Vazquez_1_2013_Produced Water Chemistry History Matching in the Janice Field SPE-164903-MS-P

8/10/2019 Vazquez_1_2013_Produced Water Chemistry History Matching in the Janice Field SPE-164903-MS-P

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Produced Water Chemistry History Matching in the Janice FieldO. Vazquez 1, C. Young 2, V. Demyanov 1, D. Arnold 1, A. Fisher 2, A. MacMillan 2, Mike Christie 1; 1Heriot-WattUniversity, 2Maersk Oil

Copyright 2013, Society of Petroleum Engineers

This paper was prepared for presentation at the EAGE Annual Conference & Exhibition incorporating SPE Europec held in London, United Kingdom, 1013 June 2013.

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 beenreviewed 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, itsofficers, 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 toreproduce 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.

AbstractProduced Water Chemistry data (PWC) is the main source of information to monitor scale precipitation in oil fieldoperations. Chloride concentration is used in order to evaluate the seawater fraction of the total produced water per producingwell and is included as an extra history matching constraint to revaluate a good conventionally history matched reservoirmodel for the Janice field. Generally PWC is not included in conventional history matching and this approach shows thevalue of considering the nature of the seawater injection front and the associated brine mixing between the distinctiveformation water and injected seawater.

Adding the extra constraint resulted in the re-conceptualization of the reservoir geology between a key injector and twoproducers. The transmissibility of a shale layer is locally modified within a range of geologically consistent values. Also, amajor lineament is identified which is interpreted as a NW-SE trending fault, whereby the zero transmissibility of asecondary shale in the Middle Fulmar is locally adjusted to allow cross-flow. Both uncertainties are consistent with thecomplex faulting known to exist in the region of the targeted wells. Other uncertainties that were carried forward to theassisted history matching phase included: water allocation to the major seawater injectors; thermal fracture orientation ofinjectors and the vertical and horizontal permeability ratio (kv/kh) of the Fulmar formation.

Finally, a Stochastic Particle Swarm Optimization (PSO) algorithm is used to generate an ensemble of history matched(HM) models using seawater fraction as an extra constraint in the misfit definition. Use of addition data in history matchinghas improved the original good history matched solution. Field Oil Production Rate is interpreted as improved over a keyperiod and although no obvious improvement was observed in Field Water Production Rate, Seawater fraction in a number ofwells was improved.

IntroductionScale precipitation is a major flow assurance problem where minerals precipitate and further nucleate on surfaces such asproduction tubing, reservoir pore or pore throats, perforation intervals and surface facilities. These deposits can inhibit wellinflow and outflow performance which may result in costly well interventions, downtime or ultimately abandonment. Thesampling of produced water chemistry and wellbore monitoring surveys can however aid oilfield scale detection and itsmanagement (Carbone et al., 1999).

One of the most common occurring oilfield scales is sulphate minerals, which form due to the mixing of formation water

(rich in cations such as Ba, Sr, Mg, Ca) and injected seawater rich in sulphate ions. Predicting the location of the front andhence sulphate mineral deposition is an intricate process; its prediction requires accurate modelling of the seawater andformation water mixing front and associated breakthrough time. The use of produced water chemistry, due to their cleardistinctive chemistries, has been used for seawater fraction determination in a number of techniques, such as reacting ionsmethod (Ishkov et al., 2009) and Multivariate Analysis (Scheck and Ross, 2008). In this particular study, Chloride ionconcentration is considered as it is one of the most common methods used in the oil industry.

Barium sulphate (barite) is relatively acid insoluble and is considered as one of the most challenging and expensive scalesto remove, where its precipitation is the result of fluid-fluid incompatibility. Pressure maintenance and secondary oil recoveryby the injection of seawater in to the reservoir is common in field development strategies and the interaction of equilibrated,Barium (Ba 2+) rich formation water, with the injected seawater rich in sulphate (SO 4

2-), can form barium sulphate scale(Puntervold and Austad, 2007). It is therefore favourable to predict and prevent sulphate scale by sulphate reduction prior toinjection or regular scale inhibition to prevent its occurrence rather than costly removal by well intervention or at worst,


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abandonment. The same is also true for the Janice field, where the formation water is rich in Calcium (Ca 2+), forming aninsoluble scale with the sulphate in the injected seawater.

