SPE-172669-MS

Comprehensive Micromodel Study to Evaluate Polymer EOR in Unconsolidated Sand Reservoirs J.G. Herbas, SPE.; J. Wegner, R.E. Hincapie, H. Födisch, L. Ganzer, Technology University Clausthal

Copyright 2015, Society of Petroleum Engineers This paper was prepared for presentation at the SPE Middle East Oil & Gas Show and Conference held in Manama, Bahrain, 8–11 March 2015. 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 In this work micromodels are generated from high resolution images of unconsolidated sands and subsequently used to study polymer EOR processes. Using this approach an almost unlimited number of equal copies can be produced. Moreover, compared to cores, the micromodels enable visual access to the flooding process, facilitating a more detailed process description. Understanding the mechanisms and effects of polymer flooding is essential to avoid failures in field applications. Core studies conducted by commercial laboratories indicated very optimistic oil recovery from polymer injection; therefore, a micromodel study was proposed to investigate the recovery process in detail. The experiments were conducted at reservoir temperature. This study aims to support the design and optimization of polymer EOR projects by experimentally determining polymer performance in terms of production acceleration, incremental production and reduction of the residual oil saturation. Performance is evaluated for a variety of critical input parameters such as polymer rheology, product, and concentration for different average rock properties. The study includes complete rheological characterization of three polymers in terms of flow curves and, in specific cases, viscoelastic effects. The characterization enables the determination of suitable polymer concentrations based on three predetermined viscosity ratios. During the flooding experiment, the oil saturation changes are continuously monitored through image analysis based on which the recovery factor can be calculated. Experimental results obtained from corefloods are compared to those obtained in micromodels, focusing on the following key parameters: (1) Recovery factor, (2) injected pore volume at breakthrough time, (3) residual oil saturation. The paper illustrates how the micromodel experiments help to improve the understanding of oil recovery processes during polymer EOR. Introduction Adding polymers to the injection water leads to an increase in aqueous phase viscosity together with a reduction in aqueous phase permeability. As a result the mobility ratio improves, leading in more efficient displacement process, higher oil recovery factor and production acceleration over water flooding. However, several physical and chemical processes accompany the flow of aqueous polymer solutions in porous media resulting in loss of polymer solution viscosity, hence, process efficiency. Therefore, a wide range of flooding experiments is required to optimize polymer flood performance in unconsolidated sand reservoirs in terms of production acceleration, incremental production and residual oil saturation. The displacement mechanisms during polymer flooding are the improved mobility ratio, capillary number and polymer solution elasticity. These mechanisms depend on several critical parameters, including but not limited to polymer type, polymer concentration, salinity of make-up water and reservoir brine as well as temperature, just to name a few. The industry standards to assess polymer flood performance are flooding experiments with cores or plugs. However, often only a limited amount of reservoir rock material is available to run enough experiments to test all important variables listed above. In addition, during flooding experiments plugs or cores remain a “Black Box“ to some extent restricting detailed scientific description of local distribution of static and dynamic flow properties. A complement to flooding experiments with cores can be micromodels such as 2D silicon edged pore-networks which resemble porous media. Compared to cores an almost unlimited amount of micromodels with the same pore-network can be

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produced. Moreover, compared to cores, micromodels enable visual access to the flooding process enabling a more detailed process description at the pore-scale. In this paper we present flooding experiments in micromodels that resemble thin section photographs of unconsolidated sands. Further, a detailed rheological characterization of polymer flood media is presented. Besides standard flow curves (viscosity versus shear rate) first normal stress difference, characteristic relaxation time and elongational viscosity are measured in rotational and elongational viscometry. Micromodels and Microfluidics In this work micromodels are generated from high resolution images of unconsolidated sands and are subsequently used to study polymer EOR processes. The design and construction of the micromodels is subsequently described. Further, the design and construction of the experimental setup used to perform the micromodel flooding experiments is presented. Micromodel Design The micromodel is a sandwich of different materials; glass-silicon-glass (GSG): The porous structure is completely dry etched through the silicon, which results in a transparent micromodel. The GSG-micromodels presented in this study show several advantages over conventional micromodels made of silicon, PDMS or glass: (1) They are transparent, hence, enable more flexible image gathering, (2) permit the construction of small pore-throats and complex flow geometries and (3) facilitate the investigation of transport processes at high p, T. Micromodel Porous Structure Porous structures used for micromodel construction attempts to resemble a thin-section photograph of a real unconsolidated sandstone. The typical size of thin-section photograph is in the range of millimeters. Microfluidic experiments have to take place on a larger area to be suitable for making visual investigations of displacement processes. Therefore, the small area of the thin-section has to be transformed to a bigger area that will be used to reproduce a similar structure on the chip (Ganzer et al., 2014). It is not possible to copy and paste the original structure multiple times, because artifacts will form at the borders (Fig. 1, middle). Fig. 1 shows a binary image of an exemplary thin section (right). The picture in the middle depicts a simple repetition of the sample image, which is not acceptable, as artifacts will dominate fluid flow. The picture on the right shows an artificial pattern generated based on the input sample with the texture synthesis algorithm by Efros and Leung (1999). Images presented here are not considered in this work and are used to present the method of approach, only.

