Design and Performance Prediction of an MEOR Field Pilot by Numerical Methods Summary

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1 Design and Performance Prediction of an MEOR Field Pilot by Numerical Methods H.K. Alkan* (Wintershall Holding GmbH), I. Gutierrez (Wintershall Holding GmbH), H. Bülltemeier (Freiberg Technical University) & E. Mahler (BASF SE) Summary This paper is dealing with the design and performance prediction of an MEOR field pilot by numerical methods. With MEOR, additional oil production is generated via a combination of various EOR mechanisms triggered through the growth of the microorganisms. Numerical modelling of MEOR performance is challenging mainly due to the complexity of describing the processes that take place in the reservoir. However, numerical evaluation is yet an inevitable part of a field application. Wintershall and BASF are conducting experimental and numerical studies on MEOR to establish it as a matured technology in a kind of tool-box concept for adding value to its assets. The technology developed so far will be tested in a pilot planned for one of Wintershall s German oil fields within the next years. Numerical work is being performed via lumped as well as discretized models. The model of Bryant and Lockhart (2002) is used to describe the reaction engineering constraints in order to optimize the inoculation versus residence time. The necessary input data are provided by growth curves obtained by feeding nutrients to defined microorganisms of the corresponding reservoir. The prediction of the production performance of the planned field pilot is performed by the numerical implementation established and validated by Wintershall using STARS/CMG as the supporting tool. The new MEOR model is calibrated with laboratory data including growth curves, measured metabolite properties and dynamic experiments, which are later up-scaled for the field pilot. The following EOR effects are considered: changes in both water and oil phase viscosities, decrease in IFT and bio-plugging. Sensitivity runs performed on a 5-spot injection pattern generic model of the field chosen for the pilot show that the dominating effect on additional oil recovery would be the flow diversion by the plugging preferential water injection paths through microbial growth.

2 MEOR as an Emerging Technology Microbial Enhanced Oil Recovery (MEOR) is listed among other methods of EOR classification. MEOR is based on the life and metabolism of microorganisms. By stimulating bacterial growth with addition of nutrients, a number of metabolites (=products of bacterial metabolism) are produced that can have beneficial effects on oil recovery. MEOR processes relying on metabolites generated directly in the reservoir are called in-situ processes. This kind of application can be further subdivided depending on the origin of bacteria: either use of external (surface-generated and injected) bacteria, or indigenous (already present in the reservoir) ones. The state of the art tends to the second option: The definition of the beneficial microorganisms in-situ (in the reservoir) and feeding them with selected nutrients resulting in the growth of the microorganisms and in the generation of the metabolites both acting on EOR. Just as an EOR method, MEOR should also be tailored to the objective reservoir. This study differs from similar works for all EOR applications being more field-specific, complex and detailed. The reason for that is the necessity to investigate the microorganisms specific to each field and to define their capability in terms of metabolism, nutritional needs and thus resulting EOR mechanisms. Since the conditions in oilfields can be very different in terms of pressure, salt content and temperature, also the microbial communities will differ that are present in the reservoirs. Another challenging aspect of MEOR is its numerical modelling. A short statement summarizes this challenge: There exists no commercial reservoir simulator that can model an MEOR field application. There are attempts to model the behavior of the microorganisms in porous media including the generation of metabolites coupled with EOR mechanisms leading to oil recovery. These are academic in nature and limited in the application. All of the previous simulation attempts are limited in terms of field scale computations making them inappropriate for reservoir modeling. Currently there exists no commercial reservoir simulator that can model MEOR although almost all mechanisms attributed to MEOR are mathematically very well described. The major reason for this is the difficulty of modeling the bacteria and their growth behavior under a standard reservoir modeling scheme. The difficulties of isolation of the individual mechanisms from laboratory works and calibrating the empirical parameters of the proposed models are other handicaps for an appropriate modeling. Another problem for commercial simulator developers is certainly the lower demand for MEOR applications; the applications are mostly performed as a black-box technology without any detailed study and response on the individual mechanisms experimentally and numerically. Wintershall and BASF are conducting experimental and numerical studies on MEOR to establish it as a matured technology in a kind of tool-box concept for adding value to its assets (Alkan et al, 2014). The technology developed so far will be tested in a pilot field trial planned for one of Wintershall s German oil fields within the next years. Numerical work is being performed via lumped as well as discretized models. In this paper the actual status of the numerical modelling work package of the project is presented and results are discussed. The model of Bryant and Lockhart (2002) is used to describe the reaction engineering constraints in order to optimize the inoculation versus residence time. The reservoir nutrient logistics and the produced metabolite concentrations are estimated based on conversion kinetics. The necessary input data are provided by growth curves obtained by feeding nutrients to defined microorganisms of the corresponding reservoir in the laboratory. The prediction of the production performance of the planned field pilot is performed by the numerical implementation established and validated by Wintershall using STARS/CMG as the supporting tool. The following EOR effects are considered: changes in both water and oil phase viscosities, decrease in IFT and bio-plugging. Sensitivity runs performed on a 5-spot injection pattern generic model of the field chosen for the pilot show that the dominating effect on additional oil recovery would be the flow diversion by the plugging preferential water injection paths through microbial growth and/or biofilm formation.

