Tu B11 Snorre In-depth Water Diversion Using Sodium Silicate ...

Tu B11 Snorre In-depth Water Diversion Using Sodium Silicate - Evaluation of Interwell Field Pilot V.R. Stenerud* (Statoil ASA), K. Håland (Statoil ASA), K. Skrettingland (Statoil ASA), Ø. Fevang (Statoil ASA) & D.C. Standnes (Statoil ASA)

SUMMARY Declining oil production and increasing water cut in mature fields indicate the need for improved conformance control. In this paper we report on the numerical modeling performed to evaluate the indepth water diversion pilot performed for the Snorre field, offshore Norway. For this pilot 240 000 m3 of a sodium silicate solution was injected in the period July to October 2013. The goal of the pilot was to form an in-depth flow restriction for improving the sweep. The setup, execution and measured data from response monitoring for the pilot have been presented in previous papers. As discussed therein, the operation clearly resulted in a strong in-depth flow restriction resulting in delayed tracer responses and decrease in the water cut. However, the monitoring was only limited to well observations, so to understand the spatial and temporal forming of the flow restrictions we had to rely on numerical simulation and modeling. In short, we calibrated simulation models to the observed well responses by introducing flow restrictions; i.e. using history matching techniques. Through the reservoir modeling work we reproduce the pilot response well by introducing sound flow restrictions. This gives us clear indications on the location, timing, strength and corresponding uncertainties of the introduced flow restriction. Moreover, the modeling work supports interpretations from the response monitoring program. Finally, in addition to help evaluating the performed pilot, the learnings from the modeling work will hopefully give more accurate evaluation of potential future water diversion candidates.

IOR NORWAY 2017 – 19th European Symposium on Improved Oil Recovery 24-27 April 2017, Stavanger, Norway

Introduction Increasing water production and bypassed oil calls for actions to increase the oil production and prolong the lifetime of mature fields. In-depth water diversion is a method used to improve the volumetric sweep efficiency, typically caused by thief zone situations. The method is typically implemented by injecting a chemical system to form a flow restriction deep into the formation. The flow restriction will lead to diversion of the injection water thereby moving bypassed oil. To form a deep flow restriction, the chemical system will therefore either need to have a delayed reaction or being triggered by an in-depth condition (typically temperature). Water diversion is sometimes referred to as conformance control. For the Snorre field, offshore Norway, an inter-well deep diversion pilot was conducted in 2013, by injecting 240 000 Sm3 of a sodium silicate solution from July until October (Skrettingland et al. 2016). In addition, there were about 1.5 and 0.5 months of low salinity pre and post flushes, respectively, in order to displace divalent ions that potentially could cause precipitation or accelerated gelling. Prior to the inter-well pilot there had been performed a thin gel treatment of an oil producer (Rolfsvåg et al. 1996), lab work (Stavland et al. 2011) and a single well pilot (Skrettingland et al. 2012). Planning, execution and the response observations for the pilot have previously been reported in several papers (Skrettingland et al. (2014, 2016)). The aim of this paper is to report the numerical modelling and interpretation work performed to consistently understand and connect all major observations. The work allows conclusions for the technical performance of the pilot to be drawn. The operation resulted in a strong in-depth flow restriction which caused delayed tracer responses, decrease in the water cut, and altered pressure responses (Skrettingland et al. 2016). The remaining success criterion to evaluate by modeling, out of the success criteria specified in Skrettingland et al. (2014), is: Proven in-depth flow restriction and minor near wellbore damage. There are indications in the well observations. However, modeling is essential for concluding on the remaining success criterion. In addition, qualified modeling is by itself essential for predictive planning of future water diversion operations with respect to reduce risk and optimize the oil production. We will come back to the evaluation of all the success criteria at the end of the paper. Since the monitoring programme was limited to well observation, numerical simulation and modeling work were initiated in order to understand spatial variation and dynamics of the flow restriction created. In short, we calibrated simulation models to the observed well responses by introducing flow restrictions; i.e. using history matching techniques. To effectively deal with non-unique solutions and uncertainties in the simulation models, we have developed and applied assisted history matching techniques using multiple simulation scenarios jointly. Moreover, the flow restrictions created have been introduced either explicitly, or by a more implicit approach linking the restrictions directly to the silicate mechanisms. These two latter approaches have advantages and disadvantages that will be discussed. The pilot area was primarily chosen with respect to minimizing risk and getting a conclusive result for the pilot - not for optimized oil production. Moreover, the success criteria do not explicitly include increased oil production. Nevertheless, it is of interest to determine the incremental oil production from the pilot, and it is essential to develop good modeling approaches for this purpose for planning future operations. In this work, we have both been using history matched simulation models and decline analysis for trying to estimate the increased oil production from the pilot. The pilot resulted in decreased water cut, but due to creation of a too strong flow restriction, the throughput rate was also reduced (Skrettingland et al. 2016). As a consequence, it was challenging to identify net incremental oil production. In this paper, we will first describe the relevant aspects of the pilot and the methodology used for the numerical evaluation of the pilot. Thereafter, we will present the results of the numerical evaluation in terms of the spatial effect between the wells. Finally, we will present the analysis of the increased oil production for the pilot and summarize the pilot.


