15000 20000 25000 30000

Time (days)

ratio

pol

y/of

fset

oil

(kg/

m³)

Fig. 11-Pattern efficiency factor with time. In this pattern, polymer injection results in more and more oil produced

from off-set oil producers. However, at a certain time (here about 20000 days), less oil is produced. This indicates

that the chosen slug size is too long.

Subtracting the cumulative oil production by pure water flooding from the ultimate cumulative oil production of a pattern gives the incremental oil production for that pattern. Dividing the polymer injection by the incremental cumulative oil production results in the pattern utility factor. We use this factor for the individual patterns to estimate the best slug size and concentration to use for each pattern. Pattern utility factor plot Plotting the pattern utility factor (end point of cumulative injected polymers divided by cumulative incremental oil production) for the various slug sizes (Fig. 10-left) and polymer concentrations (Fig. 10-right) gives the pattern utility factor versus cumulative oil production (Fig. 12). Each of the points on this plot represents the pattern utility factor for a specific polymer concentration and slug size. The higher the utility factor, the higher the incremental operating expenditures for polymer flooding. Choosing a certain utility factor, the optimum incremental oil production for the respective pattern can be

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estimated. In the example of Fig. 12, a pattern utility factor of approximately 1.5 kg/incremental bbl is desired. For this utility factor, the red point in Fig. 12 gives the highest incremental oil recovery and is sufficiently close to 1.5 to merit consideration. The red point corresponds to a polymer slug size of 9 years and 1750 ppm. In other words, if an approximate utility factor of 1.5 kg/bbl is desired, then for this particular pattern, the slug size and concentration should be picked as being 9 years and 1750 ppm respectively. For other patterns, the best injection strategy to maintain a utility factor around 1.5 kg/bbl will have different values for slug size and polymer concentration. If there is no slug size/concentration yielding a utility factor at or around the desired threshold, the pattern is disregarded and simply kept on water injection.

0

0.5

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1.5

2

2.5

3

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0 100000 200000 300000 400000 500000 600000 700000 800000


Util

ity F

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/bbl

)

Fig. 12- Pattern utility factor. On this plot, the pattern utility factor (ultimate cumulative polymer injected / ultimate

incremental oil produced) are shown for different injection strategies. Each point represents the pattern utility factor

for a different strategy. Choosing a certain utility factor threshold, the optimum injection strategy can be determined.

In this case the red point corresponding to injection of 1750 ppm polymers for 9 years.

After the best injection strategy for each pattern is selected, a new simulation is generated. Since the model is nonlinear, one should check how the patterns perform under a new per-pattern selection of slug sizes and concentration. However, for this study we simply accept this final simulation as a good future performance indicator.

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Full Field Results of Pattern Optimisation Fig. 13-left shows how the cumulative polymer injection amount was reduced and Fig. 13-right how the oil production was improved by using the methodology described above. Compared to the pure water injection, polymer injection leads to higher oil recovery as one might expect. But by additionally improving the polymer flood on a pattern basis, the polymer flood could be improved further by using less polymers than the base case while increasing ultimate oil recovery. The lowering of the amount of polymers injected is a result of being able to selectively discard patterns that underperform (high utility factors), while the increased oil production is achieved by ensuring a proper slug size/concentration for good patterns resulting in maximum sweep. For the Romanian reservoir we used in our study, the expected utility factor has been decreased from 3.01 kg/bbl to 2.01 kg/bbl and the incremental oil recovery over the waterflood increased from 5.01 mn bbl to 6.1 mn bbl. This clearly has significant implications for the economic viability of the project.

Fig. 13-left: Cumulative polymer injection with time for base case water (yellow), base case polymer without

improvement of the individual patterns (dark blue) and improvement of patterns for polymer injection (red). Right:

Cumulative oil production with time for base case water (yellow), base case polymer without improvement of the

individual patterns (dark blue) and improvement of patterns for polymer injection (red).