3 Mathematical Modelling of MEOR; a Literature Survey In the literature there are few attempts of modeling the MEOR mechanisms in porous media including the prediction of the lifetime of bacteria. Islam (1990) proposed a three-dimensional model in which the bacterial transport is modeled as the concentration of cells in the water phase. The adsorption of nutrients to the rock surface is also considered. The dependence of bacterial growth on nutrient concentration and time is represented by the Monod Equation. The decrease in permeability is due to plugging and is provided with an empirical correlation. The model is used to simulate the MEOR applications with bacterial plugging, IFT reduction and CO 2 generation. Chang et al. (1991) described the development of a three-dimensional, three-phase, multiple-component numerical model, with the input of laboratory investigations to describe the microbial transport phenomena in porous media. The proposed microbial transport formulation considers the dispersion, convection, chemotaxis, clogging/declogging and injection/production of the microbes in the aqueous phase. For modeling the growth of microbes again the Monod Equation is applied. Monod kinetics was investigated to account for all kinds of products, cells, and substrate inhibition with variations of the basic equation (Button 1985, Han and Levenspiel, 1987, Merchuk and Asenjot, 1995). The simplest form of the Monod equation is given for the bacterial growth rate, r g as follows: r g r K S S + S = max (1) In which S is substrate concentration, r max is the maximum growth rate and K S is the half rate constant representing at which the growth rate is r max /2. The laboratory experiments are reported to be successfully used to calibrate the modeling parameters. Desouky et al. (1996) proposed a one-dimensional model to simulate the process of enhanced oil recovery by microorganisms. This model involves five components (oil, water, bacteria, nutrients and metabolites) with adsorption, diffusion, chemotaxis, growth and decay of bacteria, nutrient consumption and porosity/permeability reduction effects. The adsorption of bacteria on the rock surface is modeled with Langmuir type isotherms. For the developed simulator laboratory experiments are reported to be successful in determining the input parameters of the microbial transport system. The efforts of Tiwari and Bowers (2001) focused mainly on modeling biofilms thus on modeling the alteration of the hydrodynamics by the changes on porosity and permeability. Thullner et al. (2003) developed a model simulating reactive transport in groundwater including plugging. Results from a plugging experiment in a flow cell with a two-dimensional flow field are used as a data base to verify the simulation results of the model. Kim (2006) proposed a mathematical model to describe bacterial transport in saturated porous media. Reversible/irreversible attachment and growth/decay terms are incorporated into the transport model. Additionally, the changes of porosity and permeability due to bacterial deposition and/or growth are accounted for in the model. The predictive model is used to fit the column experimental data from the literature, and the result showed a good match to these data. Nielsen et al. (2010) developed a numerical model describing the processes of MEOR. This is launched as the extension of a compositional streamline simulator where reactive transport is combined with a single compositional approach. The model describes the displacement of oil by water containing bacteria, substrate and the produced metabolite, in this case surfactant. The metabolite is allowed to partition between the oil and water phases according to a distribution coefficient. A three-dimensional multi-component transport model in a two-phase oilwater system was developed by Behesht et al. (2008). For the first time, effects of both wettability alteration of reservoir rock from oil-wet to water-wet and reduction in interfacial tension (IFT) simultaneously on relative permeability and capillary pressure curves were included in MEOR modeling. Transport equations were considered for the bacteria, nutrients, and metabolites (biosurfactant) in the matrix, reduced interfacial tension on phase trapping, surfactant and polymer adsorption, and effect of polymer viscosity on mobility of the aqueous phase. The model was used to simulate effects of physico-chemical parameters, namely flooding time schedules, washing water flow

