32

Lars PedersenStatoilBergen, Norway

Sarah RyanColin SayersCambridge, England

Lars SonnelandHelene Hafslund VeireStavanger, Norway

Seismic Snapshots for Reservoir Monitoring

Seismic surveys acquired at differe

provide time-lapse snapshots of th

This technique, called four-dimens

helping operators delineate bypass

programs to optimize recovery and

For help in preparation of this article, thanks to OlavHolberg, Geco-Prakla, Oslo, Norway; Dominique Pajotand Robin Walker, Geco-Prakla, Gatwick, England; andBenoit Reymond, Geco-Prakla, Stavanger, Norway.RST (Reservoir Saturation Tool) and TRISOR are marks ofSchlumberger. Some work described in this article wasperformed as part of the Thermie project “4D Seismic,”European Commission Contract No. OG 117/94 UK.

nt stages in the life of a reservoir can

e fluid distribution over production time.

ional (4D) seismic reservoir monitoring, is

ed hydrocarbons and design development

extend the useful life of fields.

Oilfield Review

Winter 1996 33

Reservoir management today is a science ofapproximation when it comes to the rateand direction of fluid-front movement. Opti-mal management requires up-to-date infor-mation throughout the entire reservoir vol-ume. Access to the latest data on fluiddistribution in a reservoir, and knowledge ofhow that distribution is changing with time,allows engineers to develop cost-effectivestrategies to get the most out of every field atthe lowest possible risk.

Today, in addition to static, or one-timemeasurements, time-dependent answersfrom various oilfield disciplines help con-strain, refine and improve the accuracy ofreservoir models. Time-lapse logging offluid saturation through casing can showwhich zones are contributing to productionand which are watering out or beingbypassed.1 Permanent downhole sensorsprovide continual observations of pressure,temperature and other diagnostics of reser-voir performance.2

These measurements supply crucial infor-mation about fluid behavior at the welllocation, but fail in the vast interwell region.One measurement technique, the 3D seis-mic survey, has routinely been relied on toprovide interwell data. In the past, seismicsurveys were mainly interpreted for struc-

tural features and stratigraphic variationswithin the reservoir, but they can also besensitive to contrasts in fluid type. Appliedin surveys separated by periods of produc-tion, time-lapse, or four-dimensional (4D)—3D plus time—seismic images can mapfluid changes in a producing reservoir(below). This article describes the technique,the rock physics and seismic modelingrequired for successful application, con-straints in seismic acquisition, and newinterpretation methodologies that allowchanges in seismic response to be inter-preted as changes in saturation. Applicationof these techniques will be discussed in anexample from the North Sea Gullfaks fieldwhere water is displacing oil—the ultimatechallenge in seismic monitoring.

How 4D Works As a reservoir is exploited, pore fluid under-goes changes in temperature, pressure andcomposition. For example, enhanced oilrecovery (EOR) processes such as steaminjection increase temperature. Productionof any fluid typically lowers fluid pressure,

increasing effective pressure of overburdenon the formation rock. Gas injection andwaterflooding mainly change fluid composi-tion and pressure. These fluid changes alterthe formation’s seismic velocity and density,which combine to affect travel times, ampli-tude and many other features, or attributes,of reflected seismic waves.

When these changes are great enough, aseismic survey acquired after years of pro-duction—called a monitor survey in thisarticle—will show different attributes thanone acquired earlier, perhaps even beforeproduction begins—the baseline survey.With today’s computer technology, it is pos-sible to take the difference between two sur-veys, be that the change in amplitude, fre-quency, phase, polarity, reflection intensity,or of any seismic trace attribute (see “Seis-mic Attributes,”next page).3 The key to 4Dseismic monitoring is for the change to besufficiently large to be seen once the differ-ence between a baseline survey and subse-quent monitor surveys is computed.

1. For a review: Albertin I, Darling H, Mahdavi M,Plasek R, Cedeño I, Hemingway J, Richter P, MarkleyM, Olesen J-R, Roscoe B and Zeng W: “The ManyFacets of Pulsed Neutron Cased-Hole Logging,” Oil-field Review 8, no. 2 (Summer 1996): 28-41.

2. Baker A, Gaskell J, Jeffrey J, Thomas A, Veneruso T andUnneland T: “Permanent Monitoring—Looking at Life-time Reservoir Dynamics,” Oilfield Review 7, no. 4(Winter 1995): 32-47.

3. For background on seismic attributes: Taner MT andSheriff RE: “Application of Amplitude, Frequency, andOther Attributes to Stratigraphic and HydrocarbonDetermination,” in Payton CE (ed): Seismic Stratigra-phy—Applications to Hydrocarbon Exploration AAPGMemoir 26. Tulsa, Oklahoma, USA: American Associ-ation of Petroleum Geologists (1977): 301-327.