Conventional history matching is a standard industry practice, whereby the adjustment of physical parameters, such as thepermeability and porosity of the geological model, is made in order to replicate the production field observations. Areasonably well matched model is a necessity for proficient reservoir management, as it is intuitive to draw confidence fromthe ability of the model to replicate the past, and, therefore, use it as an aid for decisions regarding the reservoir futureperformance, where associated facilities can be optimised. Bypassed or stranded oil can be targeted, and also water and gas

breakthrough time can be anticipated. This procedure of adjusting the model is generally carried out through the lifetime of areservoir as further data is gathered and a model continually updated to retain a match.It is well understood that due to the limited and spatially restricted confidence in data pertaining to geologically complex

reservoirs, significant uncertainty exists in any reservoir model. It is also well understood that there is no unique solution, dueto the fact that a number of different configurations of geological parameters can yield multiple well matched models, eachwith different forecasts of reservoir performance. It is therefore essential to quantify the uncertainty related to multiple wellhistory matched realisations (multiple local minima) of relatively geologically consistent models (Hajizadeh et al., 2010).

For the purpose of this study, particle swarm optimization (PSO) is utilised where it has been successfully shown to findwell history matched models of synthetic and real-life case-studies quickly (Mohamed et al., 2010a; Mohamed et al., 2010b),while retaining model diversity for the purpose of uncertainty quantification of forecasts. PSO is a stochastic samplingalgorithm which is not limited to integers and therefore has the advantage over classical genetic algorithms where it samplesthe complete range of variability (Arnold et al., 2012). A set of N particles, initially randomly generated and described bylaws of motion, solve the optimization problem by convergence towards the best solution an individual particle (pbest) hasseen from a population, and also the best solution from the best generation of particles (gbest). This avoids trapping in localminima such as in conventional gradient based algorithms (Kelley, 1999).

PWC is not conventionally included in history matching, although the amount of data used is crucial to improveconditioning of an ill-posed inverse problem. Therefore, integration of PWC data is seen as a unique opportunity to increasethe justification and confidence of the predictions based on HM models on a synthetic modified PUNQ-S3 case study. A pilotstudy in (Arnold et al., 2012) showed improvement in HM with PWC. The present paper extends further the methodologyapplied to a real field case.

Produced Water Chemistry (PWC)To include the PWC in the reservoir history matching exercise, the seawater fraction was calculated as a function of theChloride concentration. This is a common technique, where considering Cl - is a non-reacting ion, it is expected that theconcentration of Chloride follows a linear behaviour with respect to seawater fraction (Braden et al., 1993). The linearrelationship for the mixture of seawater and formation water was determined for Chloride concentrations between the116,950 mg/l and 19,700 mg/l endpoints, Janice formation water and seawater concentration, respectively. A schematic ofthe relationship between chloride concentration and therefore historical and simulated PWC is shown below in Figure 1.

History Match Assessment of the Original Reservoir ModelGenerally, conventional reservoir history matching considers parameters such as well gas rate, oil rate and bottom-holepressure. Other authors have proposed the inclusion of other observed data as extra constraints, such as time lapse seismicdata (Kazemi et al., 2011; Stephen et al., 2009) and PWC (Arnold et al., 2012; Huseby et al., 2005). Water is present insedimentary deposits where oil is found, which may dissolve minerals present in the formation, so formation waters havedistinguishable chemistry (Ishkov et al., 2009), which may be traceable. In this study, two main types of water areconsidered, formation (including connate and aquifer water, as normally aquifer and formation water is assumed to have thesame chemistry) and injection water (present in water-flooding as a secondary recovery mechanism).

PWC is the main source of information for the detection of scale precipitation in oil field operations, where it is commonpractice to analyse the composition of formation waters present in the reservoir and injected waters. One of the most commonscales occurring and one of the most difficult to treat is BaSO 4, which is formed when seawater rich is SO 4 ions mixes with

formation water, rich in Ba2+

ions. Below, an assessment of the history match exercise will be carried out. First, consideringsolely observed produced water, oil and gas. Then the history matched model will be assessed using PWC, which is used tocalculate seawater fraction.

Assessment without Produced Water Chemistry - ConventionalThe base case reservoir model is considered to conventionally history match against observed oil, water and gas productionrates. The resulting history matched model shows a field wide good match with respect to observed field oil production rate(Figure 2), field water production rate (Figure 3) and field gas production rate (Figure 4. A detailed assessment of this historymatched reservoir model was reviewed by the Operator.