4 rate, substrate concentration, permeability, polymer and salinity concentration on original oil in place (OOIP) in a hypothetical reservoir. All of the above simulation attempts are limited in terms of field computations making them inappropriate for reservoir modeling. In the following chapters of the paper some results of the numerical works being conducted in the frameworks of the project conducted by Wintershall and BASF are summarized. It should be mentioned that in this project the preferred MEOR method is to define and inject the screened nutrients for defined in-situ beneficial microorganisms for the field selected. The metabolites generated as the result of microbial nutrient conversion and the microorganisms themselves are creating the EOR effects. Simplistic Approaches; A Lumped Material Balance Model Bryant and Lockard (2002) proposed a simple lumped model to describe the interplay between the microbiological and reservoir engineering processes occurring during a MEOR application. Assuming a simple cylindrical reservoir part around an injector which can be considered as a bioreactor of huge dimension with a radius, r m and reservoir thickness h and porosity φ for the volumetric injection rate of the nutrients Q and S or residual oil saturation, the time spent by the injected fluid in this bioreactor (residence time) can be calculated by the following equation: 2 tres = πhrmφ (1 Sor ) / Q (2) As expected, the residence time of the injected fluid grows as the injection rate decreases, thus as the advance rate in the reservoir decreases. The equation can be reformulated to estimate the radius of the nutrient solution to be injected into the reservoir during a time period of t res. On the other hand, the time t rec required for the microbial reaction to produce a useful concentration of C rec can be calculated by assuming a first order rate of production from nutrient N, according to the stoichiometric relationship: N v N C rec (3) To estimate the reaction time, the authors assumed isothermal plug flow through the reactor, that consumption of N is first order and irreversible, and that it is injected at initial concentration n 0. The rate equation is: C rec = v k1t e k n k1 rec t 1 C = ln rec k1 vnn N 1 0 rec 1 0 (4) where the stoichiometric coefficient v N defines the conversion efficiency of nutrient into product. In order to be effective, the residence time should be greater than the reaction time so that the reaction takes place: trec t res (5) Economics favors higher injection rates but lower rates are needed for an effective MEOR. Higher flow rates imply lower residence times and hence require higher rate constants, k, that means higher reaction rates. Actually this condition can be used to estimate the distribution of produced metabolites at a given time in the reservoir. For a better understanding of this approach, let s say we want to create a bioreactor around a well. With data given in Table 1, the residence time can be calculated as depicted in Figure. 1. The injected fluid contains nutrients at a predefined concentration. The front of the injected water is calculated by

5 the Eq. (2) which neglects the dispersion which is an acceptable assumption with comparison to the convective influence. The front is sharp as it only shows the water phase; no distinction in the concentration is made. The injected fluid which also contains the nutrients reaches approximately 20m from the injector in 26 days, approximately. In Figure 1 the calculated microbial activity fronts are also depicted for two various reaction rate constants, thus bacterial growth rates. As can be concluded in both cases the microbial activity (in which the metabolite reaches its effective concentration) stays behind the hydraulic front, starting after some time which is determined with the reaction rate constant k. For the case where the reaction rate is higher (k=0.3s -1 ), metabolite activation starts in approximately three days and thus the MEOR activity starts in a closer distance to the well. In the case where the reaction rate is lower (k=0.1s -1 ) the activation of the metabolite requires ten days which means at a larger radius. For an even lower reaction rate constants the criteria given in Eq. (5) are not fulfilled and there will be no activity during the injection behind the hydraulic front. Table 1 Data used in the analytical calculations Property Value Thickness, m 10 Porosity, S or, - 0,5 Injection rate, m 3 /day 70 v N, use of nutrient efficiency 1 N o, initial nutrient concentration 1 C rec, conc. required for a metabolite to be effective 0.9 r m, radius of the bioreactor, m 20 Figure 1 Profiles of the hydraulic and microbial fronts for various activation times The radius of the microbial slug injected increases with injected volume; the activated part of this volume will always depend on the activation time (reaction rate) as exemplarily shown in Figure 1. However, two common practices avoid the plausibility of this situation. For instance if the injection