■■Seismic snapshots chronicling fluid movement over the lifetime of a reservoir.

34 Oilfield Review

Seismic Attributes

Seismic attributes represent a way to compress the

information contained in seismic traces. Attributes,

such as amplitude, frequency, phase, polarity and

a host of others, may be defined in different ways.1

Some are computed at one time sample—that of

the interpreted top of the reservoir, or whatever

surface is being characterized. Such attributes are

called instantaneous. Taken collectively, over

many traces, the attribute then represents some

characteristic of the reflecting surface. Display of

an instantaneous attribute is often accomplished by

color coding the display of the surface itself in two

or three dimensions (next page, left).Other attributes may be computed over several

time samples in a trace, effectively describing a

volume. Called volume attributes, they compress a

potentially large quantity of seismic data into a

single value. For example, reflection heterogene-

ity, related to the “length” of seismic trace

between two selected time samples, says some-

thing about the homogeneity or heterogeneity of

the internal reflector pattern in the volume of

reservoir between the two time samples.2

A third type, a surface attribute, can be com-

puted on a defined area of the interpreted surface.

Examples are the apparent dip and dip azimuth of

the surface.

Different attributes can be sensitive to different

reservoir properties. Instantaneous phase, for

example, highlights continuity of reflectors.

Instantaneous frequency has been reported as a

light-hydrocarbon indicator. Acoustic impedance

Instantaneous Amplitude Amplitude Deviation

Polynomial Factor Polynomial Factor

■■Seismic attributes of the top reservoir surface. The instantaneous attributes describe the top surface of thereservoir, and volume attributes are sensitive to fluid content. The instantaneous amplitude (top left) shows posi-tive amplitudes in red grading to negative amplitudes in blue. Instantaneous amplitude deviation (top right)enhances definition of regions where amplitude changes from positive (white) to negative (blue). The twoattributes plotted in the bottom figures are related to principal components of a polynomial that approximates thetrace shape.

contrast has been used for porosity mapping.3 Vol-

ume reflection heterogeneity is sometimes a

lithology indicator.4 Dip and dip azimuth are pow-

erful fault and fracture indicators.5 Finding the

right attribute, or combination of attributes, that

will show sensitivity to the reservoir properties of

interest, is the job of the interpreter.

Interpreting 4D seismic data for fluid contact

changes in Gullfaks field requires mapping the top

of the reservoir, pinpointing areas where different

caprocks overlie the reservoir and identifying the

type of fluid below. This process relies on a com-

bination of instantaneous and volume attributes

(below). Instantaneous attributes show sensitivity

to the top surface of the reservoir and volume

attributes describe the fluid content.

1. For the classic introduction to seismic attributes:Taner and Sheriff, reference 3, main text.

2. Sønneland L and Barkved O: “Use of SeismicAttributes in Reservoir Characterization,” in Buller AT,Berg E, Hjelmeland O, Kleppe J, Torsæter O andAasen JO (eds): North Sea Oil and Gas Reservoirs—II.London, England: Graham & Trotman (1990): 125-128.

3. Alam A, Matsumoto S, Hurst C and Caragounis P:“Qualitative Porosity Prediction from SeismicAttributes,” presented at the 65th Society ofExploration Geophysicists International Expositionand Annual Meeting, Houston, Texas, USA, October8-13, 1995, paper IN2.2.

4. Risch DL, Donaldson BE and Taylor CK: “3D SeismicSequence Stratigraphy of Lowstand Deposits,” pre-sented at the SEG Summer Research Workshop on 3-D Seismology: Integrated Comprehension of LargeData Volumes, Rancho Mirage, California, USA,August 1-6, 1993.

5. Heggland R: “Detection of Ancient Morphology andPotential Hydrocarbon Traps Using 3-D Seismic Dataand Attribute Analysis,” presented at the 65th Societyof Exploration Geophysicists International Expositionand Annual Meeting, Houston, Texas, USA, October8-13, 1995, paper IN2.3.

Winter 1996 35

If temperature and pressure in the reservoirare known, the effects on seismic propertiesexpected from a change in fluid propertiescan be estimated from laboratory experi-ments on core samples. Most of the changesin seismic behavior come from fluid effectson the formation’s seismic velocity ratherthan on its density. Laboratory experimentson fluid-filled rock show how temperature,pressure and fluid content can eitherdecrease or increase seismic velocity.4

Introduction of gas into liquid-filled rockor an increase in temperature of hydrocar-bon-filled rock both cause a decrease inseismic velocity (above). Introduction of gasdecreases velocity substantially by makingthe fluid mixture compressible. The effect ofincreasing temperature makes hydrocarbonsless viscous, reducing overall rigidity andtherefore reducing seismic velocity. Botheffects are most prominent at low overbur-den stress, such as in shallow, unconsoli-dated sands. A dramatic increase in velocitycan occur with decrease in fluid pressure, astypically occurs during oil production. Thedecrease in fluid pressure increases effectivestress on the reservoir rock, stiffening thematrix and increasing velocity.

