00075148 Geologically Based Screening Criteria for Improved Oil Recovery Projects Richard Hansen...

8/11/2019 00075148 Geologically Based Screening Criteria for Improved Oil Recovery Projects Richard Hansen 2003

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Copyright 2002, Society of Petroleum Engineers Inc.

This paper was prepared for presentation at the SPE/DOE Improved Oil Recovery Symposiumheld in Tulsa, Oklahoma U.S.A., 1317 April 2002.

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AbstractThe choice of an Improved Oil Recovery (IOR) process foruse in a particular reservoir depends on several factors; thehabitat of residual oil, the properties of the reservoir fluids,reservoir conditions and reservoir heterogeneity.

Reservoir heterogeneity exists at all scales, from the micro

to the mega-scopic. Previous workers have studied the effectsof micro and meso-scale heterogeneities on IOR processes indetail, as many processes are designed to act at those scales,

but have ignored macro-scale heterogeneities such as faciesvariations. These can have a large effect on an IOR process;controlling the magnitude and nature of the connectivitybetween wells, compartmentalising the reservoir and

influencing the balance of capillary, viscous andgravity forces.

A database of 499 IOR projects in clastic reservoirs wascollated. The macro-scale heterogeneity present in eachreservoir was categorised by depositional environment usingthe Tyler and Finley Heterogeneity Matrix. The results show

that successful IOR projects using a particular process clusterat certain combinations of lateral and vertical heterogeneity.

To investigate the distributions, a quantitative method of

evaluating macro-scale heterogeneity was devised. TheseLateral and Vertical Heterogeneity Indices (LHI and VHI)provide a simple method of summarising and communicating

geological information between different peopleand disciplines.

Reservoirs with known levels of LHI and VHI weremodelled, in which various IOR processes were simulated.Over 350 simulations of steam, polymer and WaterAlternating Gas (WAG) injection processes were run and used

to identify the processes that worked best under differentlevels of heterogeneity, dip and net to gross. The results

showed that the Heterogeneity Indices can be used to predictthe effect of macro-scale reservoir heterogeneity on these threeprocesses and that objective, geologically based screeningcriteria could be derived. Using these criteria, it is

demonstrated that in the high cost and low well densityenvironment of the North Sea, WAG injection is the mostviable IOR process, as the efficiency of the process isrelatively unaffected by macro-scale heterogeneity.

IntroductionAn oil reservoir is a complex system of interconnecting porespaces filled with a two or three phase fluid. This complexity,coupled with the depletion of the natural drive mechanism,often means that only a percentage (10 to 60%) of the total oilin place can be produced by primary and secondary recovery.

Improved Oil Recovery (IOR) processes have been

developed to increase this proportion. The choice of IORprocess for use in a particular field depends on the habitat ofresidual oil, fluid properties, reservoir conditions andreservoir heterogeneity.

Reservoir Heterogeneity exists at all levels from the micro-

scopic to the mega-scopic1, illustrated in Fig.1.i.Micro-scale heterogeneity in properties such as

permeability, porosity and capillary pressure control the oil

storage potential, fluid flow rates and residual oil (metres)ii. Meso-scale heterogeneity is a function of

sedimentary structures, ripples, cross bedding (cm to m).iii. Macro-scale heterogeneity is created by the

arrangement of individual sand and shale bodies within the

reservoir. This architecture defines the direction of fluid flowbetween wells, determines how a reservoir drains and where

hydrocarbons remain unrecovered. (1m to 100s of metres)iv. Mega-scale heterogeneity is a product of the

juxtaposition of major depositional elements, differentdepositional environments or large-scale faultcompartmentalisation, creating traps and reservoirs.(>1000 metres)

Micro and meso-scale heterogeneities have been studied indetail by previous workers in the field of IOR because manyIOR processes are designed to act on those scales. Whilepapers such as Taber et al.2 provide very useful screeningcriteria based on porosity, permeability and reservoir fluidparameters, little data is presented on the impact of larger

scale geological heterogeneity.

SPE 75148

Geologically Based Screening Criteria for Improved Oil Recovery ProjectsRichard Henson, Schlumberger DCS, Adrian Todd and Patrick Corbett, Heriot-Watt University.


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2 R. HENSON, A. TODD AND P. CORBETT SPE 75148

Tyler and Finley3 carried out a review of the reservoirheterogeneity and associated predicated recovery from 450Texan sandstone reservoirs. They showed that there was a

well-defined relationship between reservoir architecture andconventional recovery efficiency. As the complexity of the

architecture increased, the final recovery from a fielddecreased. Recovery ranged from a high of 80% of original oil

in place (OOIP) in a low heterogeneity, wave-dominateddeltaic reservoir to a low of 8% of OOIP in a mud rich

submarine fan with high heterogeneity.They found that as macro-scale heterogeneity in clastic

reservoirs is often a product of depositional environment, it ispredictable and can be characterised in terms of high,moderate or low, vertical and lateral heterogeneity. Fig.2shows the heterogeneity matrix for clastic depositional

environments developed by Tyler and Finley.Carbonate reservoirs are more problematic as the

distribution of reservoir properties is often controlled bydiagenetic changes that have occurred since deposition ratherthen the depositional environment itself.

Clastic Reservoir IOR Project Database A databaseof 500 IOR projects in clastic reservoirs from 50 countries hasbeen compiled to study the effects of different reservoirparameters and geological heterogeneity. Data was drawnpredominatly from the Oil and Gas Journal Bi-annualEnhanced Oil Recovery (EOR) Survey4-8, but also from the

available literature and a survey of the petroleum industry.The projects were evaluated in terms of reservoir and fluid

characteristics and the degree of success encountered. Allprojects have been operating during the last 12 years and haveresulted in increased production and made a profit. Manyprojects, particularly the thermal projects in America and

Canada, have been running successfully for ten or more years.The database also includes 49 projects that have failed,

projects where enhanced production has been minimal or zeroand that have run at a loss.