Method As for most chemical gel systems, the chemical system used for the Snorre pilot is a so-called thermogel. The main feature of such systems is accelerated formation of a flow restriction when exposed to increasing temperature. This process is often referred to as gelling and results in a strong flow restriction (gel). For the sodium silicate system used for the Snorre pilot, a second mechanism is precipitation. This is a combination of precipitation of Mg(OH)2 because of mixing of alkaline fluid with formation water and precipitation of amorphous Ca-Mg-silicate (Skrettingland et al. 2014). The precipitated silicate will, however, be a small fraction of the injected silicate concentration and the precipitation will produce a moderate flow restriction potentially affecting a larger volume as compared to the volume of the solid gel. Both the gelling and the precipitation are affected by salinity and pH, but as described below the pilot is designed to minimize the influence of these variables. More details on the mechanisms for the Snorre sodium silicate system can be found in Skrettingland et al. (2014). The Snorre operation is described in more detail elsewhere (Skrettingland et al. 2014; Skrettingland et al. 2016). Only a short summary of the details most relevant for the modeling, the interpretation, and the minimization of the impact of salinity and pH is given here. Desalinization of the injection water was required for the whole operation; i.e. during pre flush, silicate injection and post flush. In addition, KCl was added to the pre and post flush for preventing clay swelling and for achieving ion exchange with the divalent ions on the rock surface. Moreover, HCl was added to the silicate slug for pH activation; i.e. more temperature sensitive (faster) system. The pilot area is shown in Figure 1. It consists of an isolated segment in the Southern part of the Snorre field. The segment has one active injector and one active producer separated by a distance of about 2 km. There is also a second producer in the segment which only produced for about a year several years before the two other wells were drilled. The relevant formations in and around the thief zone consist of well-connected channel sands, so there are several high permeable sands surrounding the even more high permeable thief zone. Moreover, it is difficult from the well logs to identify the exact location of the thief zone. Hence, it is a significant uncertainty in the thief zone geometry and properties. The pilot area is described in more detail in Skrettingland et al. (2014). The uncertainty in the geometry and extent of the thief zone was captured in the modeling by establishing two different grid scenarios; See Figure 2. We will refer to these two scenarios as the narrow and the wider thief zones.

Figure 1 The pilot area is considered isolated and is located in the Southern part of the Snorre field. The area consists of two active wells separated by a distance of about 2 km. There is a third well in the Northern part, but this producer was open for just a year several years before the two other wells were drilled.