Replacing oil with water gives rise to amoderate increase in seismic velocity. Thisincrease reaches about 10% in clean, high-porosity sandstones, and can be even greaterin unconsolidated sands. Above 30% poros-ity, seismic velocities of oil-filled and water-filled rocks become distinguishable, at leastin theory and in the laboratory (next page).

Most successes in early tests of seismicmonitoring occurred where the dramaticeffects of temperature increase and gasintroduction gave clear results. In the 1980s,the first reported time-lapse surveys weremade on combustion and steamflood pro-jects, where temperature effects are easiestto see.5 Today, in some reservoirs, seismicmonitoring of EOR progress continues to bea cost-effective reservoir management tool.6

Successes have also been reported intime-lapse seismic monitoring of gas-oilcontacts. Norsk Hydro began seismic moni-toring of the gas-oil contact in the giant,North Sea, Oseberg field in 1991, and ElfAquitaine performed a similar project in thenearby Frigg field.7

x y

xy

xy

Tim

e

Seismic Data Cube

Amplitude Attribute (high in middle)

Frequency Attribute (low in middle)

■■Computing attributes on a time surface in a 3Dseismic volume. The seismic character of each traceis analyzed at the selected time (top) and assigned avalue. For example, the amplitude of each trace on asurface can be mapped on a separate plot (middle).In this example, higher amplitudes near the center ofthe 3D seismic volume plot as higher values in thecenter of the 2D amplitude plot. Other attribute sur-faces are computed in the same way (bottom).

Com

pres

sion

al v

eloc

ity, m

/sec

Nor

mal

ized

com

pres

sion

al v

eloc

ity

Temperature, deg C Gas saturation, %0 50 100 150 200 0 20 40 60 80 100

0.6

0.7

0.8

0.9

1.0

1.1

2000

1600

1200

800

0% oil100% brine

50% oil50% brine

100% oil0% brine

10 20 30 40 5004

5

6

7

8

9

10

Aco

ustic

impe

danc

e

Effective stress, MPa

Initialstress

Producerstress

WaterOil

Gas

Saturationchange 4%

Pressurechange 12%

■■Effect of gas and high temperature on seismic velocity. Increasing temperaturedecreases velocity in oil-filled rocks (left). Introduction of gas also decreases velocity dra-matically (center). A decrease in fluid pressure has the opposite effect (right), increasingacoustic impedance—the product of velocity times density.

4. Murphy W, Reischer A and Hsu K: “Modulus Decom-position of Compressional and Shear Velocities inSand Bodies,” Geophysics 58 (February 1993): 227-239.Clark VA: “The Effect of Oil Under In-Situ Conditionson the Seismic Properties of Rocks,” Geophysics 57,(July 1992): 894-901.Tosaya C, Nur A, Vo-Thanh D and Da Prat G: “Labora-tory Seismic Methods for Remote Monitoring of Ther-mal EOR,” SPE Reservoir Engineering 2 (May 1987):235-242.Wang Z and Nur A: “Wave Velocities in Hydrocar-bon-Saturated Rocks: Experimental Results,” Geo-physics 55 (June 1990): 723-733.Watts GFT, Jizba D, Gawith DE and Gutteridge P:“Reservoir Monitoring of the Magnus Field Through4D Time-Lapse Seismic Analysis,” Petroleum Geo-science 2, no. 4 (November 1996): 361-372.

5. Greaves RJ and Fulp TJ: “Three-Dimensional SeismicMonitoring of an Enhanced Oil Recovery Process,”Geophysics 52 (September 1987): 1175-1187.He W, Anderson RN, Xu L, Boulanger A, Meadow Band Neal R: “4D Seismic Monitoring Grows as Pro-duction Tool,” Oil & Gas Journal 24, no. 21 (May 20,1996): 41-44, 46.

6. Ariffin T, Solomon G, Ujang S, Bée M, Jenkins S, Cor-bett C, Dorn G, Withers R, Özdemir H and Pearse C:“Seismic Tools for Reservoir Management,” OilfieldReview 7, no. 4 (Winter 1995): 4-17.

7. Johnstad SE, Uden RC and Dunlop KNB: “SeismicReservoir Monitoring Over the Oseberg Field,” FirstBreak 11 (May 1993): 177-185.Bossert A, Blanche J-P, Capelle P, Marrauld J andTorheim E: “Seismic Monitoring on the Frigg Gasfield(Norway) Using AVO Attributes and Inversion,” pre-sented at the 55th Meeting and Technical Exhibition ofthe European Association of Exploration Geophysi-cists, Stavanger, Norway, June 7-11, 1993.