Assessment with Produced Water Chemistry - Seawater FractionTo assess the value of adding PWC as an extra constraint, the observed and calculated seawater fraction is compared usingthe original reservoir model, which was conventionally history matched. Three producers in particular, Wells A, B and C,


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showed good matches to the total water cut, however they provided poor matches to the observed seawater fraction, shown inFigure 5, 6 and 7, respectively. The poor seawater fraction match in Well A has important implications, as the simulatedresults suggest that Well A is producing ~50% seawater, but based on the observed PWC it should mainly be formationwater. Although well B showed a reasonable match to the water cut, producing solely formation water, the observed seawaterfraction is above 80% (Figure 6). Finally, Well C did not capture the seawater breakthrough time. In conclusion, the originalreservoir model did not represent adequately the water (injected and formation water) flow paths to these producers.Therefore, a re-evaluation was required that formed the basis of geological uncertainty identification and subsequent new

parameterisation.

Reservoir Uncertainty Identification, Quantification and ParameterisationAfter further investigation, two different kinds of uncertainties were identified, namely geological and water allocation.Geological uncertainties consist of the sealing potential of two shale layers in the Middle Fulmar, thermal fracture orientationin some of the injectors and finally the vertical and horizontal permeability ratio. Water allocation uncertainty is largelyrelated to the water split between injectors. A uniform distribution was chosen between the ranges of possible values for eachuncertain parameter, since each value was interpreted as equally likely and finally, to allow model diversity. The ranges wereconstrained by geological or engineering evidence when available.

Geological Uncertainties

Middle Fulmar Shale Layer 1There is uncertainty with regards to the sealing potential of a shale layer in the eastern regions of the field (Figure 8).Considering Well B is completed above the sealing shale and the closest injector is structurally and stratigraphically lower,there exists no significant pressure differential to encourage vertical movement of injected seawater. In order to initiatecommunication the transmissibility of this sealing layer had to be adjusted. Although it did not significantly improve thecontribution of injected seawater to Well B, it had a positive effect on Well C, where an increased contribution from injectedsea water was observed during initial sensitivity runs.

Middle Fulmar Shale Layer 2This second shale layer is located close to Well B, and prevented vertical communication with its closest injector. Adjustingthe transmissibility provided a good communication between the surrounding injectors of Well B and C. After thisadjustment, there was no significant effect on the water and oil production rates. In addition, the pressure field also remainedrelatively unaltered away from this region, which was a necessity to retain good well matches at field scale.

Thermal Fracture Orientation, Length and Permeability

Due to the fact that injection is above fracture pressure, and the temperature contrast between the reservoir rock and injectedseawater, it is interpreted that induced fracture wings are present. Fracture length has been limited to data from analogueliterature and pressure fall-off data provided by the Operator. Fracture morphology may have a significant impact on theinjected water flow paths and associated well water production rates; therefore it is reasonable to consider the uncertainty inthe fracture orientation where the present day maximum stress direction is known to be variable in the region. Fractures areassumed to only propagate in the X and Y directions to retain consistency with the original reservoir grid.

Absolute Vertical permeability, Kv/KhBased on the facies dependent Kv/Kh values obtained from the Reservoir Field Development Review, Kv/Kh ranges wereapplied to the Upper Fulmar, Upper-Middle Fulmar, Lower-Middle Fulmar and Lower Fulmar, in order to keep a reasonablenumber of parameters.

Water Allocation Uncertainties and Parameterisation

Injector A/B water splitInjected water is pumped to the seabed via a riser to a manifold. Water distribution after the manifold to individual wellshowever is uncertain, which is largely due to a lack of direct injection well testing. Recent information gathered by theOperator suggested that Injector A may take a larger fraction of the total Injector A/B water split than is currently assumed.First considering that Injectors A and B are a significant distance apart, and second, that they provide the majority of pressuresupport through the fields history and finally that these injectors are completed through the majority of the geologicalformations, Injector A/B water split uncertainty could have a significant impact on the history matching results.

Injection Well Uptime and DowntimeFor completeness, the injection wells uptime, downtime and plug failure records were cross-checked with the originalreservoir model schedule, which was updated accordingly.