Fig. 1: Binary images of exemplary thin sections

Therefore, algorithms have to be used to generate a larger image from a small sample (Fig. 2, left) while preserving properties such as porosity and permeability (Ganzer et al., 2014). Input for the algorithm is a binary image, where one color (e.g. black) represents the grain and another color (e.g. white) represents the pore space. This binary image has to be generated from the thin section image with a focus on matching the reported porosity. The algorithm then takes the input image and generates a non-repeating pattern on a larger area that visually looks the same. At the same time image analysis algorithms have been developed (using a Matlab® code) to calculate morphological properties of the micromodels (refer to Table 1).

Fig. 2: Binary images of lithographic masks used for micromodel flooding experiments.

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Table 1. Properties of lithographic masks calculated based on Matlab® code.

Experimental Setup A schematic diagram of the experimental setup is presented in Fig. 3. The GSG micromodel is placed with its holder inside the cabinet dryer. PTFE and stainless steel tubings are used as connections. The pressure differential over the model is recorded, as well as the applied backpressure which is set slightly above atmospheric pressure (150 mbar). It is controlled to an accuracy of 0.1 mbar. The different fluids are injected via a syringe pump outside the cabinet dryer and collected in a closed effluent bottle. A DSLR camera is used to cover the flooding process continuously (each 30 seconds), which is visible due to a UV-active dye attached to the oleic phase and UV emitting diodes.

Fig. 3: Schematic diagram of the experimental setup used to perform micromodel flooding experiments.

Preparation and Rheological Characterization of Flo od Media The objective of this stage was to select the optimum polymer product and concentrations in order to define the rheological behaviour of polymer solutions in presence of different reservoir brines and injection water, to further investigate polymer flooding. All rheological evaluations were measured under the focus of the project targets like: temperature, salinity and ph-value. An extensive rheological characterization was performed in terms of steady shear viscosity and Normal Stress difference (Rotational rheology), small amplitude oscillatory shear (oscillatory rheology) as well as extensional thickening (extensional rheology). Polymers and Brines A set of three different commercial hydrolyzed polyacrylamides (HPAM) polymer products was used. Basic properties of the polymers are presented in Table 2Table 2. Basic properties of polymers used during investigation. To note that names of polymer products were changed due to confidentiality reasons. The polymer solutions use different brines as make-up water. Brine 1 with 0,3 g/l TDS and Brine 2 with 4 g/l TDS for the make-up water. The salinity range for reservoir water was between 4 - 8 g/l TDS

Parameter Model 1 Model 2 Model 3

Porosity, % 33.3 41.9 33.3

Bulk / Pore Volume, mm³ 0.08/0.027 0.08/0.034 0.08/0.027

Min/Avg/Max pore diameter, [mm] 0.03/0.13/1.11 0.03/0.14/0.97 0.03/0.08/0.82

Min/Avg/Max grain size, [mm²] 0.03/0.13/1.11 0.03/0.14/0.97 0.03/0.08/0.82

Grain Count [-] 4051 1150 4022

Tortuosity - X/Y 1.21/1.62 1.13/1.22 1.14/1.23

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Table 2. Basic properties of polymers used during investigation