36 Oilfield Review

Until recently, the technique was consid-ered unproven for monitoring movement ofan oil-water contact (OWC). However, Sta-toil petrophysicists working on sonic logsfrom the Gullfaks field uncovered inconsis-tencies in log response that turned intofavorable conditions for 4D seismic moni-toring. Openhole logs from three wellsdrilled through the original OWC, com-pared to logs from wells drilled into water-flushed areas, showed consistently highervelocities in the water-filled zones than inthose zones filled with oil. Sonic logs run ina steel-cased observation well were foundto be unrepeatable from year to year.

Periodic logging with the RST ReservoirSaturation Tool in the same well showedthat the OWC was rising 13 m [42 ft] peryear. Analysis showed that sonic logs abovethe OWC were repeatable from one year tothe next, while below the OWC, sonicvelocities increased where water had dis-placed oil. If borehole sonic waves coulddetect saturation changes at the well scale,perhaps seismic waves could do the sameacross the whole field.

Predicting SuccessSeismic response to a change in fluid prop-erties at a reflector can be predicted throughforward modeling, if elastic properties of therock and fluids are known. Relationshipspublished by Gassmann, and later rewordedby many authors, can be used to predictdensity and seismic velocity through what iscalled fluid substitution—knowing the prop-erties of a rock filled with the original fluid,as well as the properties of the new fluid,allows computation of the properties of thenewly filled rock.8

Fluid substitution requires knowledge of thedensity, porosity, and bulk and shear moduliof the rock frame, the bulk modulus of thegrains making up the rock, and the densityand bulk modulus of the two fluids, all at thepressure and temperature conditions of thereservoir. Rock grain properties are usuallymeasured in the laboratory, while the rockframe and fluid properties may be measuredin the lab or inferred from borehole measure-ments of compressional and shear velocities,porosity and density (next page, top).

The computed compressional velocity canbe used to model the anticipated change inseismic response at the top of the reservoir,in this case, the top of the Tarbert sand (nextpage, bottom). When the rock is filled withoil, a normal-incidence reflection has a veryslight swing to the right. When rock is filledwith water, the reflection shows a greaterswing, and to the left. The difference inmodeled responses to oil-filled and water-filled reservoir becomes more pronouncedwhen the effects of seismic trace stackingare taken into account.

This seismic modeling has assumed thatthe Tarbert sand is completely filled witheither oil or water, but intermediate stages ofsaturation can also be modeled. If the OWCis an abrupt change in saturation that occursover just a few feet or meters—as was prob-ably the case when the reservoir was atequilibrium before any fluids were pro-duced—the contact may also be a seismicreflector. After years of production, the satu-ration change may occur over a wide transi-tion zone, and may or may not appear as adiscontinuity to seismic waves.

Acquisition ConcernsPredicting that the “before” and “after” pic-tures will look different is just the first stepin setting the stage for 4D seismic snap-shots. Seismic modeling usually assumesthat survey parameters in the baseline andmonitor surveys are identical. Surveyparameters include receiver positions,source positions, source signature, and anydirectivity or coupling effects associatedwith the environment. In the past, this haslimited most 4D experiments to land, wheresource positions can at least be marked andreceivers permanently implanted and revis-ited for monitoring surveys.

In the marine environment, permanentsensors have been used for decades, butusually for earthquake and other seismicitydetection. Only in recent feasibility andpilot tests have ocean bottom cables beenpermanently installed over reservoirs forrepeat seismic surveying. In what may bethe biggest 4D seismic monitoring develop-ment so far, BP and Shell have installed per-manent cables in the seabed overlying theFoinaven field in the deep waters—480 m [1575 ft]—of the North Sea west ofShetlands.9 A baseline survey was acquiredin 1995, and first oil will be produced in1997. Monitor surveys are planned everyyear. In this deepwater environment, opera-tors hope that seismic imaging of bypassedhydrocarbons will help optimize futuredevelopment plans.

Permanent sensors for marine seismicmonitoring are still in the experimentalstage. And though they will likely ease someproblems associated with imperfectlyrepeated experiments, they cannot promisethe same weather, currents or other tempo-ral conditions that affect all marine surveys.This does not mean that 4D marine moni-toring is infeasible. While some geophysi-cists press the case for exact repeatability,others say a high degree of repeatability isdesirable but not necessary. Most agree it isessential to eliminate as many physical vari-ables as possible between surveys.10

Porosity, %0

Nor

mal

ized

Vp

10 20 30 401

2

3

4

5

Water

GasOil

■■Distinguishing gas,oil and water. Com-pressional velocitiesof gas-, oil- andwater-filled cleansandstones are simi-lar at low porosities,but are differentenough at highporosities to allowfluid identification.Symbols representlaboratory velocitymeasurements, whilecurves are theoreti-cal predictions.(From Murphy et al,reference 4.)