The data was analysed using cross plots and simplestatistics to identify the oil compositions, reservoir conditions

and levels of heterogeneity at which a particular IORtechnique was a success.

The classification of the macro-scale heterogeneity presentin a reservoir was made using the Tyler and Finley Matrix.The location of projects within the matrix is somewhatsubjective as the full details of the geology are rarely

described in the literature. Based on the description of itsgeological environment a project can be assigned a high,moderate or low value for vertical and lateral heterogeneityand placed in the appropriate box.

IOR Success and Reservoir Heterogeneity IORprojects from the database were plotted in the heterogeneitymatrix. There is no scale as such, just nine categories intowhich a project can be placed, though a project can be placedon the margin between two boxes if they fall between the two

categories. Fig.3shows successful projects plotted by type anddepositional environment.

Despite the subjective nature of the plot, it can be seen that

certain types of project tend to group in specific areas of thediagram, highlighted in Fig.4. Thermal projects seem to be

almost universally successful, working at most levels ofheterogeneity except for medium to high lateral heterogeneity

when coupled with high vertical heterogeneity. Few projectsof whatever type work in the low lateral heterogeneity and

high vertical heterogeneity box in the top right of the diagram.CO2 floods show a bi-modal distribution, clustered in the

low to medium lateral and low vertical heterogeneity and thehigh lateral and moderate to high vertical heterogeneity. WAGfloods group in the middle of the matrix, successful when areservoir has moderate lateral and vertical heterogeneity.

Polymer floods are concentrated in the upper left sectionsof the matrix; they are only successful if both heterogeneitiesare low to moderate.

Hydrocarbon gas floods work best when the verticalheterogeneity is moderate at all levels of lateral heterogeneity

as well as working when both heterogeneities are low.The matrix classifying failed projects (Fig.5) shows that

while there are no successes in the low vertical and highlateral heterogeneity box as there are also no failures. Thisseems to indicate that these reservoir types are recognised asbad candidates for IOR and projects are not attempted in suchreservoirs. There are also very few failed projects in the lowlateral and vertical heterogeneity box but successes, this could

indicate that heterogeneity has minimal effects at this level.This distribution of successful IOR projects over

substantial areas of the heterogeneity matrix contrasts sharplywith predictions of Tyler and Finley (Fig.6). They predictedthat successful IOR projects would be restricted to the low

Lateral and Vertical Heterogeneity box. They do makesuggestions for ways to increase oil recovery for other areas ofthe matrix but these are methods such as infill drilling, gelplacing and well recompletions.

By plotting successful and failed IOR process on theHeterogeneity Matrix in this way it is shown thatheterogeneity has an effect on the success or failure of an IOR

project and this effect is dependant on the type of IOR beingattempted. This allows the effect of heterogeneity on an IOR

processes to be predicted and some limited screening ofpotential IOR candidates to be carried out.

However. plotting a reservoir on the Heterogeneity Matrixis largely subjective and is dependant on the published field

description presented by the operator. As it also does not takeinto account the well spacing with which the reservoir has

been developed, a more objective way to investigate, quantifyand predict the effects of macro scale heterogeneity on theperformance of an IOR process was needed.

Heterogeneity was split into two mathematically defined,

vertical and lateral components or Indices. These were adaptedto take account of the effect of deviated and horizontal wells

on the apparent heterogeneity in a reservoir.


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SPE 75148 GEOLOGICALLY BASED SCREENING CRITERIA FOR IMPROVED OIL RECOVERY PROJECTS 3

Synthetic reservoirs with known levels of Lateral andVertical Heterogeneity Index were generated using object-based stochastic modelling. These models were based on an

'average' North Sea reservoir (1 Km well spacing, 100 m grosspay, 75% Net to Gross) and covered the range of macro-scale

heterogeneity encountered in the UKCS.Three IOR processes were investigated further; Steam

Drive, Polymer Flood and WAG. Compositional Simulationsmodelling the effects of these IOR processes on a reservoir

and the reservoir fluids were developed.A total of 349 different IOR simulations under a variety of

conditions were run. The results were used to identifyprocesses that worked best under certain levels ofheterogeneity, dip and net to gross. The results of thesimulations were summarised to create objective,

geologically-based screening criteria for the three IORprocesses, that can be used to identify potential candidate

reservoirs suitable for the application of IOR.

Quantifying Heterogeneity

As classification of heterogeneity by depositional environmentin the Tyler and Finley Matrix is highly subjective, a method

that allows a more objective assessment of the level ofheterogeneity present in a reservoir has several advantages;

- it enables simple comparisons betweendisparate reservoirs.

- reservoirs that consist of several intervals withdifferent levels of heterogeneity (such as the Brent sequence in

the North Sea) can be plotted on the diagram using the averageheterogeneity throughout the pay zone, or each interval couldbe characterised individually.

- heterogeneity variation within the same depositionalenvironment could be identified.

- it enables synthetic reservoirs to be generated forany point in the heterogeneity matrix.

As in the Tyler and Finley Matrix only two dimensions areconsidered, lateral and horizontal heterogeneity. The simplestmodel on which to base a scale is to consider the heterogeneitybetween two adjacent wells; an injector and producer pair(Fig.7). The reservoir between the wells is a two-lithology

system; a background shale in which sand bodies arerandomly placed. These sand bodies or genetic units

(channels, crevasse splays, dunes, etc) can be defined in twodimensions very simply.

Three other assumptions are made;- the wells are vertical or near vertical over the

reservoir interval considered, with only a small variation in thedistance between them.

- the reservoir is developed in such a way that flowbetween the wells is along, or close to the long axis of thesand bodies.

- there is only a small variation in the trend (10 o

spread) of the deposited sand bodies in the reservoir interval tobe considered.

Lateral Heterogeneity Index (LHI) If the aboveassumptions are made, the degree of lateral heterogeneity

between two wells depends on the correlation of sand bodiesor stacks of bodies between the wells; the more sand bodiesthat correlate, the lower the heterogeneity. The importantfactor therefore, is the ratio between the mean sand bodylength and the inter-well distance or well spacing. This is

expressed in the equation below, with the addition of a logfunction to give results that are easily scaleable. The minussign is included to give positive heterogeneity indexes thatincrease with increasing heterogeneity.