6 stops, the injected volume will be totally activated as the nutrients find sufficient time for the reaction. In a second practice if the nutrient solution slug is followed by a water injection this would also give the nutrient slug sufficient time to be activated. Figure 2 shows the calculated nutrient consumption for two activation rate constants given in Figure 1. The consumption starts as soon as the injection begins, the concentration is decreasing with a higher rate for the case k=0.3s -1. As expected the consumption of nutrient resulted in the generation of the metabolites und thus activation of the EOR effects. After the total consumption of the nutrients the metabolites should be active to fulfil their defined EOR activities in a region reaching the hydraulic front. Figure 2 Profiles of nutrient consumption for various activation times Discretized Modelling; Wintershall STARS MEOR Implementation A work package of the project MEOR Studies being conducted by Wintershall and BASF is dealing with a MEOR numerical simulator due to the reasons mentioned in above sections. As a first step of this work package a numerical tool for modeling MEOR has been developed. The capability of this numerical tool has been tested with small scale models, namely sandpacks. Based on these efforts a pragmatic however physically and numerically sound modeling concept has been developed. This concept is implemented into the simulator STARS of CMG (Computer Modelling Group) successfully. The commercial simulator STARS has been chosen for its modeling flexibility and also because of the willingness of CMG for cooperation. STARS is a three-phase, multi-component thermal and steam-additive simulator. The grid systems STARS handles are Cartesian, cylindrical, or variable depth/variable thickness, with 2D and 3D configurations possible (including full-field modelling) in any of these grid systems. Its most relevant features for our purposes, are: (1) the modelling of dispersed components (stabilized dispersion like droplets, bubbles and lamellae), which can be treated as components in the carrying phase that provides the flexibility of modelling and quantification of bacteria, nutrients, biopolymers, biosurfactants and CO 2 for our specific case. This concept can be coupled with the component property input package capabilities, like adsorption, blockage, non-linear viscosity, dispersion and non-equilibrium mass transfer to model complex phenomena via input data choices. (2) The reaction kinetics module, allows the calibration of the bacteria concentration as it changes with time and nutrient availability, as an analogy to the Monod Equation. It also enables the generation of metabolites as a product of the biochemical reactions (STARS, 2012).