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PWC History Matching and Uncertainty Quantification

Misfit definitionChoice of the misfit definition is one of the crucial tasks in history matching. A conventional misfit definition commonlyused is the least squared norm:

Where W is the number of wells, V is the number of production variables and T is the number of time steps for each. Thismisfit definition is then used in the likelihood model for the posterior inference, see below,

L=exp(-M)

Use of such a likelihood model implies that the model errors are independently normally distributed. However, this maynot be the case and the choice of ijk in the denominator becomes vital. Generally speaking the corresponds to the level ofconfidence attached to every observation, in other words it describes how close the match is expected to approach theobservation. In this case the ijk does not correspond just to the measurement device error, but reflects the overall uncertainty

associated with the observation (e.g. due to averaging over a period of time, accumulation, allocation, calibration, etc.). Acommon practice is to choose a constant ijk throughout assuming they are independent and identically distributed. This is astatistically sound assumption. However, this is not always practical as it does not reflect larger uncertainty of higherobserved values. Another common approach is to set the value of ijk to be a fraction of the observed value (or in someproportion to it), which inevitably leads to propagation of the correlation from the reservoir response into the match errors.Therefore, a sensible guidance is to assess the value of ijk with the desirable match error, which can be obtained based onengineering judgment.

Also, it is important to take into account the correlation between the errors, which is generally done through a fullcovariance matrix. Autocorrelation or the error through the time steps occurs due to the imposed influence of the numericalsimulation and the periods of stationary model behaviour. Thus, correlation between the errors from one time step to anothermeans that the ijk are no longer independent and the impact of each single error should be mitigated by a weighting factor.The weighting factor mitigates the impact of the mismatch from multiple history observations with correlated errors.Statistical correlation analysis with an autocorrelations or variogram function provides a way to measure the correlation inthe errors. Figure 9 shows an absence of correlation in the errors for observations from one of the wells with thecorresponding variogram nugget behaviour. The errors for the observations from another well (see Figure 10) demonstrateperiodic correlations and its period can be measured on the variogram. Based on this information the weight for this series ofdata is chosen inversely proportionate to the number of points within the correlation range.

This procedure of computing the weights is performed for all matched variables across all the production wells. Itappeared that water production error is highly correlated in all the wells, while the tracer (i.e. seawater fraction) error iscorrelated only in some of the wells, leaving the error of the sparse pressure data uncorrelated. Factoring this information intothe misfit definition leads to mitigation of the influence of relatively large amounts of water production data in favour of thetracer water production, so the value of the latter becomes more important in history matching. This technique also allowsdecreasing the range of the operating misfit values from tens of thousands (for thousands of observed history data) tohundreds.

PWC History matchingWith the new updated parameterisation based on the information provided by the PWC and the misfit definition described

above, a new history matching exercise was performed including the PWC as a constraint. The same three producers, WellsA, B and C which showed a good match to the total water cut, provided poor matches to the observed seawater fraction. Theoverall match for Well A (Figure 11), is significantly improved as the observed seawater fraction and watercut is extremelywell matched. Well B is slightly better matched (Figure 12 and finally, Well C is slightly better matched, where the seawaterbreakthrough time is accurately captured (Figure 13).

Uncertainty QuantificationIn this section the uncertainty in oil and water production, including seawater, was predicted for the following threeproducers, Wells B, D and E. These three producers would be potentially actively producing in future years in the Janicefield. The uncertainty quantification is based on multiple model realizations, which provides an ensemble of good historymatched models, which then determine the uncertainty of the well fluid production. These calculations provide a Bayesianconfidence interval (P10-P50-P90) in time for oil production, which can be used to evaluate the value of the well. Then, thisinformation is combined with the water production and the seawater fraction predictions to evaluate the scale risk associated


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with potentially valuable wells. This information could in turn be used to design the best strategy for the field development infuture years of production, where the appropriate logistics for scale management could be put in place, before scale might bedeposited in these wells. This idea builds on the approach adopted in (Mackay et al., 2005), where an integrated risk analysisfor scale management was proposed during the Front End Engineering Design (FEED) stage.