Polymer Preparation Approaches First, behavior of mother (10000 ppm) and stock (5000 ppm) solutions were defined for each polymer comparing the performance by using a shearing device, which is meant to shear the polymer in order to degrade mechanically the longest macromolecules without significantly reducing the mean value of the molecular weight. Second, different diluted polymer concentrations in most of the cases between 600 ppm – 2000 ppm were prepared using a quiasi-standardized procedure and rheologically characterized in favor of defining the required concentration of polymer in solution for a viscosity at 10 s-1 (shear rate) and reservoir temperature, like the measured live oil viscosity from PVT analyzes (refer to Table 3 where a relation of 1, 3.5 and 7 times the oil viscosity was used for experimental purposes). Third, the defined concentrations were characterized as a function of temperature with the purpose of defining the power law model parameters, power law constant (H) and power law exponent (n) which will support the injection pressure calculations.

Table 3 Required concentrations of polymer for experiments. Defined after rheological characterization to match the oil live viscosity

It is well known that technical acrylamide/acrylate copolymers have a wide molecular weight distribution. The components with an extremely high molecular weight can cause plugging, at least in zones of lower permeability, and impair the injection of the solution (Littmann, W 1988, Karau, D. et al., 1988). As a consequence, the aim of all shearing applications to the polymer solutions is to degrade mechanically the longest macromolecules without significantly reducing the mean value of the molecular weight. This mean value governs the viscosity at the rates of shear encountered under reservoir conditions and should therefore be as high as possible. Moreover for a proper polymer solution injection the Newtonian fluid behaviour must be guarantee at a low shear rate (<1s-1), where viscosity is independent of shear rate in the porous media. This phenomenon is one of the reasons why so many polymer projects failed in praxis. Similar approaches inducing degradation was study by Dupas et al., (2013) with remaining questions regarding the impact of mechanical degradation in both shear and extensional viscosities. Due to the shearing process the viscosity yield of polymer solutions is lower. In other words, for the Newtonian fluid behaviour at low reservoir shear rates a higher product concentration is needed. Only by lab tests the optimal concentration of polymer solution can be obtained. An additional support to this practice is the work performed in the Bockstedt field (Germany), where it was found that sheared solutions provided a constant RF-value (Karau, D. et al., 1988). A configuration of the shearing device is presented in Fig. 4. A stainless steel container of about 1000 ml is connected to a compressed N2 line supplier. The container includes a Teflon piston to provide a uniform displacement of the polymer through the cylinder as well as constant pressure differential. The bottom part contains the holder tube where the shear plates are located. Every shear plate (with diameter of: e.g. 0.5mm, 1.0mm or 3 mm) having a distance of 10mm separation. In total six (6) plates can be placed. The amount of plates was calculated considering Bernoulli equation, flow rate and pressure drop across the plates.

Injected Fluid Expected Viscosity [mPas]* Measured Viscosity [mPas] ** Concentration (ppm) Expected Viscosity [mPas]* Meas ured Viscosity [mPas]** Concentration (ppm)

Polymer A 3.3 3.5 300 6.6 7.0 600

Polymer A 11.4 12.0 500 23.1 25.0 800

Polymer A 22.9 22.0 700 46.2 48.0 1000

Polymer B 3.3 3.8 450 6.6 7.0 1200

Polymer B 11.4 12.5 650 23.1 24.5 1400

Polymer B 22.9 23.0 850 46.2 47.0 1600

Polymer C 3.3 3.0 400 6.6 7.5 960

Polymer C 11.4 12.0 600 23.1 24.0 1200

Polymer C 22.9 24.0 800 46.2 47.0 1400

* Estimated based in viscosity ratios (1, 3.5, 7)

* Rheology Measured at shear rate: 10 s-1 and 73 °C

Brine 1, 73°C Brine 2, 62°C

Polymer Type M.W (Dalton) D.H

Polymer A HPAM 26 x 106 28-30%

Polymer B HPAM Unknown Unknown

Polymer C HPAM 20 x 106 30%

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Fig. 4 Configuration of the stainless steel shearing device. Modification from Karau, D. et al., 1988

Fig. 5 shows a comparison of the viscosity behaviour for the sheared and Non-sheared solutions using the device built for this investigation at a shear range between 1 – 10 s-1 the viscosity loss by impact due to the shearing was only around 15%.