7 In general, if a nutrient (substrate) is added to a fluid with selected bacteria, the nutrients can be consumed and converted into metabolites and therefore the bacterial growth process can be formulated with a chemical reaction of the following form: In STARS, simulation of the chemical processes requires the definition of related reactions (STARS, 2012). There is no fixed scheme, so the user can define practically everything, making the usage of the simulator flexible: The in Eq. (6) presented chemical reaction between nutrients and bacteria can be easily applied to the scheme above (Eq. 7). The stoichiometric coefficients are responsible for the material balance between bacterial number (concentration) and the nutrient supply. As many reactions as necessary can be applied for the investigated MEOR process if experimental evidence allows. For example if bacteria use multiple energy sources (nutrients, substrates) and grow at different rates correspondingly, multiple reactions have to be defined. The reaction rate (how many reactions/bacteria per time) which replaces the Monod kinetics is calculated depending on the frequency factor, temperature and concentration of reacting components, i.e. bacteria and nutrients with Arrhenius Equation. By setting the activation energy equal to zero an isothermal reaction is obtained. This simplification is made in our case; because the application can be assumed as an isothermal one as only nutrient solution is injected being heated up in the reservoir in a short time as it advances into porous media. The reactant concentration factors are calculated depending on their concentration in a reference phase and reactants are considered as tracers in water and oil phases. Their EOR effects are given as a function of their concentration in the existing phases. A detailed description of the EOR effects namely changes in the water and oil phase viscosities, IFT due to metabolites in water phases and generated CO 2 implemented this way is given in Bültemeier et al, The last implementation has been the plugging effect based on adsorption analogy of the bacterial growth in the porous media. The idea behind using this approach is that microorganisms and the metabolites cause a permeability reduction in the high permeable layer, where nutrients are readily available and the metabolism of the microorganism creates cell mass and/or a biofilm that restricts the flow of one or all the phases. The residual resistance factor (RRF) is the factor that gives the magnitude of the level of reduction in permeability caused by the adsorbed metabolite or microorganisms. An estimation of this value from a microbiological point of view is challenging and constitutes one of the biggest uncertainties in this approach, given that the MEOR metabolites and bacteria themselves have a behaviour that is significantly different from conventional synthetic polymers and surfactants with a clear and defined molecular structure and characteristics. The other factor of importance in the modelling of the microorganism adsorption that causes a permeability reduction is that the behaviour of the attached (or adsorbed) microorganisms is poorly understood, but it does not hinder the modelling of the observed effect without it being too simplistic. Lastly, the adsorption curves (concentration of metabolites vs. concentration of attached/adsorbed microorganisms/metabolites), are so far not being generated experimentally, instead they have been generated so that the metabolites lost to the rock are minimum, but still cause a permeability reduction and enough is left available in the fluid phase to produce other effects. The residual resistance factor for the water phase ( ), the permeability reduction factor ( ) and the effective permeability ( ) equations are as follows: (6) (7) (8)

8 (9) (10) Where, : Permeability reduction factor of the water phase : Adsorption in the grid, gmol/m 3 : Maximum adsorption, gmol/m 3 : Effective permeability of the water phase : Relative permeability to water : Absolute permeability Other premises that have been assumed are that the entire pore volume is accessible for metabolite adsorption, and that adsorption/attachment is irreversible. Having defined the equations involved in the reaction kinetics that will enable us to model the bacterial life cycle, the following assumptions are taken for the calibration of the model: There is no temperature dependence, given the reservoir temperature is assumed constant and the growth of the bacteria was possible at a constant 37 C in the laboratory. As the oil reservoirs are generally isothermal this is a plausible assumption. Pressure does not affect the reaction rate either, it was confirmed by laboratory experiments at 30 bar that the effect of pressure on microbial activity is negligible, hence no pressure dependence is included. The reactions are set to first order reactions, meaning the reaction orders of the all reactants are set to the unity, which makes the reaction rate dependent on the concentration of both reactants: bacteria and nutrients. Only advective flow is considered, given that no total dispersion coefficients are specified, neither molecular diffusion nor mechanical dispersivity coefficients are given. The metabolites generated are assumed to be preserved in time, assuming they do not undergo any decay process, and hence their effects are conserved nevertheless dependent only on concentration. Before implementing the modelling concept into numerical simulation, experimental data from the laboratory collected in order to explain and understand the relevant phenomena occurring during MEOR processes has been and to this day is still being used for the calibration and validation of the model. To mention it briefly, it includes the initial sampling and profiling of the microorganisms from the different pre-screened fields, the cultivation of the microorganisms in the laboratory and screening for nutrients with subsequent characterisation of the enrichment cultures and characterisation of the metabolites of these in batch-type experiments. Following, a series of dynamic experiments were (still are) conducted in sandpacks and core flooding. There are numerous amounts of laboratory work and data, but only the relevant data for the numerical simulation are addressed in the following sections, each where it is used. Moreover, since microorganisms and metabolites can be incorporated into numerical simulation in the form of tracers or dispersed components in the water phase, the results obtained from the lab needed to be transformed into STARS language. In addition to batch experiments, a series of dynamic experiments were carried out to observe the recovery in sandpacks, with different configurations of injection and set up. The bacterial growth observed in the batch experiment is representative to the growth in the middle of the sandpack during the first inoculation period and, the flow that may occur during this static phase due to gravity