8. Gassmann F: “Uber die Elastizät Poröser Medien,”Vierteljahrsschr. Naturforsch. Ges. Zürich 96 (1951):1-23.See also Murphy et al, reference 4 and Wang andNur, reference 4.

9. Kristiansen P and Currie MT: “Seismic Imaging Capa-bilities Optimize Reservoir Management,” PetroleumEngineer International 67 (December 1995): 22-23,25.

10. Von Flatern R: “Adding Time Makes Seismic Data aProduction Tool,” Petroleum Engineer International67 (December 1995): 17-18, 20-21.

Winter 1996 37

1880

1900

1920

1940

1960

1980

Dep

th, m

0 150 1.95 2.95 2177 7622 0 100API g/cm3 %

1.95 2.95 2177 7622g/cm3

Gamma RayDensity, Water-Filled

Porosity

Density, Oil-Filled

Velocity, Water-Filled

Velocity, Oil-Filled

m/sec

m/sec

Top Tarbert

■■Fluid substitution.Replacing the oil(red) in the porespace with water(blue) increases thedensity and com-pressional velocityin the high-porosityTarbert sand.

TopTarbert

50 125

GammaRayAPI

Oil-Filled SyntheticReal Seismic Section

Water-Filled Synthetic SeismicTrace at Well

NIP Stack NIP Stack

Tim

e, m

sec

1866

1900

1925

1950

1875

■■Modeling seismic response to different fluids using log input. Normal-incidence compressional (NIP) syntheticseismograms computed for oil-filled Tarbert sand (track 2) and water-filled sand (track 5) show different charac-teristics at the top reservoir reflection. Real seismic data traces, however, are the result of stacking traces frommany angles of incidence. The synthetic oil- and water-filled equivalents to the real stacking process are intracks 3 and 6, respectively. The seismic section near the well is shown in track 4. The seismic trace at the welllocation is in track 7, repeated several times for ease of comparison with the synthetics.

38 Oilfield Review

For some time, conventional towed marine3D has involved careful positioning of in-sea elements, with differential global posi-tioning system antenna and acoustic rangingroutinely providing receiver streamer posi-tioning accuracy to within a couple ofmeters.11 Imminent technological improve-ments that help correct for tidal changes willimprove overall data quality and accuracy.Similarly, improvements in source monitor-ing possible with the TRISOR source controlsystem should lead to the elimination ofmost of the variability in individual shotcharacteristics, or signatures. With this vari-ability minimized, processing should beable to give “matched” data sets, in whichdifferences between surveys are removed.

To ensure that the data from the two sur-veys resemble repeat acquisitions, therepeatability must be quantified. Thisrequires a diagnostic tool to verify that therepeat accuracy requirements are met. Inaddition, there must be a compensation pro-cedure to fix data that deviate from idealconditions. These diagnostic and compensa-tion tools may be used during acquisition orprocessing, depending on the problem.

An example of repeatability diagnosis andcompensation may be found in a study ofsource depth during marine seismic acquisi-tion. The depth of the source in the watercan be monitored with the TRISOR sourcecontrol system to within one meter [3.3 ft].There may be, without intention or knowl-edge, a perturbation of the depth, say to 2 m[6.6 ft] deeper than specified, starting atsome shot point. The effect on the recordedwaveforms may be a slightly changed ampli-tude, which, when processed, could beinterpreted as a change in seismic reflectioncharacteristics at the target. Diagnosing theorigin of the amplitude change as a sourcedepth shift can be accomplished by moni-toring the source signature. Continuity ofsignal is expected from shot to shot if thesource depth is constant. When a perturba-tion of the standard signal is detected, theproblem may be fixed during acquisition. Ifsource monitoring is not done in real time,the archive of acceptable shot signals maybe used to create a match filter, forcing theperturbed source signal to conform to thosefor the correct depth.

Monitoring the quality of the seismic sig-nal and other essential measurements, suchas navigation accuracy, during acquisition,allows identification of those perturbationsthat can be accepted, those that can becompensated for, and those that require anew experiment.

Gullfaks

Aberdeen

Bergen

Stavanger

GullfaksSouth

S E A

UK

Areaof detail

N O R T H

NORWAY

DENMARKN

■■Gullfaks field, Norwegian North Sea.

Tarbert formation

500

Dep

th, m

1000

0

1500

2000

2500

0Distance, m

2000 4000 6000 8000 10,000

Dipping seafloor

400% verticalexaggeration

■■Effects of Gullfaks structural complexity on seismic data qualityand modeling. The steep change in seabottom depth over thefield, strong reflectors overlying the target and a highly faultedtarget combine to make imaging and characterization of Gull-faks reservoir properties difficult.

Winter 1996 39

The following 4D seismic monitoringexample shows how the combination ofhigh-quality marine acquisition and innova-tive interpretation techniques has helpedStatoil optimize development of the Gull-faks field (previous page, top).