Fig. 5 Viscosity behavior for polymer stock solutions 5000 ppm at 62°C

Rheological Characterization We consider of high importance a comprehensive characterization of polymer to be use for EOR applications, in that order is necessary to look at all the different approaches when referring to either shear thinning or shear thickening behavior. A complete rheological characterization was performed for all solutions in terms of steady shear viscosity and Normal Stress difference (Rotational rheology), small amplitude oscillatory shear (oscillatory rheology) as well as extensional thickening (extensional rheology). Rotational Rheology Polymer solution viscosities as well as Normal stress difference (N1) measurements were performed using a Kinexus Pro Malvern rheometer at reservoir temperature conditions (62°C and 73°C), which was controlled with an Active Hood Peltier Plate Cartridge (Environmental controller with minimized thermal gradients for plate measuring systems). The shear rate interval used was of 0.01 s-1 to 1000 s-1 for all rotational measurements. In this paper most of the viscosities as well as N1 values are cited at a fixed shear rate of 10 s-1.

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The viscosity behaviour of polymer A at 2000 ppm concentration (Fig. 6) was used to compare the sheared and non – sheared solutions rheology. Both solutions exhibit a pseudoplastic behaviour as well as the selected solutions for experimental purposes, over the range of shear rate measured, hence, power law equation was used to fit the relationship between shear stress (σ) and shear rate (ϒ)´. The polymer solutions have similar concentration, however as expected the non-sheared solutions exhibit higher viscosity than the sheared on despite of the N1 which presented similar values. Shear stress behaviour was drastically changed by the shearing process with changes in around 4 Pa. For the concentrations defined in Table 3 N1 measurements were affected by temperature and salinity. Most of the cases the weak response in normal stress difference was unclear due to the mechanical artefacts during the measuring time as well as possible drifting in the solution, hence the higher the temperature the most dissipated was the N1 response. According Grigorescu et al., (2000), the precision of the measured N1 values can be influenced by inertia and other external variables. Inertia reduces the measured normal forces and it only depends on the diameter of the system and the rotational speed for a given solution. In the case of a polymer solution with real but small normal forces (diluted or lower masses), even negative N1—values are simulated by this effect.

Fig. 6 Viscosity curves for Polymer A, comparing sheared and non-sheared solutions. Rotational rheology comparison (Viscosity,

shear Stress, N1)

Oscillatory Rheology Small amplitude oscillatory shear measurements were performed using the same rheometer. Oscillatory properties give the response of polymer molecules to oscillatory perturbation. Frequency is the independent variable in these measurements and allows as well estimating various elastic modulus (G’), such as storage modulus or viscous modulus (G’’), hence relaxation times (Macosko, W. 1994). The relaxation time is defined as the required time for a property having been displaced from equilibrium to decay back to its original value. In other words the property of polymers to recover its original viscosity value after has been under deformation. The interest of the relaxation time falls into the need to compute the Deborah number, which is used to quantify viscoelasticity. Measurements are divided into two parts: Amplitude sweep and frequency sweep. In the amplitude sweep measurements are performed at constant frequency and temperature with variation either in shear stress or shear strain, with the purpose to define the linear viscoelastic region (LVER) and torque levels. The amplitude sweep will provide if a sample is elastic or viscous dominated. The data obtained in the amplitude sweep measurement will then be used to run the frequency measurement which allows to determine the cross over frequency at which G’ = G’’. This is a measure of the longest relaxation time, and is given by the inverse of the numerical value in radians per second. The impact of mechanical degradation on polydisperse high molecular weight HPAM polymer solutions required further studies (Dupas et al., (2013) as well as the consideration of designing to obtain good injectivity or the remaining technical challenges whether or not the existence of viscoelasticity provide by the polymer solutions. This paper was focused in to design based on injectivity purposes taking into account the possible impact of viscoelastic effects. We found in most of the cases the required concentrations to obtain viscoelastic behaviour must be high, which may lead into injectivity problems once again.