Well B is predicted to be producing oil and water at a similar rate, which is around 500 bbl/day (Figure 14). The seawaterfraction uncertainty prediction is estimated to be around 15% and increasing, although this prediction seems to be a bit lowbased on the limited number of PWC history data points (Figure 15). However, based on these observations, it seems

reasonable to consider this well as a potential candidate for scale deposition, due to the likely increase in water productionand rising seawater fraction.Well D is predicted to be the biggest oil producer of the three with an oil rate of 500 bbl/day (Figure 16); but it is

predicted to produce around 4,000 bbls/day of water, with a predicted seawater fraction of above 40% (Figure 17). This wellis possibly the well of the three where most attention should be given, as it is the biggest oil producer, but also as it producesmore water, with a considerable predicted seawater fraction around 40%. The high volume of water and the high mixingpredicted (40-60 seawater-formation water) makes this well significantly prone for scale deposition, with the highestsaturation ratio (Vazquez et al., 2013).

Finally, Well E is predicted to produce around 200 bbls/day of oil with a very low water cut (Figure 18). This makes thewell valuable, but based on these observations, it seems that in terms of scale management this well should not be of anymajor concerns considering that the predicted produced water remains entirely sourced from the formation (Figure 19).

ConclusionsPWC (Produced Water Chemistry) has been integrated into the history matching process for the Janice filed, which yieldsnew information regarding the nature of fluid flow and brine mixing that may otherwise remain unrepresented in the reservoirmodel. A well matched reservoir model provided by the Operator was assessed using the seawater fraction, which wascalculated using the observed produced water composition. Based on the results of the assessment, a re-evaluation wasrequired that formed the basis of geological uncertainty identification and subsequent new parameterisation. Two differentkinds of uncertainties were identified, namely geological, including the sealing potential of two shale layers in the MiddleFulmar, thermal fracture orientation, and water allocation between injectors. This new reservoir parameterisation provided aslightly better overall history match based on these parameters, but also provided a methodology to generate informationabout the nature of fluid flow and seawater and formation brine mixing.

Finally, the uncertainty in oil and water production, including seawater fraction was estimated. The uncertainty wascalculated using an ensemble of good HM reservoir models using the updated reservoir parameterisation, which provides aBayesian confidence interval (P10-P50-P90) for the production predictions. The estimated oil production was combined withthe water production and associated seawater fraction to identify valuable wells (high oil producers) which might be underscale deposition risk should untreated seawater breakthrough. Scale risk is calculated using the water production and thecorresponding seawater fraction, where the worst scaling conditions generally occur when the seawater fraction is above30%. This information provides very valuable information to design the best strategy for the field development, where a goodscale management strategy can be adopted.

AcknowledgementsThe authors would like to thank the management of Maersk Oil for permission to publish this paper. We would also like tothank Epistemy Ltd for supplying the RAVEN software for history matching and uncertainty quantification.

ReferencesArnold, D., Vazquez, O., Demyanov, V., and Christie, M.A., 2012. Use of Water Chemistry Data in History Matching of a Reservoir

Model. SPE 154471, presented at the EAGE Annual Conference & Exhibition in Copenhagen, 4-7 June Denmark.Braden, J.C., and McLelland, W.G., 1993. Produced Water Chemistry Points to Damage Mechanisms Associated With Seawater Injection.

SPE 26045.Carbone, L.C., Fleming, N., Spark, S., and Patey, I.,1999. Scale Management Through Laboratory Analysis and Wellbore Monitoring

Surveys. SPE 54733.Hajizadeh, Y., Christie., M., Demyanov, V., 2010. Comparative Study of Novel Poulation-Based Optimization Algorithms for History

Matching and Uncertainty Quantification: PUNQ-S3 Revisited. Prepared for the Abu Dhabi International Petroleum Exhibition andConference, 1-4 November.

Huseby, O. Chatzichristos, C., Sagen, J., Muller, J., Kleven, R., Bennett, B., Larter, S., Stubos, A.K., Adler, P.M., 2005. Use of naturalgeochemical tracers to improve reservoir simulation models, Journal of Petroleum Science and Engineering, Volume 48, Issues 34,Pages 241-253.

Ishkov, O., Mackay, E., Sorbie, K., 2009. Reacting Ions Method to Identify Injected Water Fraction in Produced Brine. SPE 121701.Kazemi, A., Stephen, K. D., Shams, A., 2011. Seismic History Matching of Nelson Using Time-Lapse Seismic Data: An Investigation of

4D Signature Normalization. SPE Reservoir Evaluation & Engineering Vol 14, Num 5, pp. 621-633.Kelley, C.T., 1999. Iterative methods for Optimization, Society for Industrial and Applied Mathematics.Mackay, E.J., Jordan, M.M., Feasey, N.D., Shah, D., Kumar, P., Ali, S.A., 2005. Integrated Risk Analysis for Scale Management in

Deepwater Developments. SPE 94052.