Monitoring Gullfaks ProductionUnder the current production plan, begunin 1986, the Gullfaks field in the NorwegianNorth Sea will produce 50% of its 480 mil-lion m3 [3020 million bbl] original oil inplace. About half the remaining oil could beproduced with infill wells, but locatingthose wells optimally and reducing costscontinues to be a challenge.

Development of Gullfaks has been ham-pered by the complexity of the faulted reser-voir structure (above). New seismic data andresults from development wells have led toimproved knowledge and refinement infault mapping. Carefully planned dataacquisition and flexible drilling programs

resulted in a sound understanding of reser-voir properties early in the development.

Reservoir simulation has been used exten-sively to continually evaluate production anddevelopment options for the field. But evenwith detailed reservoir models, engineersdon’t know where a fluid front is until itarrives at a well. By tracking fluid-contrastfronts before they get to wells, reservoir engi-neers are able to take action to avoid poten-tial problems. With 4D seismic monitoring,the project team—comprising geophysicists,geologists, petrophysicists and reservoir engi-neers—will be able to map reservoirdrainage and optimize future production.

Several sets of seismic data have beenacquired over the Gullfaks field. Followingthe initial exploration interpretation basedon 2D lines spaced on a 2-km [1.2-mile]grid, a first 3D survey was acquired in 1979and a second in 1985. Despite the markedimprovement in the quality of the secondsurvey, and careful reprocessing in 1992,the data are still very complex due to vari-able reflectivity and the highly faulted targetzone (previous page, bottom).12

A pilot reservoir volume was chosen to testthe ability of 4D seismic monitoring to mapchanges in the oil-water distribution. Theselected area exhibited reservoir conditionsknown to be favorable for seismic monitor-ing: high porosity—averaging 34%—and areliable seismic interpretation.

The three production platforms created anacquisition challenge for the 1995 monitorsurvey: seismic vessels had to navigate toavoid the platforms, leaving a gap in the1995 3D volume. The missing volume wasfilled in by undershooting—a source vesselshoots to a separate receiver vessel posi-

■■Difficult field development. Complex faulting of the Gullfaks reservoir structure presents a challenge to reservoir management.

11. Beckett C, Brooks T, Parker G, Bjoroy R, Pajot D, Tay-lor P, Deitz D, Flaten T, Jaarvik LJ, Jack I, Nunn K,Strudley A and Walker A: “Reducing 3D SeismicTurnaround,” Oilfield Review 7, no. 1 (January1995): 23-37.

12. Petterson O, Storli A, Ljosland E and Massie I: “TheGullfaks Field: Geology and Reservoir Develop-ment,” in Buller AT, Berg E, Hjelmeland O, Kleppe J,Torsæter O and Aasen JO (eds): North Sea Oil andGas Reservoirs—II. London, England: Graham &Trotman (1990): 67-90.

40 Oilfield Review

Lowcoverage

Low coverage

1985 Attribute Map 1995 Attribute Map (uncalibrated)

1995 Attribute Map (calibrated)Calibration Function

■■Compensating for coverage differences between baseline and monitor surveys. The1985 Gullfaks survey, acquired before oil production, was able to achieve full coverageof the top of the reservoir (top left). For the 1995 survey, seismic vessels had to undershootthree platforms, leaving gaps in the 1995 3D volume (top right). If the overburden is notaffected by reservoir production, then comparison of the two surveys allows computationof a calibration function (bottom left), which, when applied to the 1995 data boosts theamplitudes to those that would have been recorded had the platforms not been present(bottom right).

1985 Baseline Survey

1995 Monitor Survey

■■Tracking the top of the Tarbert. The 1985 baseline survey (top) was interpreted for thereservoir top and all associated faults. The 1995 monitor survey (bottom) was interpretedfor the same feature.

2

0

-2

-4

App

aren

t pol

arity

-1 1 20Factor

Am

plitu

de

0

2

3

1

-2

-1

Factor-1 0 1

Oil

Gas

Water

Nonreservoir

■■Attribute space plots showing bad andgood correlation between reservoir proper-ties and attributes. Reservoir property val-ues from well data are color-coded dotswith oil in red, gas in yellow, water in blueand nonreservoir in green. Values are plot-ted as points in the space defined by the“apparent polarity” attribute on the verti-cal axis and on the horizontal axis anattribute related to a principal componentof a polynomial function fit to the seismictrace (top). This pair of attributes does notshow good correlation with reservoir prop-erties: members of the nonreservoir classare mixed with those of the oil and waterclasses. A similar plot made with traceamplitude shows good correlation, andbetter separation of classes (bottom).

Winter 1996 41

tioned on the other side of the platform—but the acquisition geometry, and thereforethe raypaths and seismic energy reflected atthe target, were different from the rest of thesurvey (previous page, top left).