(LHI) = - log Genetic Unit Mean LengthInter-Well Distance

(1)

This equation generates a dimensionless index, allowingthe comparison of reservoirs with very different sand bodysizes, distributions and development strategies. When themean genetic unit length is equal to the inter well distance, a

LHI of zero is generated. Means greater than the inter welldistance result in a negative index. The equation alsodemonstrates that the heterogeneity present in a reservoir isnot fixed but relative and can be increased or decreased byvarying the well spacing in a reservoir.

Vertical Heterogeneity Index (VHI) The amount ofvertical heterogeneity is controlled by the superposition ofsand bodies in the space between wells. The thicker the sandbody compared to the gross thickness of the reservoir, the

more likely these interceptions are. A 10 m mean thickness forsand bodies within the reservoir is going to lead to a higher

heterogeneity in a thick reservoir than in a very thin reservoir.This can be expressed in the equation;

VHI = - log Genetic Unit Mean Thickness

Gross Pay Thickness(2)

Again, the log function and minus sign are included to givea positive Index that increases as heterogeneity increases. AHeterogeneity Index of zero is calculated when the mean

genetic unit thickness is equal to the gross reservoir thickness,as in a single genetic unit reservoir.

Genetic Units A genetic unit is a body that can be relatedto an individual depositional event or series of events and can

be constrained both geometrically and spatially9. For the

purposes of geological modelling a genetic unit can be definedwith only a few parameters; shape, length, width, thickness,orientation, porosity distribution and permeability distribution.

The scale, geometry and orientation of genetic units arecritical in appraising the heterogeneity, connectivity and net togross of a reservoir. A well in a clastic reservoir may

encounter hydrocarbons in a series of stacked sand bodies.Althpugh seismic data can usually give a reliable estimate of


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the size of the reservoir and the larger structural elements, howextensive will the sand bodies be? Will they be field wide orwill they cover only part of the reservoir? What chance is

there of other sands of limited lateral extent being present butnot having been contacted by the well?

There is a large amount of literature on genetic unitdimensions in a variety of depositional environments, such as

paralic (ocean margin) sandstones10 and fluvialenvironments11. Where good correlations between genetic unit

dimensions exist, they allow the width and length of a geneticunit to be predicted from the measured thickness from core or

wireline logs. Where correlations are poor, the likely ranges oflength and width can still be estimated.

Horizontal Wells For the purposes of calculating theheterogeneity between wells, a horizontal well is any well witha deviation of over 45

o from the vertical, where the flow

between them can be considered to be more vertical thanhorizontal. Therefore, the controlling factor for the VHI willbe the vertical inter-well distance rather than the reservoir

thickness, while the LHI will be a function of the length of thecompleted well interval in the reservoir (Fig.8).

To calculate the heterogeneity between two wells wherethe flow between them is vertical, modifications of equations(1) and (2) are needed;

LHI = - log Genetic Unit Mean Length

Completed Well Interval(3)

and;

VHI = - log Genetic Unit Mean Thickness

Vertical Inter-Well Distance(4)

Again, the result is dimensionless with the log and theminus sign included to give an easily scaleable result thatincreases with increasing heterogeneity.

Generation of Synthetic ReservoirsObject-based stochastic modelling provides the tools to createreservoirs of known heterogeneity based on the HeterogeneityIndices described above. In this process, geologically-definedobjects are randomly placed in a background facies until a set

limit, defined as a percentage of the total reservoir volume, isreached. The dimensions and trend of each individual objectare randomly generated from frequency or fuzzy possibilitydistributions defined by the user.

The reservoirs to be modelled were simple sand shalemodels, with shale as the background facies. It was decided

that the range of heterogeneity that could be found in anaverage North Sea reservoir should be investigated. Todetermine these parameters, data from 78 North Seareservoirs, taken from the 25th United Kingdom Oil and GasFields Commemorative Volume12was analysed.

It was determined that an average North Sea reservoirhas a 1 km well spacing in 100m of gross pay with a net togross of 75%. Therefore, the synthetic reservoirs are two-

dimensional rectangles, 1000m long by 100 m thick, with aninjector at one edge and producer at the other. In a reservoir of

these dimensions Lateral and Vertical Heterogeneity Indicesranging from zero to 2 would result from sand bodies ranging

1 m thick and 10m long to 100m thick and 1000m long.Five values of both LHI and VHI were selected (0.10,

0.55, 1.00, 1.45 and 1.90) to create 25 reservoirs that providedgood cover across the parameter space. The models created are

shown in Fig.9.While the value of mean thicknesses and lengths are

known, to create a realistic reservoir a range of sand bodies ofdifferent dimensions, either side of the mean, is needed. In real

field examples they are generated from probabilitydistributions of thickness and lengths identified from wellbore

and analogue data. While it would be possible to arbitrarilycreate a probability distribution around the mean of each of thefive values of both LHI and VHI, this would not fully capture

the uncertainty involved. Instead, Triangular Fuzzy Numbers(TFN) were used.

For each of the values of both LHI and VHI, a TFN wasdefined, using the mean thicknesses or lengths as the mostlikely value and a spread of 0.3 to either side of the mostlikely value, as the least likely values (Table 1).

Reservoir SimulationThree IOR processes were simulated;

- Steam Drive- Polymer Flooding- Hydrocarbon Gas WAG Injection

Thermal IOR is the second most successful IOR process,

both in terms of the number of active projects and totalproduction. Steam drive is the most used thermal process andso has a large number of projects classified in the Clastic IORProject Database, which can be compared with the results ofthe simulations.

Twenty years ago, polymer flooding was widely used butoften failed to meet the expectations generated from single

well trials when it was applied at a larger scale. Several of thepolymer projects documented in the Clastic IOR Project

Database were said to have failed due to reservoirheterogeneity, but without further discussion of the nature ofthe heterogeneity or how it resulted in the projects failure.