9 segregation is negligible. Consequently, a single grid cell in the numerical configuration of the sandpack was established as the monitoring cell for the bacteria moles in the water phase, to be compared and history matched to the lab data. Using a typical routine of history matching in CMOST (a history matching, optimization, sensitivity analysis, and uncertainty assessment tool from CMG), setting the objective function as the mole fraction of bacteria in the water at the monitoring cell, and providing CMOST with a range of values for each of the parameters mentioned in the calibration of the bacterial growth was possible and the results obtained as compared to the observed data, are shown in Figure 3. Figure 3 Modelling the bacterial growth in the first period of inoculation at various nutrient concentrations The most important learning from this approach is that, at each of the nutrient concentrations injected to the sandpack, the calibration process had to be repeated, yielding consequently a different chemical equation for each nutrient formulation. Additionally, efforts are being made on the experimental side to try and obtain an actual the profile of the microorganisms in the sandpacks, but they are still in progress, therefore this approach is considered valid with the available data at the moment. In addition to growth curves, following experimental data have been used to calibrate the model: viscosity-shear rate relationship of the water phase with dissolved metabolites, the oil viscosity as function of the CO 2 generated as gas metabolite and the IFT between the water (with metabolite) and oil phase, all at reservoir temperature being 37 C. Having calibrated the growth curves and ensuring the production of the metabolites and their corresponding effects in the sandpacks, a conceptual up-scaled model was created to observe the MEOR effects in a field-size scale, and to run a sensitivity analysis to understand each individual effect and the synergy of the combination of various effects with the corresponding impact they have on the total oil recovery. This study is performed as a prior step to a full/sector field simulation of the planned pilot field in the north of Germany. The model consisted of one-eighth of a normal 5-spot injection pattern as schematically given in Figure 4. The porosity, permeability, injector-producer distance and other reservoir and production properties used in this model are in the typical range for what is expected from the field (Table 2).

10 Table 2 Reservoir properties used in the conceptual model Property Value Distance injector-producer, m 100 Porosity, % Permeability (I,J), md Kv/Kh 0.01 Reservoir Pressure, bar 30 Max. Water Injection Rate, sm 3 /d 100 Figure 4 Schematic and 3D description of the conceptual model To establish which of the MEOR effects or combinations thereof have a major impact in the final recovery, every possible combination of effects and/or isolated effects were tested under three different production strategies defined in Table 3. To identify the cases, the notation used refers to the effect that has been activated for the named case. The 12 or 23 in the polymer cases indicate the maximum viscosity for the water phase and similarly the 10 or 100 in the plugging cases refer to the Residual Resistance Factors used in the case. The SR indicates that the velocity dependent viscosity model has been included (it stands for Shear Rate). Results and Discussions The growth and decay of microorganisms and production of metabolites was successfully modelled in all of the cases. The front advancement in terms of bacterial and metabolite mole fraction in the water phase may be observed in Figure 5 at different time steps for the three production schemes. As it may be seen in the Figure 5, the microorganisms continue to grow as long as nutrients are available, as soon as they are depleted and only water is injected then the decay over time is evident, until it reaches very low metabolite amounts in the reservoir. The main flow mechanism pictured here is the advective movement of bacteria and metabolites in the porous media, as mentioned before; dispersion and chemotaxis are negligible and therefore not modelled here.