Figure 2 The two thief zone scenarios used for the modelling. The sub figures display a lateral cross section of the thief zone, and the high permeable region is the blue area. We will refer to these thief zones as narrow and wider – To the left and right, respectively. For modeling the effect of the injected silicate we have relied on two different approaches, both aiming at introducing flow restrictions into the reservoir model dynamically. The first approach is introducing the flow restrictions by traditional multiplier boxes into the simulations; See Figure 3. The multiplier value, the time of introduction, and the extent (width and height) can be varied for each box. This approach is simple, flexible and data driven. The downside is that it is not directly linked to the mechanisms of the silicate. Therefore, the dynamic position of the silicate concentration, the temperature front and the seawater/salinity concentration are not taken directly into account. Moreover, because quite large multiplier boxes are used, the effect of the silicate will be introduced quite roughly in the simulations. Nevertheless, the simplicity and flexibility of the approach may make it easier to identify models that match the observed data. We will refer to this approach as the “Box” approach. For clarity of the presentation we have selected to focus on this approach when presenting the history matching results. The second approach is using a script with a feedback loop for forming the flow restrictions. We will therefore refer to this approach as the “Script” approach. The script is managing the simulation and is during simulation evaluating criteria for introducing flow restrictions for each grid cell; See Figure 3. The second approach is more in line with the assumed mechanisms for the silicate, and the restrictions are formed more gradually. Therefore, the criteria for forming flow restrictions are based on simulated temperature, silicate concentration, and salinity. Consequently, it may be easier to transfer knowledge gained for the matching parameters when predicting responses from silicate injection in new field segments. Another reason for using a second approach in the modeling is to gain additional understanding of the pilot, and getting a better understanding of the accuracy of the results from the Box approach. For performing the history matching of the pilot responses we started out with simulation models history matched until the pilot start. We used an ensemble based method for assisted history matching (Slotte and Smørgrav 2008). This is a Bayesian method taking the relevant uncertainties into account and conditioning multiple simulation scenarios jointly. During and after the pilot we have been monitoring several well responses which have been reported previously in Skrettingland et al. (2016). However, we have here included a few updated plots with new relevant well responses. In Figure 4, we present an updated plot for the tracers injected more than 6 months after the end of the silicate injection – At April 30th, 2014. Both a 25kg and a 125kg slug of two different tracers were injected simultaneously. The 125kg tracer was detected in the producer on April 4th, 2016. The tracer shows even more delay than the tracer injected at the end of the post flush in terms of displacement volume from the date of injection. That indicates more change in the flow pattern due to increasing flow restriction – More flow diversion. The 25kg tracer injected together with the 125kg tracer slug in April 2014 has not been observed. That may be because of additional dispersion due to the increased flow path. Altogether, the delay of the tracer injected in April 2014 is about 220% compared to the tracers injected before the pilot; See Figure 4. In Figure 5, we present an update plot for the observed water cut plotted against the displacement volume from the start of the silicate injection. The water cut is now slowly increasing after the decrease following the silicate injection.


In this work, the responses have been used both in the modeling and for the interpretation of the pilot. Response data up until September 15th, 2015 have been used for conditioning. However, some of the figures include observations beyond that date. For the modeling, we have focused on well pressures and water cut. Moreover, we have focused qualitatively on produced tracer concentrations and repeated fall-off as well as step-rate tests.

Figure 3 Two modelling approaches. To the left is an example of different multiplier boxes distributed along the thief zone for the Box approach. The multiplier value, the time of introduction, the width and the height can be varied for these boxes. To the right is a synthetic illustrating example of grid cells with introduced gelling by the Script approach. The Script approach can also introduce precipitation to the grid cells.

Figure 4 Updated tracer production concentrations. The produced concentrations are plotted against displacement volume since the injection of each tracer. Left and right is the tracers injected in 25kg and 125kg slugs, respectively. The injection time for each tracer is indicated by text next to each curve. The tracer injected in April 2016 (more than 6 months after the silicate injection) has now broken through in the producer. This tracer shows a delay of about 100% compared to the 125kg tracer injected early in the pre flush. Moreover, the 25kg tracer injected early in the pre flush shows a delay of about 60% compared to the tracers injected before the pilot (only injected in 25kg slugs). Altogether, a delay of about 220% is observed compared to the tracers injected before the pilot.

Figure 5 Updated water cut response as green triangles plotted against displacement volume. The plot also includes the simulated water cut from the time when the pilot was sanctioned.