Hydrocarbon WAG injection is under-represented in the

Clastic Reservoir IOR Database, with only eight successfulprojects identified. However recovery seems to be good and

there are no reported failures. The process is currently beingused in several North Sea reservoirs and is being consideredfor use in several others. It is therefore a process that isconsidered to be applicable in offshore areas with large

well spacings.The simulation models are all 1km long by 10m wide and

100m thick. The permeability of the sand blocks was set at aconstant 1000 mD, with a porosity of 30%. The shale blocks


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had a permeability of 0.001 mD with 0.001% porosity,preventing any significant storage or flow of reservoir fluidwithin them. The distribution of sand and shale is dependent

on the geological model used, which in turn is dependent onthe level of LHI and VHI. An injector was placed at the left

margin of the model and a producer at the right, bothcompleted throughout the whole reservoir interval.

Different simulation models were created for each IORprocess, as the reservoir conditions need to be optimised for

that particular process. The effectiveness of a project is,therefore, only dependant on the LHI and VHI values. The

initial conditions and oil properties for all three models aresummarised in Table.2.

The steam injection model is based on Problem 3a in"Fourth SPE Comparative Solution Project - A Comparison of

Steam Injection Simulators"13

. The polymer model is based onan amalgamation of three micellar polymer field tests thathave been reported in the literature

14-16.

Results

Two measures are used to compare and contrast theeffectiveness of the IOR processes in reservoirs with

different heterogeneities;- Recovery is the oil produced at surface conditions

divided by the total stock tank oil in place. This productionindex, like the Heterogeneity Indices themselves,is dimensionless.

-Efficiencyis calculated as the ratio of the volume of

oil produced by the volume of fluid injected, both at surfaceconditions. This equation results in a dimensionless figure thattakes into account both the volume of oil produced and howefficiently it was produced. This can easily been converted togive an indication of how profitable the IOR process will be

by including the operating costs of the IOR process used andthe current or predicted sale value of the produced oil.

Stream Drive Figs.10 and 11 show the recovery andefficiency for a steam drive in the 75% net to gross reservoirsplotted as surfaces against the Lateral and Vertical

Heterogeneity Indices. As there is no connectivity between theinjector and producer pair in the 75% net to gross reservoirs athigh LHI (above 0.8) and low VHI (below 0.3), flow betweenthe wells is impossible in these reservoirs and the surfaceshave been truncated to reflect the lack of data in this region ofthe matrix.

Fig.10shows that steam drives produce a good recovery inreservoirs with low heterogeneity (LHIs and VHIs below1.2), peaking at 51.1 % in the LHI: 0.10/VHI: 0.10 reservoir.At higher heterogeneities recovery is smaller, dropping to15.8% in the LHI: 1.90/VHI: 1.45 reservoir.

The most efficient steam drive simulations appear in a

relatively small area of the LHI and VHI Matrix, below 1.2 forboth Indices. When successful thermal projects from theClastic IOR Project Database were plotted on a Tyler andFinley Heterogeneity Matrix, they could be found at almost alllevels of heterogeneity. The two different results can be

reconciled if we take into account the limitations of the Tylerand Finley Matrix. The matrix does not take into account theeffect of well spacing on the heterogeneity between wells nor

does it represent the range of heterogeneity present in anaverage North Sea reservoir. In this case we would expect that

the screening criteria derived from the Tyler and Finley Matrixwould not be valid at high levels of Heterogeneity Index in a

North Sea reservoir.This is demonstrated in Fig.12, where the efficiency of the

simulated steam drives is compared with the pattern ofsuccessful and failed thermal projects plotted on a Tyler and

Finley Heterogeneity Matrix. Contours showing the variationin the efficiency of simulated steam drives, with values of LHIand VHI below 1.2, are superimposed on a Tyler and FinleyHeterogeneity Matrix. The matrix shows the levels of

heterogeneity present in successful thermal projects (plottedas triangles).

There is a good correlation between boxes of the matrixwith large numbers of successful thermal projects and highlevels of simulated steam drive efficiency. This demonstrates

that the level of heterogeneity in a reservoir, characterisedusing the Indices, and the results of simulated IOR processesin that reservoir are consistent with observed IOR successes in

reservoirs characterised by depositional environment.The variation in efficiency of steam drives with LHI and

VHI can be explained by comparing the distribution, two yearsinto the steam drive, of the reservoir oil in the reservoirs wherethe process is most and least efficient. The most efficientsteam drive is in the LHI: 0.10/VHI: 0.10 reservoir, shown in

Fig.13a. A piston like flood front has formed about 500m intothe reservoir.

Fig.13bshows the LHI: 1.90/VHI: 1.45 reservoir, in whichthe simulated steam drive was least efficient. Again an oil

bank has formed at the flood front but it has been driven lessthen half the length of the reservoir. The flood front has been

distorted and fragmented by the many small shales acting asbaffles to flow. The shales can identified as the volumes of thereservoir still at the initial oil saturation.

Fig.14 shows the temperature distribution in the samereservoirs after 10 years, at the end of the steam drive. In the

most efficient steam drive (Fig.14a) the steam has heated upmost of the reservoir, both sands and shales, with the top of

the producer at over 340oF. Fig.14b shows the temperature

distribution in the least efficient reservoir. All but the first300m of the reservoir is still at the original reservoirtemperature, despite the oil saturation distribution in Fig.13bshowing that the injected steam front had travelled further thanthis after two years of injection. Though the injected steam has

penetrated 500m into the reservoir, it has cooled to such anextent by that time that it has negligible heating effect on thereservoir. The energy of the injected steam is being wastedheating the matrix of the shales. The larger the Heterogeneity

Index, the greater the surface area of the shales and the moreheat that will be lost to the non-net part of the reservoir.