Researchers are testing ways of compen-sating the undershot data for the differencein the amount of energy reaching the target.One method that appears to give goodresults is to assume that across the surveyarea the overburden does not change withtime. This translates into the constraint thatthe total energy contained in the seismictrace from the seabottom to the top of thereservoir be constant from one survey to thenext. A calibration function, or match filter,can be applied to the low-energy 1995 datato fulfill this constraint.

Interpretation InnovationsThe goal in interpreting seismic monitoringdata is to extend reservoir knowledge atwell locations into the interwell volume—topredict reservoir properties where there areno wells. The Gullfaks well data used forthis purpose consist of basic rock and fluidinformation—whether the reservoir sand ispresent, and if so, the type of fluids present.

To extend this information away from thewell into the reservoir, first the reservoir vol-ume, especially the top surface, must beaccurately interpreted from the 3D seismicdata. In the case of Gullfaks, this surface isthe top of the high-porosity Tarbert sand,and the interpretation includes all faults thatintersect it (previous page, bottom left). Thetop picked in 1985 is a positive-polarityreflection, and the same feature was inter-preted in the 1995 data. Automatic horizon-picking software is used to achieve therequired accuracy and avoid inconsistenciesintroduced through manual interpretation.

The second step is to characterize the seis-mic data, at or surrounding the well loca-tion, that correlate with the information onreservoir properties at the well. The seismicinformation is captured in attributes. For theGullfaks interpretation, at least 20 differentinstantaneous and volume attributes werecomputed for the Tarbert sand.

Next, the attributes are examined for cor-relation with a number of classes of reser-voir properties. Four classes were identifiedin the Gullfaks pilot area: 1) oil-filled Tarbertsand; 2) water-filled sand; 3) gas-filled sand;and 4) nonreservoir. Correlation of a classwith a set of attributes is determined by thecloseness with which members of the classare located in a multidimensional clusterplot with the attributes as axes (previouspage, right). The multidimensional spacethus created is called attribute space. Whenmembers of a class lie close together, or thecluster is tight, they correlate well with thatcollection of attributes. The class membershave a distribution in attribute space thatcan be quantified statistically.

In addition to mutual proximity, the mem-bers of a class must also be well separatedfrom members of other classes. Severalcombinations of attributes must be tested toobtain a set of attributes that are sensitive tothe class properties and able to distinguishthe classes from one other (above).

To find this correlation between well dataand seismic attributes, only seismic datanear wells have been used. In a manner ofspeaking, the seismic data have now been“trained” to recognize desired reservoirproperties, perhaps in the way a person maylearn to relate sounds or letters to words.

Attributes may be thought of as the lettersneeded for the spelling of words—the alpha-bet of a language, and so can be used to dis-criminate between different words with dif-ferent spellings or sounds. When the rightcombination of letters yields the desiredwords, the next step is to hunt through theentire volume of sound for other groups ofsounds that resemble the words. For theGullfaks seismic interpretation, when theoptimum set of attributes has been found,that next step is classification of the attributesurfaces according to the four classes ofreservoir properties.

Several methods in multivariate statisticalanalysis have been tested to achieve classifi-cation. Some techniques, labelled “unsuper-vised” classification, do not require trainingwith log data, but simply look for correla-tions within the seismic data. The number ofclasses to find must be specified. “Super-vised” classifications use log constraints inan initial classification and make assump-tions about the statistical distributions ofclass members in attribute space. Classifica-tion results from different methods will besimilar if the statistical assumptions made bythe methods are not violated.13

Am

plitu

de

Polarity

Phase

Amplitude

Polarity

Amplitude

Polarity

Phase

Am

plitu

de

Polarity

■■Creating attribute-space plots. In thisexample, the goalis to classify theattributes in theareas marked bythe triangle, squareand circle. Eachshape containsmany data points,plotted as pointswith that shape onthe correspondinggraph. When onlytwo attributes (top)are examined—amplitude andpolarity—the result-ing 2D plot fails todistinguish squaresfrom triangles.When threeattributes—ampli-tude, polarity andphase—are used(bottom), all threeshapes can be dis-tinguished becausethe phase attributenow distinguishessquares from trian-gles. On real seis-mic data, any num-ber of attributesmay be examined.

13. Two unsupervised classification methods weretested—competitive learning, sometimes called neu-ral network classification, and K-mean, or nearestneighbor classification. The supervised methodstested were Bayesian and Fisher. In the case of theGullfaks data, neural network classification wasused, since the assumptions of the other methodswere violated. For more on the methods: Johnson RAand Wichern DW: Applied Multivariate StatisticalAnalysis. Englewood Cliffs, New Jersey, USA: Pren-tice Hall, 1992.