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Mohamed, L., Christie, M., Demyanov, V., 2010a. Comparison of Stochastic Sampling Algorithms for Uncertainty Quantification.SPE119139.

Mohamed, L., Christie, M., Demyanov, V., 2010b. Reservoir Model History Matching with Particle Swarms: Variants Study. SPE129152.Puntervold, T., and Austad, T., 2007. Injection of Seawater and Mixtures with Produced Water Into North Sea Chalk Formation: Impact on

Wettability, Scale Formation, and Rock Mechanics Caused By Fluid-Rock Interaction. SPE 111237.Scheck, M., Ross, G., 2008, Improvement of Scale Management Using Analytical and Statistical Tools, SPE 114103, Society of Petroleum

Engineers Inc.Stephen, K. D., Shams, A., MacBeth, C., 2009. Faster Seismic History Matching in a United Kingdom Continental Shelf Reservoir. SPE

Reservoir Evaluation & Engineering, Vol 12, Num. 4, pp. 586-594.Vazquez, O., Young, C., Demyanov, V., Arnold, D., Fisher, A., MacMillan, A., Christie, M., 2013. Estimating Scale Deposition through

Reservoir History Matching in the Janice Field. SPE 164112

Figure 1: Schematic of the relationship between chloride concentration, simulated and historical tracer concentration

Figure 2: Field Oil Production Rate vs. Time for the Original Model and the History observed data.

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Figure 3: Field Water Production Rate vs. Time for the Original Model and the History Observed data.

Figure 4: Field Gas Production Rate vs. Time for the Original Model and the History observed data.

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Figure 5: Well A match using the Base Case reservoir model. WWCT: Simulated Well Water Cut, vs. WWCTH; Observed Well WaterCut History, Calculated Seawater Fraction vs. Observed Seawater fraction.

Figure 6: Well B match using the Base Case reservoir model. WWCT: Simulated Well Water Cut, vs. WWCTH; Observed Well WaterCut History, Calculated Seawater Fraction vs. Observed Seawater fraction.

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Figure 7: Well C match using the Base Case reservoir model. WWCT: Simulated Well Water Cut, vs. WWCTH; Observed Well WaterCut History, Calculated Seawater Fraction vs. Observed Seawater fraction.

Figure 8: Looking north, the position of Well A and Well B is shown relative to the Middle Fulmar sealing shale 1. The nearestinjector is located down dip to the south east, structurally lower than the shale.

a) b)Figure 9: (a) Error based on the initial HM and the production observations from a Janice well, (b) variogram of the errors showsabsence of temporal correlation.

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a) b)Figure 10: (a) Error based on the initial HM and the production observations from a Janice well, (b) variogram of the errors shows aperiodic temporal correlation with a period 350-450 lag units.

Figure 11: Well A best match PWC History Matching. WWCT: Simulated Well Water Cut, vs. WWCTH; Observed Well Water CutHistory, Calculated Seawater Fraction vs. Observed Seawater fraction.

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Figure 12: Well B best match PWC History Matching. WWCT: Simulated Well Water Cut, vs. WWCTH; Observed Well Water CutHistory, Calculated Seawater Fraction vs. Observed Seawater fraction.

Figure 13: Well C best match PWC History Matching. WWCT: Simulated Well Water Cut, vs. WWCTH; Observed Well Water CutHistory, Calculated Seawater Fraction vs. Observed Seawater fraction.

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Figure 14: Well B Uncertainty Prediction: Left Oil Production Rate; Right, Water Production Rate.

Figure 15: Well B Seawater Fraction Uncertainty Prediction.


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Figure 16: Well D uncertainty prediction: Left Oil Production Rate; Right, Water Production Rate.

Figure 17: Well D Seawater Fraction Uncertainty Prediction.


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Figure 18: Well E uncertainty prediction: Left Oil Production Rate; Right, Water Production Rate.

Figure 19: Well E Seawater Fraction Uncertainty Prediction.

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Vazquez_1_2013_Produced Water Chemistry History Matching in the Janice Field SPE-164903-MS-P