For evaluating the increased oil production volume (IOR-volume) for the pilot we performed simulations using both models history matched to the pilot observations as well as models for the case of not injecting any silicate. These simulations were performed using prediction mode. This require incorporation of realistic downtimes, and that the prediction simulations including silicate injection to some extent is matching the actual production data. However, it is not essential with a perfect match, because it is the delta between the cases that are of most interest. The fracturing injection pressure limit is included in the prediction setup, so the rate will be reduced if constrained by injection pressure. To assess the uncertainty in the volumes, we were using multiple history matched parameter sets. From the simulations we obtained sets of IOR-volume estimates for both the Box approach simulations and the Script approach simulations. For determining the IOR-volume we also performed a decline analysis based on the historic data. The oil cut was extrapolated from the pre pilot production data – to estimate oil cut development for the case of not performing the silicate injection. Because the silicate injection resulted in a decreased throughput, a realistic gradual increase of the liquid production rate had to be assumed for the case of not performing the silicate injection. We will here refer to the volume estimates based on the decline analysis as the “Analytic” approach. Modelling results We will here summarize the main results from the modeling performed after the pilot. For the sake of brevity, we will mainly present and discuss results for single simulations using the Box approach rather than the different ensembles of simulations. The results include simulations using both scenarios for the thief zone. Even though most of the results displayed are generated using the Box approach; the Script approach also matched the pilot response well and resulted in sound flow restrictions. When we sum up the most likely location of the flow restrictions, we have taken all the simulations matching the data well into account. Moreover, we will in the next section also use matching simulations for the different approaches and scenarios for assessing the increased oil production for the pilot. Figure 6 shows simulated and observed effective reservoir transmissibility between the injector and the producer. The reservoir transmissibility is calculated as the rate divided by the pressure drop between the wells, and can be related to the effective flow properties using Darcy’s law. Before the silicate injection the reservoir transmissibility is on a stable level, but after it drops to less than half the original level during a time period of approximately one year. This is a strong signal indicating that the silicate has reduced the effective flow properties significantly. Figure 6 also shows that the simulations are capturing the reduction in the transmissibility well, which simply is a consequence of honoring rates and pressures in the wells. Fall-off tests for the injector were performed both before and after the pilot (Skrettingland et al. 2016). Figure 7 shows both the observed fall-off responses and the results of detailed simulation of the fall-off periods. History matched flow restriction parameters for the Box approach were used in these simulations – Including both thief zone scenarios. The fall-off tests give a large change in the pressure behavior after about 20 hours, which indicates an increased flow restriction at a distance away from the injector. Moreover, the simulated and observed repeated fall-offs are quite in line. They are capturing both the pressure development of each test and the difference in the pressure development between the tests. The simulated and observed tracer responses for the tracer injected during the post-flush are shown in Figure 8. The figure shows that the delay in the post flush tracer is captured well even though the tracer responses were not included as conditioning data in the history matching. Figure 9 shows simulated and observed water cut in the producer for 13 of the best history matched cases using the narrow thief zone geometry. The overall water cut was matching well for the whole


production period, but at pilot start it was off by about 1 percent point. Therefore, we have focused on matching the change in the water cut rather than the exact level.

Figure 6 Observed and simulated reservoir transmissibility versus time. The black dots represent the reservoir transmissibility based on observations. Similarly, the green and the red dots represents reservoir transmissibility based on simulation with and without modelling silicate injection, respectively. Moreover, the start of the silicate injection is indicated by the black vertical line. The simulation is matching well the observed development of the reservoir transmissibility.

Figure 7 Observed and simulated fall-off tests performed before and after the silicate injection. For each simulated fall-off test two simulations are displayed – One for each thief zone scenario.

Figure 8 Produced tracer concentration vs. displacement volume for the tracer injected during the post flush. The red circles are the observed tracer responses – the first samples with detected tracer. The simulated tracer responses are plotted using the solid and dashed curves, representing with and without simulating the effect of the silicate, respectively. The green and the blue curves are for narrow and the wider thief zones, respectively. The history matched simulations, taking the effect of the silicate into account, are capturing the delay in the tracer response well.


Figure 9 Water cut in the producer simulated with the narrow thief zone. The blue curves are the 13 best simulations according to the objective function used. The black dots represent the observed water cut, while the red diamonds are the conditioning points included in the objective function. Finally, the green curve is the resulting simulation result by not modelling the effect of the silicate. For the water cut we have focused on not matching the exact level, but rather the change in water cut; i.e. differences between the conditioning points. Figure 10 sums up the permeability reduction for the history matched ensemble of simulations. The overall picture is that a moderate flow restriction is forming at an early stage relatively close to the injector. In addition, a much stronger flow restriction is formed later further away from the injector. The strong flow restriction is interpreted to be caused by gelling. Figure 11 shows the simulated temperature distributions for the two thief zone scenarios. The temperature gradient is caused by 11 years of cold sea water injection, and is not changing much during the duration of the pilot. For the pilot the sodium silicate system was designed to gel up at a temperature around the transition between green and yellow in the simulated temperature - corresponding to approximately 70°C. Hence, the expected location from the temperature simulations and the summarized flow restrictions in Figure 10 are quite in line. This was also the case for the flow restriction from the Box approach ensemble that is not linked directly to the simulated temperature. The early moderate flow restriction was unintended, so we will discuss it a bit more in detail below. The reservoir transmissibility also indicated the forming of a moderate flow restriction at an early stage – Already during the silicate injection. This was in particular identified to happen right after a break in the silicate injection of about a week for replacing and repairing injection pumps. A hypothesis for the cause of the early moderate flow restriction is introduction of saline water due to cross flow through the injector during the 1-week break. Figure 12 demonstrates how cross flow of saline water from deeper isolated good sands may happen at breaks during the operation. The pre flush will not have flushed the isolated good sand (no communication to the producer through these sands), so stagnant saline water may therefore cross flow through the well due to the pressure gradient and the good flow properties. Both the silicate injected right before and after a break are likely to be contacted by saline water causing accelerated gelling and potentially precipitation. In the flow simulations we have also observed cross flow through the injector at breaks during the operation. The cross flow hypothesis is also in line with that the early flow restriction is occurring a bit away from the well. The part of the silicate slug affected by the cross flowed water will reach a bit away from the well before the affected silicate volume gels up. Based on this it seems likely that unintended cross flow of saline water in fact was the reason for the establishment of an early moderate flow restriction. The Script approach used for the modeling was at the time being not able to model the salinity dependence of the gelling properly, so a hybrid approach including multiplier boxes was used to account for the cross flow effect. Nevertheless, for future operations cross flow of saline water during the operation should be mitigated. As mentioned earlier in this sub section, the results from the history matched ensemble indicate no flow restriction closest to the injector and a moderate flow restriction a bit further away. From repeated step-rate tests performed in time steps of 2-3 hours within a total duration time of 16-19 hours we see no IOR NORWAY 2017 – 19th European Symposium on Improved Oil Recovery 24-27 April 2017, Stavanger, Norway