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N1 Polymer A 2000 ppm, Sheared N1 Polymer A 2000 ppm, Non - Sheared σ (Pa) Polymer A 2000 ppm, Sheared

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For the concentrations defined in Table 3 most of the amplitude sweep measurements depicted weak elastic behaviour at room temperature and at reservoir conditions a strongly viscous dominated behaviour, with prevalence of G’’ against G’. Thus cross over points were not found in frequency measurements, hence relaxation times could not be estimated. Fig. 7 clearly depicted the dependence in temperature and most likely the effect of shearing for the elastic properties estimation. Note that Polymer A which posses the highest molecular weight still showed an elastic dominated behaviour at room and reservoir temperature, despite of the slightly low concentration. On the other hand Polymer B just in case of room temperature showed elastic properties with prevalence of elastic modulus. Inverse of the frequency where G’ and G’’ encounter or cross over is generally accepted as the fastest method for relaxation estimation and is well referred by different authors (Kim et al., 2010; Delshad et al., 2008; Castelleto et al., 2004). As afore mentioned just few cases during the course of this investigation showed cross over points at reservoir temperature conditions. An important finding is that none of the solutions prepared in brine 2 showed elastic responses of the concentrations defined in Table 3.

Fig. 7 Amplitude Sweep measurement comparison for two different defined concentrations with a temperature dependence.

Table 4 describes the elastic rheological parameters defined for the analysed solutions. To note that only polymer A showed viscoelastic response at frequency sweep measurement, hence relaxation time was defined. Also when comparing the relaxations times for sheared and non-sheared solution at reservoir conditions relaxation times are about three times different. Which definitely suggest that mechanical degradation have a strong impact in the possible provided viscoelastic polymer effect. As well as suggested that the amount of polymer product required when designing looking into elastic response need to be higher, otherwise the expectations are quite low.

Table 4 Rheological Oscillatory Parameters defined for the solutions showing elastic response.

Extensional Rheology Extensional thickening or known as strain hardening properties, were determined using a microfluidics device or extensional viscosimeter (Pipe et al., 2007). Despite it was not the focus in this investigation, was important to have an insight regarding possible effects related to elongational behavior. Wu, S. (2013) found a direct relationship between first normal stress

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G' Polymer A 700 ppm 22°C G'' Polymer A 700 ppm 22°C G' Polymer A 700 ppm 73°C G'' Polymer A 700 ppm 73°C

G' Polymer B 850 ppm 22°C G''Polymer B 850 ppm 22°C G' Polymer B 850 ppm 73°C G'' Polymer B 850 ppm 73°C

Solution Cp [ppm] Temp [°C] G'/G'' [Pa] f , Frequency [Hz] ω [rad/s] λ [s]

Polymer A, Brine 2 (Sheared) 2000 22 0.4714 0.2141 1.3450 4.6707

Polymer A, Brine 2 (Sheared) 2000 62 0.5730 0.9120 5.7300 1.0965

Polymer A, Brine 2 (Non-Sheared) 2000 22 0.3972 0.0650 0.4082 15.3917

Polymer A, Brine 2 (Non-Sheared) 2000 62 0.4198 0.2096 1.3170 4.7710

Polymer A, Brine 1 700 22 0.2357 0.1952 0.9124 5.1230

Polymer A, Brine 1 700 73 0.2739 0.5983 3.7600 1.6714

Polymer B, Brine 1 850 22 0.2967 0.3134 1.9690 3.1908

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differences and elongational viscosity. In this matter we provide just an example of the properties showed by the elongational measurements. This is still a topic part of our current research. Fig. 8 depicted the changes in apparent extensional viscosity for a solution submitted to mechanical degradation. To note that apparent extensional rates were converted to shear rates using Mocosko, C. (1994) approach, relating the apparent viscosity to the shear rate as a shear ���� = √� ∗ έ Eq. 1

Fig. 8 Apparent Extensional Viscosity measurement using microfluid rheometer Polymer A, 2000 ppm, Brine 2, 22°C.