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Polymer Flood Figs.15 and 16 show the recovery andefficiency surfaces for polymer floods. There is a good

correlation between recovery and efficiency; both indicate thatthe process is most effective at low levels of LHI (up to 0.8).The VHI of a reservoir has little or no effect on recoveryand efficiency.

The polymer flood models have markedly lower recovery

and efficiency than those demonstrated for steam drives at alllevels of LHI and VHI. This is because the polymer floodmodel was designed to simulate a tertiary recovery process,after one year of water flood, whereas the steam flood wasbeing used as a primary recovery method. As a consequencethe polymer models have lower initial oil saturation than thesteam drive models, 35% compared with 55%.

In Fig.17 the contours showing the variation in theefficiency of simulated polymer floods, with values of LHIand VHI below 1.2, are superimposed on a Tyler and FinleyHeterogeneity Matrix, showing the levels of heterogeneitypresent in successful polymer projects, plotted as circles.

There is a good correlation between the variation of

efficiency with LHI and the presence of successful polymerprojects. Projects over an LHI of 0.8 have a low efficiency andthere are no successful projects in the High LateralHeterogeneity boxes. There is no similar correlation with thevariation of efficiency with VHI, the contours show that thesame efficiency is expected in reservoirs in the Moderate andHigh Vertical Heterogeneity boxes, despite the fact that there

are few successful projects in the High Vertical Heterogeneityboxes. This could be because high heterogeneity reservoirs arerecognised as bad candidates for polymer floods, but the typeof heterogeneity has not been taken into account. Reservoirs inwhich polymer floods could be successful, because the highlevel of heterogeneity present can be classed as vertical, are

being ignored.Fig.18 demonstrates how LHI and VHI effect polymer

floods by comparing the oil saturation after 6 years of thepolymer flood in three different reservoirs. Fig.18ashows oilsaturation distribution in the most efficient reservoir(LHI:0.55/VHI: 0.10) where the injection of the surfactant for

a year has created a bank of enriched oil that the polymer slugand chase water has pushed towards the producer. The fronthas begun to collapse under gravity to the base ofthe reservoir.

Fig.18b shows how high levels of both Lateral andVertical Heterogeneity Index can decrease the efficiency of

the polymer flood. In this figure, the change in oil saturationwith time in the LHI:1.90/VHI:1.45 reservoir is shown. Theinjection of the surfactant slug has created a bank of high oilsaturation near the injector, but the front is distorted and hasmoved only ~150m along the reservoir. The oil saturation inthe swept region behind the flood front is patchy, high in some

areas, low in others. Volumes of oil have been trapped in deadends and alcoves as they follow a tortuous path tothe producer.

Fig.18cshows an efficient application of polymer floodingat a medium LHI but a high level of VHI, in the

LHI:1.00/VHI1.90 reservoir. The injection of the surfactantslug has formed a series of small flood fronts in the individualflow units that have swept a third of the reservoir. The shales

between flow units are preventing the collapse of the floodfront to the base of the reservoir as chase water is injected and

so allowing a better vertical sweep.These examples show that polymer floods are more

effective at low LHIs because the flood front is movingthrough thick, homogenous flow units with few internal shales

to act as baffles. As LHI increases the presence of internalshales becomes more likely as sand bodies become shorter,

and flow units are made up of more and more intersectingsand bodies.

The effect of VHI on the efficiency of polymer floods isdue to the presence of shales distorting the flood front. At low

levels of VHI there are thick flow units with no internal shales.As VHI increases shales appear within these units, acting as

baffles to flow and distorting the horizontal flow within flowunits. At levels of VHI approaching 2 the shales become thinenough that they have a minimal distorting effect on

horizontal flow, while acting as baffles and barriers to verticalflow and preventing the polymer slug from slumping to thebase of the reservoir.

Hydrocarbon WAG Simulations Due toconvergence problems, a coarser grid had to be used for theHydrocarbon WAG Simulations. With this coarse grid it was

impossible to model any reservoir with a VHI over 1.45. Thefive most vertically heterogeneous reservoirs were abandoned.

There are two separate fluids injected into the reservoir butit was decided to use only the amount of water injected tocalculate the efficiency of the WAG process. The injection ofwater is designed to displace the miscible

hydrocarbon/injected gas mixture from the reservoir. As sucha value of close to 1 for efficiency would indicate that thevolumes of water injected and oil produced are similar. Againthis measure is dimensionless.

When successful field applications of WAG injection wereplotted on a Tyler and Finley Matrix in Fig.3 they were

scattered over most of the matrix. As the eight data points alsoinclude both CO2 and hydrocarbon gas WAG injection it isdifficult to make any predictions as to the effects ofheterogeneity on the success of hydrocarbon WAG projects,but the wide scatter of the projects suggests that heterogeneityhas a relatively small effect.

The recovery of the hydrocarbon WAG floods through the75% n/g reservoirs is plotted as surface against the Lateral andVertical Heterogeneity Indices in Fig.19. The surfaces havebeen truncated to reflect the lack of simulation data in someregions of the matrix. Recovery is high across the wholematrix with little variation from the mean of 86% and changes

in heterogeneity have a negligible effect on the recovery ofhydrocarbon WAG injection. Water injection efficiency(Fig.20) is also high across the whole matrix.

The two ternary diagrams in Fig.21show the distributionof the three phases (gas, water and oil) present in the


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LHI:0.10/VHI: 0.10 reservoir (Fig.21a) and LHI:1.90/VHI:1.45 reservoir (Fig.21b) after 270 days. There has been onecomplete cycle and the slug of gas for the second cycle has

been injected. The distribution of gas oil and water in both thehigh heterogeneity and low heterogeneity reservoirs are very

similar. The shales seem to be having little effect on theinjected gas and water.

Net To GrossThe effects of net to gross on the recovery and efficiency of allthree IOR process was investigated by carrying out

simulations in reservoirs modelled at 60% and 45% net togross (n/g). At lower n/g there is more variation in thepercentage of total sand that is connected to both wells and agreater tendency at high levels of LHI for there to be no

continuous sand bodies or amalgamations of sand bodiesconnecting the injector and producer. The simulation result

surfaces have been truncated to reflect the regions of thematrix where no flow between wells is possible.