change in slope/PI (Injectivity/Productivity Index) from before to after the pilot; See Figure 13. The depth of investigation from the step rate tests reflects a near wellbore measurement compared to the depth of investigation from 20 hr shut-in time of transient fall-off tests, at which a major flow restriction is observed; See Figure 7. The step-rate tests in Figure 13 show higher pressures after the silicate injection. This is mainly caused by the induced flow restriction. We have also performed detailed steprate test simulations using a history matched simulation; Also included in Figure 13. Despite the moderate flow restrictions relatively close to the well, we also do not observe any change in the near wellbore injectivity in the simulations. Moreover, simulated and observed step-rate tests are matching quite well, but the slope is slightly steeper for the simulations - Indicating a bit too low PI (Injectivity).

Figure 10 Interpreted flow restriction multipliers based on history matching. The strong permeability reduction indicated by the red circle is interpreted to be caused by gelling. Similarly, the moderate permeability reduction inside the green circle is interpreted to be mainly caused by the unintended cross-flow of saline water during the silicate injection period.

Figure 11 Simulated temperature distributions for the narrow and wider thief zone – left and right, respectively. The temperature gradient is the result of 11 years of cold water injection.

Figure 12 Illustration of cross/back flow. The figure shows a situation with isolated good sand below a thief zone. At shut-in for the injection well it is likely that the pressure P2 is high enough to cause flow from the isolated sand through the well and into the thief zone. The production well is producing from the upper sand interval.


Figure 13 Repeated step-rate tests for E-4 before and after the pilot. Both simulated and observed results are shown. For the simulation after the pilot a history matched realization is used with typical multiplier values for the multiplier boxes closest to the injector. For neither the observed nor the simulated step-rate tests we see any difference in the slope from before until after the pilot. Pilot result As mentioned previously, the remaining success criterion to evaluate is: Proven in-depth flow restriction and minor near wellbore damage. A flow restriction was proved by the well observations (Skrettingland et al. 2016). The modeling results reported in the previous section provide the required support for indepth placement and minor near wellbore damage for the flow restriction. Tracer and water-cut responses was also presented in Skrettingland et al. (2016). However, the updated tracer responses presented above shows an even larger delay for the tracer injected about 6 months after the end of the silicate injection – Altogether 220% delay measured by displacement volume compared to before the silicate injection. This gives additional support for a significant change to the flow pattern. In addition, the updated plot for the water cut provided above, substantiate the reduced water cut trend after the silicate injection. Based on the results reported in Skrettingland et al. (2016), the updated well responses provided herein, and the modelling results reported herein, we sum up the success criteria for the pilot specified in Skrettingland et al. (2014): x Successful large scale transportation, mixing and pumping of silicate - Confirmed x Proved in-depth flow restriction and minor near wellbore damage – Confirmed x Proved significant change in flow pattern – Confirmed x Conclusive IOR-response (reduced water cut) – Confirmed In addition, for claiming successful modeling of the pilot, we also have to be able to model the effect on the oil production. The approaches we have used for this purpose was described above. Below we will report the results and the analysis. As discussed in Skrettingland et al. (2016) the silicate injection resulted in reduced throughput because the injector was constrained by pressure due to the induced flow restriction. Hence, the reduction in water-cut would not necessarily result in increased oil production from the producer. The Snorre A platform, which the producer is connected to, is not limited by the total water processing capacity. If the water processing capacity had limited the production, the total oil production could have been increased from other wells due to the lower water production for the pilot producer. We will compare the IOR-volumes already at the end of 2019, because that is when the injection agreement for the injector ends (using subsea template for the neighbouring field). However, the Analytic approach had to be compared at the current time (March 2016), else additional extrapolations and assumptions had to be introduced. The resulting IOR-volume estimates using the three approaches are given in Tabel 1. The estimates were quite centred around zero, with largest volumes for the Box IOR NORWAY 2017 – 19th European Symposium on Improved Oil Recovery 24-27 April 2017, Stavanger, Norway