42 Oilfield Review

Final results of classification show how thefluid distribution has changed in the Tarbertsand over the course of nine years (above).The fluid distribution changes inferred from4D seismic monitoring are in agreementwith the expected drainage in the area. Sim-ulated fluid saturations for 1985 and 1995were extracted from the drainage simulationmodels, which have been history matchedto the well data (right).

These simulated saturation distributionsshow the same general features as the seis-mic interpretations. Oil has been drainedfrom the westernmost fault blocks, and fromthe western edges of the two central faultblocks. A fault in the northern portion of thecenter block appears, however, to be isolat-ing oil and gas from the southern part of thefault block, where the oil sweep appears tobe effective.

Based on the positive results of the 4Dseismic monitoring study, two major deci-sions have been taken toward modifyingGullfaks development. First, a new 3D sur-vey was acquired to cover the rest of thefield in 1996, together with simultaneouslogging of pressure and saturation distribu-tion in key wells. The new 3D survey alsocovers satellite fields to the south and willbe the baseline survey for future monitoringin those fields. Second, a new extended-reach well is being drilled from the C Plat-form in the eastern part of the field, whereproduction from zones immediately belowhas declined. The trajectory of the well isdesigned to tap multiple compartmentsidentified by the 4D survey as containingbypassed oil.

1985 Fluid Distribution 1995 Fluid Distribution

Oil Gas Water Nonreservoir

■■Fluid distributions before and after ten years of oil production. The best classification, obtained by analysis of 14 attributes, shows howfluid distributions have changed between the 1985 survey (left) and the 1995 survey (right). Overall, oil (red) has been replaced bywater (blue) in the lower, western sections of each fault block. This indicates a relatively smooth sweep. In some areas, however, suchas in the northern part of the middle fault block, oil and gas (yellow) appear to be in a separate compartment that has not been tapped.

1985 Saturation Distribution

1995 Saturation Distribution

Oil saturation, %0 100

■■Fluid saturationdistributions fromreservoir simula-tion. Simulations ofthe 1985 (top) and1995 (bottom) satu-rations show sweeppatterns similar tothose imaged withthe 4D seismicmonitoring. Redindicates high oilsaturation.

Winter 1996 43

Now that Statoil has confirmed the feasi-bility of 4D monitoring in the prime condi-tions of the Tarbert formation in the Gullfaksfield, other more challenging applicationsawait. Deeper reservoir layers with lowerporosity might also be candidates for moni-toring, pushing the technique to its limits.

4D Monitoring Will Change with TimeMany operators and service companies aretalking about and gaining experience with4D seismic monitoring. The technique isexpected to decrease uncertainty in reser-voir models and reduce risk in drilling newwells. With faster and improved-quality 3Dseismic data acquisition, the investments ina 4D seismic monitoring survey havebecome low enough to be offset by theexpected increase in production associatedwith a better understood reservoir. But 4Dseismic monitoring is still in its infancy: 3Dseismic technology took ten years tobecome established. More field studies andfurther developments are needed to provethe value of additional knowledge broughtby 4D monitoring.

Further work is required in many areas tobring this extraordinary technique into ordi-nary practice. Repeatability of acquisitionmay never be perfectly realized, but the lim-its and tolerances of acquisition differencesshould be better appreciated. There willprobably not be a single acquisition schemethat will have universal application for 4Dmonitoring, but rather a range of solutionsto cover a spectrum of different reservoirs.

Some of the failures of early attempts at4D monitoring have been attributed to the“incidental” nature of time-lapse surveys—most 3D surveys are acquired without anyintention of comparing the results to a latermonitor survey. With 4D monitoring on thehorizon, there is great interest in making theextra effort to ensure that baseline surveyshave all the information imaginable toextend their value as long as possible.

More research and experimentation withpermanent sensors, both in ocean-bottomcables and in boreholes, will advanceknowledge of hardware limitations andbring down the costs associated with theiroccasional use today.

Work remains to be done on understand-ing the relationships between seismicattributes and rock and fluid properties. Cur-rently, finding the right attributes for adesired reservoir property is a time-consum-ing interpretation project requiring anexpert. Forward modeling may help predictwhich attributes are useful for a given fluidchange or rock type, and might allow someautomation of the interpretation process.

Central to integrating the efforts of geolo-gists, geophysicists, petrophysicists and reser-voir engineers is the model—a commonearth model—that can be tapped and refinedat every stage of reservoir management(above). Continued refinements to 4D seis-mic monitoring will be driven by the projectteams charged with optimizing recoveryfrom existing fields. These teams requiremethods that allow them to improve thelocation and timing of development drilling,to not only understand but also control reser-voir behavior. With that as a goal, seismicmonitoring may one day become the mostpowerful of reservoir management tools.

—LS

Classification System

Petrophysical Modeling

Reservoir Simulations

Well Logs

Common Earth Model

4D Seismic Data

Seismic Modeling

■■Central role of the common earth model in the many disciplines contributing to 4D seismic monitoring.