Extended Cox-Merz Plot (relation between Rheological parameters) In rheological applications, the Cox–Merz rule or Laun's rule has frequently been employed for transforming rheological data or for checking consistency of the data collected in different experiments (Wen et al., 2004). For instance, because the linear viscoelastic data can, in general, be obtained more easily, these empirical rules can thus be utilized to predict corresponding viscometric properties in steady shear flows. The Cox-Merz rule and Laun’s rule are two empirical relations that allow the estimation of steady shear viscosity and first normal stress difference respectively using small amplitude shear measurements (Sharma, V, and McKinley, G. 2012) The Cox-Merz rule is very useful when viscosity data is needed and only linear viscoelastic data is available. The Cox–Merz rule states that the shear-rate dependence of the steady-state viscosity, η(γ), is equivalent to the frequency dependence of the complex viscosity, η*(ω):

�� � = |∗���|��� = ����� �� �� + ���� �� ������ Eq. 2

The rule is very useful when viscosity data is needed and only linear viscoelastic data is available. Laun's rule proposed a useful empiricism analogue to the Cox-Merz rule where he relates the first normal stress difference measured in steady shear experiments to the dynamic moduli as:

���� � = ��′��� = �� + �����������!� + �′′ �� !�"#.%��� Eq. 3

where 0.7 represent the power-law index (originally given in a range of 0.5 to 0.7). In contrast with the case of the Cox–Merz rule, the validity of Laun's rule has not been so extensively tested against experimental data on various polymer liquids. Grigorescu et al., (2000) shows that polymer develops not only shear stresses but also normal stresses perpendicular to the flow directions. Normal stresses are primarily a manifestation of the elasticity of polymeric liquids and they are not found for Newtonian liquids. At high frequencies, the solutions show an increasing elastic portion, for instance only a small part of the energy is transformed into viscous flow. A complete rheological characterization of polymer solutions in steady shear flow is only given if the stress functions are determined (shear stress, τ12, first and second normal stress differences, N1 and N2. Based on that idea the main efforts have been focus into compare behavior through the use of extend Cox-Merz plots with the produced data. And in that way have a fully characterized material. The extended Cox-Merz rule equates the steady and dynamic viscosities at equivalent shear rate and frequency, Fig. 9 depicts the whole rheological characterization for Polymer A at 2000 ppm. The upper part of the chart present the Cox-Merz rule

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demonstration, additionally N1 measured (still with mechanical issues) is compared with one defined through the Laun’s Rule, to note that at a shear rate of 100 s-1 both curves take similar tendency. Two different oscillatory measurements (G’ and G’’) were plotted in order to demonstrate the data reproducibility by using different samples. Shear stress versus shear rate is plotted as well to control the quality of the data. These results allow predicting polymer behavior by using a data combination and might lead in the generation of different correlation at specific conditions for simulation purposes.

Fig. 9 The extended Cox-Merz plot for Polymer A, 2000 ppm

Comparison of Corefloods and Micromodel Flooding Ex periments In general three different effects have been analyzed using micromodel flooding experiments. First of all the controversially discussed influence of viscoelasticity on the residual oil saturation (Sor), secondly the variation of the viscosity ratio and the effect of the pressure difference (dP) over the model. In the following these results are presented and two core flooding experiments are shown. Table 5 describes all the variables analyzed during the flooding experiments and the data generated for every rheologically characterized fluid in model type 1 and 2.

Table 5 Experimental Review Results for Model 1

The flooding experiments were all performed at the same temperature of 73°C, with an oil viscosity of 3.27 mPas and an injection rate of 0.44 µl/min. The investigation in model 1 focused on the effect of viscoelasticity. In

Fig. 10 two typical pictures from a flooding experiment can be seen. The models are operated in a quarter of a five-spot pattern, which is indicated by the green and the red arrow, representing the inlet, which is always in the top right corner and the outlet, which is always in the bottom left corner. The left picture shows the initial saturation of the model, the right one the

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Parameter E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 E11 E12 E13

Polymerflooding

Micromodel Type 1 1 1 2 2 2 2 2 2 2 2 2 2

Polymer Product A A - A A A B B B C C C -

Concentration [ppm] 2000² 2000³ - 300 500 700 450 650 850 400 600 800 -

Relaxation Time [s] 1.09 4.77 - <0.01 <0.01 1.671 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 -

Soi Mean [%] 84 80 75 77 73 74 76 77 80 78 75 77 74

Sor Mean [%] 51 49 76 45 38 45 38 28 34 32 32 34 77

Recovery Factor 0.49 0.51 0.34 0.55 0.62 0.55 0.62 0.72 0.66 0.68 0.68 0.66 0.23

Pressure Differential Final [mbar] 34.5 43.1 14.6 12.6 18.7 20.1 10.3 22.9 16.6 12.6 30.9 22.8 4.5

Micromodel ID 2 2 2 9 9 9 9 9 9 9 9 9 91 Measured at shear rate: 10 s -1 and 73 °C

² sheared solution

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final saturation after the flooding with polymer A at 2000 ppm unsheared. In these images black represents the grains, blue is the water saturation and green is the oil saturation. Hence, the water saturation initially was 20 percent and in the end it reached 61 percent.