approach and smallest for the Analytic approach. The volumes estimated by the end of 2019 tend to be slightly larger than in 2016. The increasing trend may continue after 2019, but currently there are uncertainties related to the injection agreement for that period. Script approach Box approach Analytic approach

Incremental oil prod. by March 1st, 2016 Incremental oil prod. by December 31st, 2019 -20 000 - +10 000 Sm3 -20 000 - +25 000 Sm3 +14 000 - +20 000 Sm3 +25 000 - +51 000 Sm3 -20 000 - -2 000 Sm3 -

Tabel 1 Estimates of increased oil production for the pilot by two different times. The estimates are based on three different approaches. The analysis performed indicates a marginal positive net increased oil production from the pilot. A larger increase was anticipated before the pilot (Skrettingland et al. 2014). Though, we have a good understanding of why a smaller volume was obtained. The main reason is that the resulting effective flow restriction was too strong, leading to a reduction in the throughput. We have used simulation models calibrated to the pilot response for predicting the effect of a less strong flow restriction. By removing the early moderate flow restriction considered to be caused by unintended cross-flow during the operation, the simulations give about 100 000 Sm3 in increased oil production by the end of 2019. For future operations similar cross flows should be mitigated. Another upside identified by simulation is performing the operation earlier. Figure 14 shows that the simulations predict a much larger decrease in water cut if the pilot had been performed in 2006 or 2008 rather than 2013. The reason is simply that the areas around the thief zone is less drained at an earlier stage. Simulating the pilot in 2006 also resulted in about 100 000 Sm3 of increased oil production after the same amount of time as described above. Finally, for the case of performing the pilot in 2006 without the restriction induced by the unintended cross flow, the simulations indicated an increased oil production of about 200 000 Sm3.

Figure 14 Simulations indicate a larger effect on the water cut reduction by performing the pilot several years earlier. The dashed and the solid lines are simulated predictions for, respectively, not performing and performing silicate injection. The year of the simulated silicate injections are indicated for each set of simulated predictions. Finally, the red stars are the historic water cut. Conclusions The Snorre pilot on water diversion has been presented in a series of papers (Skrettingland et al. 2014; Skrettingland et al. 2016). In this paper we have summarized the numerical evaluation of the pilot and conclude on the technical performance. More specifically, we history matched the pilot using two different modelling approaches. We obtained a consistent and realistic picture of the location and strength of the induced flow restrictions. Determining the oil production response for the pilot is not straight forward, but based on our analysis we concluded with a marginal increased oil production. The flow restriction resulted in flow diversion leading to reduction in the water cut, but it was a bit too strong resulting in reduced throughput. Nevertheless, the modelling work has been successful, we gained a good understanding of the effect of the silicate injection performed, we have obtained a calibrated modelling approach for evaluating future water diversion candidates, several upsides and mitigating actions have been identified, and the new operational concept worked out well. The predefined success criteria for the field pilot was met. IOR NORWAY 2017 – 19th European Symposium on Improved Oil Recovery 24-27 April 2017, Stavanger, Norway

Acknowledgements The authors thank Statoil ASA and the Snorre Unit partners: Petoro AS, ExxonMobil Expl. & Prod. Norway AS, Idemitsu Petroleum Norge AS, DEA Norge AS and Point Recources AS for their permission to publish this work. The conclusions presented in this work are the opinions of the authors and may not reflect those of the license owners. We would like to thank Vilgeir Dalen for valuable discussions and mentoring during the performance of the work. We would also like to thank Jan-Boye Kristoffersen for the discussions and analysis of the fall-off tests. Finally, we would like to thank Arne Stavland for input and discussions on the sodium silicate system. References

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