As net to gross is reduced, connectivity declines, resulting

in lower levels of recovery and efficiency for all three IORprocesses. Despite the reduction in efficiency across the whole

matrix there are still regions of the matrix in which the processoperate at a higher efficiency.

WAG injection seems to be particularly sensitive tochanges in reservoir connectivity. There is a close correlationbetween variations in the recovery and efficiency of WAGprocesses with LHI and VHI and variation in the

reservoir connectivity.

Reservoir DipThe effects of reservoir dip on the recovery and efficiency ofall three IOR process was investigated by carrying out

simulations in reservoirs to which a dip had been added. It wasdecided to simulate reservoirs at two degrees of dip; a lowvalue of 10

oand a high value of 60

owere chosen.

A small amount of reservoir dip added to the steam drivesimulations increases efficiency at the expense of productionrate. The pattern of more efficient steam drives is identical tothat seen in the non-dipping reservoirs. A slight reservoir dip

also has a detrimental effect on the simulated polymer floods,reducing injection rate and therefore recovery and efficiency.

Dip has no effect at any level of Heterogeneity Index on theWAG models.

Steeply dipping reservoirs, coupled with large inter welldistances, often result in the injector being below the effective

limit for steam or polymer injection. The simulations often faildue of lack of pressure support at the producer. Conversely,

when the dip is increased to 60oin the hydrocarbon gas WAG

models, both the recovery and efficiency increase at everylevel of heterogeneity.

Screening CriteriaFrom the results of these simulations it is possible to create

screening criteria for IOR processes that could be applied toidentify suitable candidate reservoirs for further investigation

and simulation. Figs.22, 23 and 24 summarize the findingsfrom the analysis of the Clastic Reservoir IOR ProjectDatabase, the screening criteria available in the literature and

results from the simulations.The nomenclature used in the screening criteria is taken

from Taber et al2. He emphasizes that the criteria are not

absolute. They are intended to show approximate ranges of a

particular reservoir or fluid property for good projects. Valuessuch as > x or < z are not meant to imply a specific upper

or lower limit to the parameter. The steam drive screeningcriteria, for example, shows that steam drives are

recommended for reservoirs with less than 1.2 VHI, but thisdoes not mean that the probability of a successful steam drivedrops to zero at 1.21 VHI.

Taber et al. have attempted to show that, for a given

parameter, if >x is feasible >>x may be even better for a givenprocess, within the natural limits of the parameter. By

underlining a value they indicate the mean of the parameter forthat particular IOR method. For example, for oil gravity in amiscible nitrogen flood the table states, > 35 48 means

that the process should work with oils greater than 35 API, ifother criteria are met, and that high gravity oils are better. It

also shows that the approximate mean of current miscibleprojects is 48 API. The ascending arrow indicates that highergravity oils are better still.

Conclusions Lateral and Vertical Heterogeneity Indices have been

developed to quickly identify suitable IOR candidatereservoirs for more detailed study.

Synthetic reservoirs models have been developed tostudy the interaction of geological object size, well spacingand IOR performance.

The synthetic model results have been plotted on asimple architecture matrix using the LHI and HI indices.

The simulation results confirm earlier publishedexpectations about the relationship between the efficiency ofIOR processes and architecture.

Data from the large IOR project database was plottedon the architecture matrix. Despite the often brief geological

descriptions available, the patterns plotted are consistent withthe results of the simulations, suggesting that a more detailed

and systematic recording of geological data could bedeveloped to provide guidance for screening IOR projects inthe future.

The results suggest that WAG is an effective IOR

process across a range of geological scenarios.

References1. Alpay, O. A. A Practical Approach To Defining Reservoir

Heterogeneity, Journal of Petroleum Engineering, (1972) V 24,

p841-848.

2. Taber, J. J; Martin, F. D. & Seright, R. S. EOR Screening Criteria

Revisited-P1: Introduction to Screening and EOR Field Projects.Part 2: Applications and Impact of Oil Prices, SPE Reservoir

Engineering, (Aug., 1997) p189-220.


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3. Tyler, N. & Finley, R. J. Architectural Controls on the Recovery

of Hydrocarbons from Sandstone Reservoirs, SEPM Conceptsin Sedimentology and Palaeontology, (1991) V3, p3-7.

4. Moritis, G. CO2 and HC injection lead EOR productionincrease, Oil and Gas Journal, (Apr. 23, 1990) p49-81.

5. Moritis, G. EOR increases 24% World-wide; Claims 10% of US

Production, Oil and Gas Journal, (Apr. 20, 1992) p51-79.

6. Moritis, G. EOR dips in US but remains a significant factor, Oil

and Gas Journal, (Sept. 26, 1994), p51-79.

7. Moritis, G. Technology, improved economics boost EOR hopes,Oil and Gas Journal, (Apr. 15, 1996) p31-69.

8. Moritis, G. EOR oil production up slightly, Oil and Gas Journal,

(Apr. 20, 1998) p49-77.

9. Hurst, A; Cronin, B; Hartley, A. & Verstralen, I. Sand-RichFairways in Deep-Water Clastic Reservoirs: Genetic Units,

Capturing Uncertainty, and a New Approach to ReservoirModelling, AAPG Bulletin, (1999) V83, No. 7, p1096-1118.

10. Reynolds, A. D. Dimensions of Paralic Sandstone Bodies,AAPG Bulletin, (1999) V82, No. 2, p211-229.

11. Bryant, I. D & Flint, S. S. Quantitative Clastic Reservoir

Modelling, In: BRYANT, I. D & FLINT, S. S. (eds.) TheGeological Modelling of Hydrocarbon Reservoirs and Outcrop

Analogues. International Association of SedimentologistsSpecial Publication 15. Blackwell Scientific Publications,(1993) p3-20.