11 Table 3 Production strategies applied in MEOR modelling in STARS Production Sequence - Strategy #1 PVI Time Waterflooding 3 4 years Injection of nutrients ~ days Final waterflood 1-2 ~4 years Production Sequence - Strategy #2 PVI Time Waterflooding 3 4 years Injection of nutrients ~ days Chasing waterflood months 2 batch of nutrients ~ days Final waterflood 1-2 ~4 years Production Sequence - Strategy #3 PVI Time Waterflooding 3 4 years Injection of nutrients ~ days Final waterflood 1-2 ~4 years The metabolites which are the ones causing the different effects, in this case only pictured the metabolites dissolved in the water phase, they remain in the water i.e. laboratory data indicate that they do not undergo any decay process after being produced and are constantly being pushed towards the producer by the injection of water (with or without nutrients, depending on the time and case) unless they are mixing with formation water causing a decrease in the concentration. It can clearly be seen that the amount of metabolites produced after a year in the first production scheme is much lower than in the last, and since the effects are directly related to the amount of metabolites, this echoes that the oil recovery is in general terms higher with the production strategy #3. The main results in terms of recovery factors are presented in the Table 4. In Table 4, the traffic light notation indicates, how high the recovery factor is above the waterflooding case. The green light for cases with recoveries greater than 5, yellow for RF in the range of 3.5 till 5, red for cases below 3.5 till 2 and black for cases with recovery less than 2. Using this notation the main two conclusions from this study are easily identifiable: Isolated effects have a very low to no impact in oil recovery, as it is seen especially in the cases for the IFT and oil viscosity reduction by CO 2. The polymer effect and selective plugging are the effects that contribute the most to the ultimate recovery, especially if combined. Taking as example the case with strategy #3 and with the effects of Plugging 100 and Polymer 23 with active shear rate dependency, the recovery was calculated as given in Figure 6 compared to the waterflooding only case.

12 Strategy #1 Strategy #2 Strategy #3 After 10 days of MEOR Injector 1 Producer After 30 days of MEOR Injector 1 Producer Injector 1 Producer Injector 1 Producer After 8 months of MEOR After 1 year of MEOR Scale High Low Figure 5 Modelling metabolite concentrations advancing in the generic model Having mentioned that the plugging and polymer effects are the most significant for this MEOR application, in Figure 7 the cross-section can be seen with the water saturation profile, water viscosity and the water fractional flow, after nine months of the MEOR for the production strategy #3. It is evident that, in the case with low residual resistance factor the advancement of the viscous water is less uniform than in the case with high RRF. The movement of the waterfront in the high permeability

13 layer is retarded and in the low permeability layer is favoured, extracting the significant amount of residual oil that was left behind after the waterflooding period. It is therefore key for the plugging effect to have an important contribution to the total recovery factor, that there is enough oil bypassed in the secondary production stage. Also, it must not be overlooked, that in the case of plugging the values used for this study come from reasonable conjectures, and qualitative laboratory observations and these results represent only a macro estimate of what can be achieved in the field. Table 4 Results of the runs performed with generic model with isolated and combined EOR effects Prod. Strategy #1 Prod. Strategy #2 Prod. Strategy #3 RF, % Increment to WF RF, % Increment to WF WF Waterflooding RF, % Increment to WF Independent Effects 1 Polymer Polymer IFT CO Plugging Plugging Polymer23 + SR Combined Effects Two 8 Polymer12 + IFT Polymer12 + CO Polymer12 + Plugging Polymer12 + Plugging Polymer23 + Plugging SR IFT + CO IFT + Plugging IFT + Plugging CO2 + Plugging CO2 + Plugging Three 18 Polymer12 + IFT + CO Polymer12 + IFT + Plugging Polymer12 + IFT + Plugging Polymer23 + IFT + Plugging SR IFT + CO2 + Plugging IFT + CO2 + Plugging All 24 Polymer12 + IFT + CO2 + Plugging Polymer12 + IFT + CO2 + Plugging Polymer23 + IFT + CO2 + Plugging SR The case of the polymer only option; this is based on strong quantitative observations and the outcome from a successful MEOR application and will depend more on the heterogeneities in the reservoir that will lead to different velocities and therefore viscosity yields, as expected from the results in the laboratory. In Figure 8 it can be observed that the heterogeneities in the vertical direction in combination with the amount of metabolites present and the shear rate equally gives a very disparate viscosity distribution. Two additional cases were run for a homogeneous reservoir to observe the perfect ideal uniform displacement behaviour of the viscous waterfront for when the SR dependency is not included and nearly perfect when it is.