Fig. 10 Initial and final image for polymer A 2000 ppm unsheared in Model 1 (E2)

Fig. 11 Initial and final image for polymer A 2000 ppm sheared in Model 1 (E1)

In Fig. 11 the same experiment is shown for polymer A 2000 ppm sheared. In comparison of the initial saturation there are slight differences visible, which occur due to small differences in the light configuration. Therefore, some grains seem to be connected. Both experiments reach similar recovery factors in the end with 51 and 49 percent. In order to see the difference in the performance, both final saturation distributions are compared in Fig.12.

Fig. 12 Difference of Sheared and Non-sheared flood for oil saturation distribution

Black represents again the grains, grey is water saturation, white is oil saturation in both cases, green is the oil that has been in this position after the sheared flood, purple is the oil that is in this position after the unsheared flood. Here again it can be seen that there has been a different allocation of oil ganglia after the floods, but the amount of oil that is actually more produced due

Injection Production

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to the higher viscosity is rather small. The effect of viscoelasticity in micromodels cannot be excluded with these experiments, but the effect does not seem to be significant. Hence, some ganglia seem to be mobilized but then trapped again in the next pores and that is the reason why they are not produced in the end. As described in the rheology section, the viscoelastic properties of the polymer solutions at reservoir conditions concerning salt concentration and temperature are rather small. Shearing will occur during the polymer injection into the field, which means that even higher polymer concentration would have to be used to achieve the performance of the unsheared solution. This is on the one hand side an economical evaluation, but on the other hand the injectivity of non-sheared solutions is much worse and they can even lead to blocking during the injection.

Fig. 13 Initial and final image for brine in Model 1 (E3)

In comparison to the polymer flood, the result from the water flood shows the situation, when the viscosity ratio and therefore the mobility ratio is worse. The water saturation has been increased from 25 to 43 percent (

Fig. 13). The less stable front induced by the weaker viscosity ratio results in viscous fingering and a smaller areal sweep efficiency. The flooding results of the investigation in model 2 focused on the variation of the viscosity ratio. The experiment with the polymer A at a concentration of 500 ppm is picked as an example. The recovery factor of this flood was 62 percent and the experiment can be seen in Fig. 14. The water saturation increased from 27 to 72 percent. The water flood in the same model (Fig. 15) gives an increase in water saturation from 26 to 43 percent, again clearly showing viscous fingering and poor areal sweep efficiency.

Fig. 14 Initial and final image for polymer A 500 ppm in Model 2 (E5)

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Fig. 15 Initial and final image for brine in Model 2 (E13)

From the result table it can be seen that the applied viscosity ratio does not show a clear trend in the recovery factors in model 2. The highest ratios of 7 had the worst performance in all three polymer solutions. The best performances were given by the viscosity ratio of 3, whereby in general polymer A had the weakest temperature stability, which is also shown in the rheology section. The third part of the investigation was the pressure difference during the experiment. On the one hand it depends on the viscosity of the injected fluid, since every injection was performed at the same rate, but on the other hand the highest pressure difference was observed at the experiments with the highest recovery factor in the end. This might be due to the slightly different initial conditions in the model prior to the floods and how the channels formed during the experiment. From the micromodel point of view it is the viscosity of the polymer that should be slightly above the live oil viscosity, to counteract absorption, which cannot be tested in micromodels so far. Two polymer floods are shown in Fig. 16 in terms of oil recovery. The core flood started at a water saturation of 39 percent and finalized at 80 percent, leading to a recovery factor of 67 percent. The micro-model flood (E5) described above recovered 62 percent.

Fig. 16 Oil recovery vs. PV injected in core flooding and micromodel experiments for polymer flooding

The results in terms of recovery factor are qualitatively similar and promising, but the set up is different. The core is flooded in a linear way and the models are operated in a quarter of a five-spot pattern. Therefore, a direct comparison is difficult to accomplish.