12. Abbot, I. L. (Ed.) United Kingdom Oil and Gas Fields, 25th

Commemorative Volume,. (1991) Geological Society of LondonMemoir 14, 573p.

13. Aziz, K; Ramesh, A. B. & Woo, P. T. Fourth SPE ComparativeSolution Project: Comparison of Steam Injection Simulators,

Journal of Petroleum Technology, (Dec. 1987) p1576-1584.

14. Saad, N. & Sepehrnoori, K. Simulation of Big Muddy SurfactantPilot, SPE 17549, SPE Reservoir Engineering, (1989) p24.

15. Chapotin, D; Lomer, J. F. & Putz, A. The Chateaurenard (France)

Industrial Micro-emulsion Pilot Design and Performance, SPE14955, SPE/DOE 5thSymposium, Tulsa, (1986).

16. Huh, C; Landis, L. H; Maer Jr., N. K; Mckinney, P. H. &

Dougherty, N. A. Simulation to Support Interpretation of theLoudon Surfactant Pilot Tests, SPE 20465, 65th Annual SPETechnical Conference and Exhibition, New Orleans, 23 th-26th

September, 1990.

17. Dreyer, T. Geometry and Facies of Large-Scale Flow Units inFluvial-Dominated Fan-Delta Front Sequences, In: ASHTON,M. (ed.) Advances in Reservoir Geology. Geological Society

Special Publications, (1993) No 69, p135-174.

Fuzzy Sand Body Fuzzy Sand Body

Heterogeneity Possibility Length for Av. Height for Av.

Index North Sea Reservoir North Sea Reservoir

-0.20 0 1584.9 158.5

0.10 1 794.3 79.4

0.40 0 398.1 39.8

0.25 0 562.3 56.2

0.55 1 281.8 28.2

0.85 0 141.3 14.1

0.70 0 199.5 20.0

1.00 1 100.0 10.0

1.30 0 50.1 5.0

1.15 0 70.8 7.1

1.45 1 35.5 3.5

1.75 0 17.8 1.8

1.60 0 25.1 2.5

1.90 1 12.6 1.3

2.20 0 6.3 0.6

Table.1-Heterogeneity Values Index for 25 Synthetic Reservoirs,defined by one most likely value (with a possibility of 1) and

a spread of +/- 0.3 (with a possibility of 0)

Steam Polymer HC Wag

Oil 11 20.26 44.6 oAPI

Oil Density 0.993 0.932 0.804 g/cm3

Initial Oil Saturation 55 35 80 %

Reservoir Temperature 125 207 160oF

Reservoir Pressure 1102 6246 4000 psia

SandPorosity 30 30 30 %

SandPermeability 1000 1000 1000 mD

ShalePorosity 0.001 0.001 0.001 %

ShalePermeability 0.001 0.001 0.001 mD

i 100 100 100

j 1 1 1

k 100 100 50

Total Blocks 10,000 10,000 5,000

Simulation Length 10 10 1 years

Injectant 70% steam at Year 1: 3 month injection

450oF for 10 Wate r a lone of gas , consi st ing

years Year 2: off 77% C1, 20% C3

Surfactant slug and 3% C6, alternating

Years 3 and 5: with a three month

Polymer slug water slug.

Year five:

Polymer taper

Years 6 to 10:

Chase water

Table.2-Summary of Simulation Parameters for the ThreeIOR Processes


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Fig.1-Scales of Reservoir Heterogeneity (from Dryer16

).

Lateral Heterogeneity

Low Moderate High

Wave-dominated delta Delta front mouth bar Meander belt1

Barrier Core Proximal delta front

Barrier Shore Face (accretionary) Fluvially dominated delta1

Sand Rich Sand Plain Tidal deposits

Mud-rich strand plain Back barrier1

Shelf bars

Alluvial fans

Aeolian Fan delta Braided stream

Lacustrine delta

Wave-modified delta Distal delta front Tide-dominated delta

(distal) Wave-modified delta

(proximal)

Coarse-grained meander Back barrier2

belt Fluvially dominated delta2

Basin flooring turbidites

Braid delta Fine-grained meander belt2

Submarine fans2

1single units

2stacked systems

VerticalHeterogeneity

Low

Moderate

High

Fig.2-Tyler and Finley Clastic Heterogeneity Matrix(after

Tyler and Finley3).

.

Fig.3-Successful IOR projects plotted by project type on a Tylerand Finley Heterogeneity Matrix

Fig.4-Tyler and Finley Heterogeneity Matrices showing areaswhere different IOR processes are successful.


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Fig.9-75% Net to Gross Synthetic Reservoirs, Sand in Light Grey and Shale in Dark Grey. Reservoirs Outlined in the Dotted Line Have ZeroConnectivity. x3 Vertical Exaggeration.

Fig.5-Failed IOR Projects Plotted by Project Type on a Tyler andFinley Heterogeneity Matrix

Low High

1. Very high to total mobile 1. Low mobile oil recovery

oil recove ry

2. Compartmentalised, unconta cted

3. Excellent EOR candidate and lateral bypassed oil

3. Targeted infill drilling

1. Very low mobile oil recov ery

1. Low efficiency

2. Vertical by pas sed mo bile oil 2. Bo th un co nt acted an d b yp as sed

mobile oil

3. Profile modification waterflood

re des ig n, rec omp letio ns 3. Ta rg eted in fill d rillin g, waterflo od

redesign, profile modification and

recompletions

Low

High

VerticalHe

terogeneity

Lateral Heterogeneity

Fig.6-Heterogeneity Matrix Showing Predictions for SuccessfulIOR projects (after Tyler and Finley

3).

Fig.7-Simple Heterogeneity Model. An Injector and Producer Pair in aSand/Shale Reservoir.


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Fig.8-Horizontal Injector and Producer Pair in a Simple Sand/ShaleReservoir.

Fig.10-Steam Drive Recovery vs. Lateral and VerticalHeterogeneity Indices.