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Summary and Conclusions Modern streamline simulation has been extended to include polymer flooding calculation. Frequently, polymer flooding projects are performed for reservoirs containing oil with somewhat higher in-situ viscosities (6-150 cP) and low gas/oil ratios. Such fields are effectively simulated using streamline simulations due to the limited compressibility effects. Streamline simulation is not only more computationally efficient compared to a finite difference models allowing faster turn-around times, but more importantly offers a number of powerful diagnostic plots to help manage and improve an ongoing polymer injection project. Typically, polymer injection projects are operating costs driven, with the main cost being the polymer itself. Streamline simulation offers a unique possibility to efficiently minimise these costs on a per-pattern basis. A methodology for improved reservoir management of a polymer flood of a medium viscous Romanian oil field was discussed. We used the following workflow to design and improve the polymer flood.

1. We first improved the base waterflood by changing the injection/production rates according to the methodology proposed by Thiele & Batycky (2006), although any waterflood optimisation procedure can be substituted here. The main reason for this step is to measure the incremental oil recovery from the polymer flood against an optimal waterflood base case.

2. We then simulate a base polymer flood using the same slug size and injection concentration for all patterns (injectors) and create pattern efficiency plots (cumulative polymer injected versus cumulative oil production from connected producers). We use these plots to identify and screen for poorly responding patterns. Generating these plots is unique to streamline simulation and is not available for finite difference simulations. Poorly performing patterns are disregarded in this step.

3. We then perform a sensitivity analysis on the remaining patterns by modifying parameters such as polymer concentration and slug size length. Here we take advantage of the computational efficiency of streamline simulation to be able to simulate a number of cases to assess the production response.

4. We now are able to generate a utility factor on a per-pattern basis for each sensitivity run (20 simulations). By plotting the utility factor versus cumulative oil production for each individual pattern we are able to additionally screen each pattern so as to ensure that it remains within a desired utility factor range and select that particular combination of slug size and concentration. At this stage we are also able to additionally eliminate some patterns because the desired utility factor can not be achieved for any of the combinations of slug size and concentration.

5. We now perform our final polymer simulation using the best slug size/concentration for each pattern. Patterns identified as being underperforming for the polymer flood are simply kept on waterflooding.

The workflow described above was used to design a polymer flood for a Romanian reservoir. Due to the fast runtimes of streamline simulation and the unique capabilities of connecting injectors and producers and the development of diagnostic plots, the workflow was successfully applied. We additionally intend to use streamline simulation for monitoring the flood on a per-pattern basis as the flood is being implemented.

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For the example reservoir, we project an increase in the recovery factor by 5.3 % of the oil originally in place compared to the base waterflood scenario while operating at a field-wide utility factor of 2.01 kg/incremental bbl. Acknowledgements Thanks to Azat Shaymardanov and Markus Buchgraber for the discussions and to OMV E&P for the permission to publish this paper. References Al Sofi, A.M., Blunt, M.J. (2009) Streamline Based Simulation of Non-Newtonian Polymer Flooding, SPE 123971

presented at the Annual Technical Conference and Exhibition, New Orleans, USA.

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Batycky, R.P, Seto, A.C., and Fenwick, D.H. (2007) Assisted History Matching of a 1.4-Million-Cell Simulation Model for

Judy Creek ‘A’ Pool Waterflood/HCMF Using a Streamline-Based Workflow. Paper SPE 108701 presented at the

SPE Annual Technical Conference and Exhibition, Anaheim, California, USA, 11–14 November. doi:

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Batycky, R.P., Thiele, M.R., Baker, R.O., and Chugh, S.H. (2008) Revisiting Reservoir Flood-Surveillance Methods Using

Streamlines. SPE Res Eval & Eng 11 (2): 387–394. SPE-95402-PA. doi: 10.2118/95402-PA.

Beliveau, D (2009) Waterflooding Viscous Oil Reservoirs. SPE Reservoir Evaluation and Engineering Journal, 12 (5):

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