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Summary and Conclusion • Micromodels have been designed and constructed based on high resolution images of thin sections of reservoir rocks. These models attempt to resemble the properties of the thin section photograph (not of core plugs). • Compared to flooding experiments performed in cores or plugs, micromodels provide visual access to the pore-scale displacement of oil by aqueous polymer solutions. • The first normal stress difference N1 of the aqueous polymer solutions considered in this work showed a strong dependence in temperature and salinity at the prevailing experimental conditions. Although a clear first normal stress difference was measurable at standard conditions and high shear rates, the higher the temperature the lower was the N1 response. The N1 measured at typical reservoir shear rates (≈ 10 s-1) is considered a result of mechanical artefacts during the measuring time as well as possible drifting in the solution and not as a viscoelastic response of the aqueous polymer solution. • Shearing of aqueous polymer solutions results in a strong reduction of the characteristic relaxation time. • In order to measure a strong viscoelastic response (relaxation time, first normal stress difference and elongational viscosity) polymer concentration must be large (>> 1000 ppm) at the experimental conditions considered. • The increased amount of polymer product required to establish a viscoelastic polymer flood can result in injectivity issues (not investigated here) and leads to higher project cost. These costs must be balanced by an incremental oil recovery that results from the increased polymer concentration. • However, the results obtained in Model 2 indicate that increasing the polymer concentration and thus the polymer solution viscosity does not result in a significant increase in oil recovery factor. In fact, the highest recovery factors were achieved with the medium concentrations of Polymer B and C. • As expected all polymer floods result in larger oil recovery compared to waterflooding. The additional oil recovery can be contributed to the improved viscosity ratio (mobility) and increased pressure differential in secondary mode. • The difference between a high and a low viscoelastic polymer flood (4.77 and 1.09) show a difference in recovery factor of 2 %, only. This is by far not significant and cannot be clearly contributed to elasticity as the pressure differential during the viscoelastic flood was larger. However, more repeated experiments are required to establish a statistical trend that could reveal an impact of viscoelasticity on oil recovery. Nomenclature η Viscosity, [mPas] σ Shear Stress, [Pa] ϒ Shear Rate, [s-1] G’ Elastic Modulus, [Pa] G’’ Viscous Modulus, [Pa] σ * Complex Shear Stress [Pa] έ Apparent Extensional Shear Rate, [s-1] ω Angular Frequency [rad/s] η* Complex Viscosity, [mPas] N1 First Normal Stress Difference [Pa] D.H Degree of hydrolysis References • Ganzer, L., Wegner, J. and Buchebner, M.., 2014. Benefits and Opportunities of a “Rock-on-a-Chip” Approach to Access New Oil - Oil Gas-European Magazine 39, p 43-47, Urban-Verlag Gmbh P.O Box 70 16 06, D-22016 Hamburg, Germany, 2014 • Efros, A. and Leung, T. Texture synthesis by non-parametric sampling. In International Conference on Computer Vision, volume 2, pages 1033–8, Sep 1999. • Littmann W., 1988. Polymer Flooding: Development in Petroleum Science 24, Elsevier Science & Technology. • Dupas, A., Hénaut, I., Rousseau, D., Poulain, P., Tabary, R, Argiller, J.-F, and Aubry, T. Impact of Polymer Mechanical Degradation on Shear and Extensional Viscosities: Towards Better Injectivity Forecats in Polymer Flooding Operations. Paper SPE 164083 presented at the SPE International Symposium on Oilfield Chemistry, The Woodlands, Texas, 8-13 April 2013. • Karau, D., Martischius, F.D., Sewe, K.U., and Weinreich, H.J.: Polymer Project Bockstedt: New Technical Equipment for Dissolving and Shearing Polymers for EOR, Proc., 4th European EOR Conf. (Nov. 1988) 195-205. • Macosko, Christopher W. 1994. Rheology: Principles, Measurements, and Applications. First Edition August 1994. John Wiley & Sons • Pipe C.J., Kim N.J., Mc Kinley G., Microflidic Rheometry on Chip, the 4th Annual European Rheology Conference (AERC 2007), Napoli – Italy 2007.

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