Fig.11-Steam Drive Efficiency vs. Lateral and VerticalHeterogeneity Indices.

Fig.12 Contours Showing the Variation in Efficiency of SimulatedSteam Drives (scale on bottom and right edges of diagram)Superimposed on a Tyler and Finley Heterogeneity Matrixshowing the level of Heterogeneity in Successful Thermal

Projects (scale on top and left edges of diagram).

Fig.13a Oil Saturation 730 Days into a Steam Drive Through the

LHI: 0.10/VHI: 0.10 Reservoir.

Fig.13b Oil Saturation 730 Days into a Steam Drive Through theLHI: 1.90/VHI: 1.45 Reservoir.


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Figure 14a Temperature 3650 Days into a Steam Drive Throughthe LHI: 0.10/VHI: 0.10 Reservoir.

Figure 14b Temperature 3650 Days into a Steam Drive Throughthe LHI: 1.90/VHI: 1.45 Reservoir.

Fig.15-Polymer Flood Recovery vs.Lateral and VerticalHeterogeneity Indices.

Fig.16-Polymer Flood Efficiency vs. Lateral and VerticalHeterogeneity Indices.

Fig.17-Contours Showing the Variation in Efficiency of SimulatedPolymer Floods (scale on bottom and right edges of diagram)

Superimposed on a Tyler and Finley Heterogeneity Matrixshowing the level of Heterogeneity in Polymer Thermal Projects

(scale on top and left edges of diagram).

Fig.18a-Oil Saturation 2190 Days into a Polymer Flood Through1the LHI: 0.55/VHI: 0.10 Reservoir.


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Fig.18b Oil Saturation 2190 Days into a Polymer FloodThrough the LHI: 1.90/VHI: 1.45 Reservoir.

Fig.18c Oil Saturation 2190 Days into a Polymer FloodThrough the LHI: 1.00/VHI: 1.90 Reservoir.

Fig.19-Hydrocarbon WAG Injection Recovery vs. Lateral andVertical Heterogeneity Indices.

Fig.20-Hydrocarbon WAG Injection Efficiency vs. Lateral and

Vertical Heterogeneity Indices.

Fig.21a-Saturation 270 Days into a Hydrocarbon WAG Flood: LHI:0.10/VHI: 0.10 Reservoir.

Fig.21b-Saturation 270 Days into a Hydrocarbon WAG Flood: LHI:1.90/VHI: 1.45 Reservoir.


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Fig.23-Polymer Flood Screening Criteria

Description:A small amount of a high molecular weight, water-soluble, polyacrylamide or polysaccharide are added to the water in awater-flood type operation.

Mechanisms:The polymer reduces the mobility of the flood water and therefore lowers the mobility ratio, improving microscopicdisplacement efficiency. It also increases the viscosity of the displacing fluid to prevent water fingering and gravity slumping.

Heterogeneity: The effectiveness of a simulated polymer flood declines with increasing lateral heterogeneity as flood front is distortedand broken up by baffles to horizontal flow.

Net to Gross: When the net to gross is reduced to 60%, mean recovery and efficiency declines. The range of heterogeneities over which a polymer

flood provides relatively good recovery at relatively high efficiencies also declines. When the net to gross is reduced to 45% the mean recovery andefficiency declines further, but the range of good results does not change.

ReservoirDip: A reservoir dip of, 10oreduces the injection rate and therefore recovery and efficiency decline. A steep reservoir dip causes the

simulations to fail.

Problems:The stability of polymers can be threatened by hydrolysis and attack by free radicals. Polymers can be retained in the rock by adsorptionon the surface of the pores, mechanical entrapment in pores and hydrodynamic retention. Velocity enhancement, where injected water races ahead of

the polymer slug, can reduce the effectiveness of the process. Polymers can also degrade at high temperatures.

Oil Properties Reservoir Characteristics

Oil Net Average Average

Gravity Viscosity Composition* Saturation Thickness* k Depth Temp.

(API) (mPa.s) (% PV) (m) (%) (mD) (m) (C)

> 15 > 0.02 > 27 NC > 4.4 > 7 > 240 > 100

28 < 190NC

58 19.5 540 1,534 54

*Taken from Taber et al., 2 NC = Not Critical Underlined values are the approximate mean for successful projects


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Fig.24-Hydrocarbon WAG Injection Screening Criteria

Description:Water is injected with gas as alternate slugs. By combining the two the sweep efficiency and conformance of both miscible and

immiscible flooding can be improved.

Mechanisms:Recovery is increased by oil swelling, viscosity reduction and limited crude oil vaporisation. If the reservoir conditions are right for the

gas injected, miscibility is developed. Water is injected to drive the oil towards the wellbore and prevent gas interfingering and override.

Heterogeneity: The effectiveness of the simulated hydrocarbon flood is unaffected by the level of heterogeneity with high recoveries and efficienciesat all levels of heterogeneity.

Net to Gross: When the net to gross is reduced from 75%, hydrocarbon WAG injection recovery and efficiency also decline. The major control onboth recovery and efficiency at all levels of n/g is connectivity.

Reservoir Dip: A dip of 10ohas no effect at any level of heterogeneity. When the dip is increased to 60 oboth the recovery and efficiency for WAGinjection increases at every level of heterogeneity, due to the reduction in water slumping. An updip injector would reduce gas override.

Limitations:A source of cheap gas is necessary, depth and pressure thresholds are required for the development of miscibility.

Problems:Dependent on the gas injected but can include the production of low pH water and gas escape to the atmosphere.

Oil Properties Reservoir Characteristics

Oil Net Average Average

Gravity Viscosity Composition* Saturation Thickness* k Depth Temp.

(API) (mPa.s) (% PV) (m) (%) (mD) (m) (C)

High fraction > 3038 0.6

of C2to C7 70NC NC NC 2,560 NC

*Taken from Taber et al., 2 NC = Not Critical Underlined values are the approximate mean for successful projects

00075148 Geologically Based Screening Criteria for Improved Oil Recovery Projects Richard Hansen...

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