14 Figure 6 Calculated production histories in generic model Strategy #3 Only Waterflooding Plugging with RRF=10, Polymer 12 Water saturation, after 9 months of MEOR Strategy #3 Plugging with RRF=100, Polymer 12 Water viscosity, after 9 months of MEOR Water fractional flow, after 9 months of MEOR Figure 7 Effect of plugging and polymer in cross-sectional view of the generic model

15 Only Waterflooding Heterogeneous, SR-dependent Homogeneous, SR-independent Homogeneous, SR-dependent Scale, [mpa.s] Figure 8 Calculated viscosity distribution (polymer effect) in cross-sectional view Conclusions Following conclusions are drawn from the study: The lumped modelling approach can be used to estimate the approximate fronts of the MEOR zones activated by injected nutrients. This approach would also give the volume of the metabolites including liquid and gas. With present knowledge on the selected nutrients and injection rates to be used the proposed criteria for the activation of the MEOR zone is assured for the field application. The implementation of the developed modelling concept into STARS has been validated. Although some limitations still exist, the calibrated model can be used for predicting the recoveries in a MEOR application. The main limitation is due to the missing and not yet reliable experimental data, especially in the case of plugging mechanism. Modelling runs performed using the experimental data from the ongoing project shows that the additional recoveries are expected to be around 5-7%OOIP. For the case studied the most effective EOR mechanisms are the plugging and the increase in the water phase viscosity. The change in oil

16 viscosity and IFT are calculated to have minor to negligible effect on oil recovery. However it should be noted that the data to calibrate the plugging effect is the most difficult one and for a more realistic prediction of the oil recovery more efforts are needed to estimate the relationship between the biofilm formation-adsorption of microorganisms and permeability reduction. Acknowledgement The authors appreciate the works of the laboratory personal who delivered the experimental data used to calibrate the model. References Alkan H., Biegel E., Krüger M., Sitte J., Kögler F., Bültemeier H., Beier K., McInerney M.J., Herold A., Hatscher S. (2014). An Integrated MEOR Project; Workflow to Develop a Pilot in a German Field, SPE Improved Oil Recovery Symposium, Tulsa, Oklahoma, USA, April. Behesht, M., Roostaazad R., Farhadpour F., Pishvaei M. R. (2008). Model Development for MEOR Process in Conventional Non-Fractured Reservoirs and Investigation of Physico-Chemical Parameter Effects, Chem. Eng. Technol. 31, No. 7, Bryant S. L., Lockhart P. T. (2002). Reservoir Engineering Analysis of MEOR, SPE Reservoir Evaluation & Engineering, February, Bültemeier H., Alkan H., Amro M. (2014). A New Modeling Approach to MEOR Calibrated by Bacterial Growth and Metabolite Curves, SPE EOR Conference at Oil and Gas West Asia held in Muscat, Oman, 31 March 2 April. Button D.K. (1985). Kinetics of Nutrient-Limited Transport and Microbial Growth, Microbiological Reviews, Sept Chang, M-M., Chung F.T-H., Bryant, R.S., Gao, H.W., Burchfield, T.E. (1991). Modeling and Laboratory Investigation of Microbial Transport Phenomena in Porous Media, SPE presented at 66th Annual Technical Conference and Exhibition of the Society of Petroleum Engineers held in Dallas, TX, October 6-9, Desouky, S.M., Abdel-Daim, M.M., Sayyouh, M.H., Dahab, A.S. (1996). Modelling and Laboratory Investigation of Microbial Enhanced Oil Recovery, Journal of Petroleum Science and Engineering 15, Han K., Levenspiel O. (1988). Extended Monod Kinetics for Substrate, Product, and Cell Inhibition, Biotechnology and Bioengineering, Vol. 32, Pp Islam M.R. (1990). Mathematical Modeling of Microbial Enhanced Oil Recovery, SPE presented at 65th Annual Technical Conference and Exhibition of the Society of Petroleum Engineers held in New Orleans, LA, September Kaster K.M., Hiorth A., Kjeilen-Eilertsen G., Boccadoro K., Lohne A., Berland H., Stavland A., Brakstad, O.G. (2011). Mechanisms Involved in Microbially Enhanced Oil Recovery, Transp Porous Med DOI /s Thullnera,T., Schrotha, M.H., Zeyera, J., Kinzelbach W. (2004). Modeling of a Microbial Growth Experiment with Bioclogging in a Two-dimensional Saturated Porous Media Flow Field, Journal of Contaminant Hydrology 70,

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