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    Introduction to Seismic

    InterpretationStratigraphic

    Interpretation

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    Contents AVO

    Seismic attributes

    Time-lapse (4-D) seismic Multicomponent (shear wave) seismic

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    Amplitude variation with offset 1. n. [Geophysics]

    Variation in seismic reflection amplitude withchange in distance between shotpoint and receiverthat indicates differences in lithology and fluidcontent in rocks above and below the reflector.

    AVO analysis is a technique by which geophysicistsattempt to determine thickness, porosity, density,velocity, lithology and fluid content of rocks.

    Schlumberger Oilfield Glossary

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    Hampson-Russell

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    The distribution of energy amongst thereflected and transmitted waves depends on the

    angle of incidence (theta) and physicalproperties of the layers above and below theinterface.

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    VS/VP and Poissons Ratio ()

    Important for amplitude-versus-offset studies

    VS = ---

    VP

    1 - 2 2

    = ------------2(1- 2)

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    AVO Principles The shear modulus of a rock does not change

    when the fluid is changed. H

    owever, the bulk modulus changes significantlywhen the fluid changes. As such, the p-wave velocity of a rock will

    change as hydrocarbon saturation changeswhereas the s-wave velocity will change

    relatively little (there is a slight densityeffect). Poissons ratio changes

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    Shuey (simplified):

    RC(U) = reflection amplitude for incident angle UNIP = Normal incidence p-wave reflection

    W 2 , W 1 - Poissons ratios for lower & upper layers respectively

    RC(U) $ NIP cos2U + (

    W 2 - W 1) sin2U(W 2 + W 1)

    21 -

    RC(U) $ NIP cos2U + (

    W 2 - W 1) sin2U(W 2 + W 1)

    2

    (W 2 + W 1)

    21 -

    Normal IncidenceReflectivity

    PoissonReflectivity

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    U= 10o

    U= 60o

    Shale

    Sand

    For shale overlying water-filled sand, there

    is little variation in reflection amplitude with

    increasing offset (blue curve). The amplitudefrom the shale/gas sand interface shows a

    strong increase in absolute amplitude of the

    trough of the reflection (red curve).

    Shuey's AVO Approximation

    using U = (U1 + U2)/2

    -0.5

    -0.4

    -0.3

    -0.2

    -0.1

    0.0

    0.1

    0.2

    0.3

    0.4

    0.5

    0 10 20 30 40 50 60 70

    Angle of Incidence, U1

    Amplitude

    Good to about 10 degrees below critical angle.

    Shale / gas sand

    Shale / wet sand

    Shuey's AVO Approximation

    using U = (U1 + U2)/2

    -0.5

    -0.4

    -0.3

    -0.2

    -0.1

    0.0

    0.1

    0.2

    0.3

    0.4

    0.5

    0 10 20 30 40 50 60 70

    Angle of Incidence, U1

    Amplitude

    Good to about 10 degrees below critical angle.

    Shale / gas sand

    Shale / wet sand

    Shuey's AVO Approximation

    using U = (U1 + U2)/2

    -0.5

    -0.4

    -0.3

    -0.2

    -0.1

    0.0

    0.1

    0.2

    0.3

    0.4

    0.5

    0 10 20 30 40 50 60 70

    Angle of Incidence, U1

    Amplitude

    Good to about 10 degrees below critical angle.

    Shale / gas sand

    Shale / wet sand

    Shale / gas sand

    Shale / wet sand

    Courtesy Chroma Energy

    Amplitude Variation with Offset (AVO)

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    Courtesy Chroma Energy

    Amplitude Variation with Offset (AVO)

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    Bright SpotBright Spot Phase ChangePhase Change Dim SpotDim SpotBright SpotBright Spot Phase ChangePhase Change Dim SpotDim Spot

    Amplitudes/DHI

    AVO AnomalyClass III Class II Class I

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    Variations in amplitude with offset approximated by:

    R() = A + Bsin2

    A slope (P in Europe)

    B gradient (G in Europe)

    Sin2 U

    |Am

    plitude|

    Slope (gradient)

    Intercept

    Sin2 U

    |Am

    plitude|

    Slope (gradient)

    Intercept

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    Gradient Back

    groundtrend

    0.20

    0.15

    0.10

    0.05

    0.0

    -0.05

    -0.10

    -0.15

    -0.20 -0.20 -0.12 -0.04 0 0.04 0.12 0.20

    R0

    Class IV

    Class III Class IClass

    II

    Gradient Back

    groundtrend

    0.20

    0.15

    0.10

    0.05

    0.0

    -0.05

    -0.10

    -0.15

    -0.20 -0.20 -0.12 -0.04 0 0.04 0.12 0.20

    R0

    Class IV

    Class III Class IClass

    II

    Core Labs

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    AVO Crossplots Intercept gradient cross-plot shows different

    fields, corresponding to different AVO anomaly

    types Gradient and intercept derived from analysis of

    pre-stack data

    Need to establish a background trend wet

    sands Castagnas mudrock line

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    For fine-grained rocks, Biot-Gassman model doesnt work well.Castagna et al. compiled available data and defined a

    mudrock line that relates Vp to Vs (velocities in km/s):

    Vp = 1.16 Vs + 1.36

    -NOTE:

    Coefficients for mudrock line vary from basin to basin

    Castagna et al., 1985

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    The mudrock line is used to predict thebackground response for wet sands on

    Gradient vs Intercept plots Deviations from the background trend are

    identified as AVO anomalies

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    A sample cross-plot (left) showing gradient and intercept

    values obtained from gathers (pre-stack). Regions are circledand assigned different colors by the interpreter: Grey is thebackground trend, yellow is the top of a gas sand, and redx corresponds to base of a gas sand.

    The software will show the location of these data points in

    the stacked section.

    Hampson-Russell

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    AVO Summary

    The most useful setting for the technique isstill for gas detection in young, shallow, porous,poorly consolidated clastic rocks.

    Despite the success, there have beensignificant failures, often due to the presenceof fizz water, sub-economic accumulations ofgas that can produce impressive AVO anomaliesbecause even small amounts of gas significantlyaffect Vp and hence Poissons ratio.

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    AVO Summary

    AVO analyses should be thought of as anadditional tool that is to be added to other

    geophysical and geological approaches.

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    Four principal attributes

    Reflection strength, instantaneous phase and instantaneous frequency arecomplex-trace attributes

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    Seismic amplitude is the basic measurement of seismic data

    Each trace consists of a time series of amplitude measurements.

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    For 8-bit data, amplitudes theoretically range from 128, 16-bit datatheoretically range from 32,768 and 32-bit data range from 4,294,967,296. 32- and 16-bit data have more dynamic range than 8-bitdata, but take up correspondingly more amounts of storage space.

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    You should work with 16- or 32-bit data when doing quantitative attributeanalyses.

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    Bright SpotBright Spot Phase ChangePhase Change Dim SpotDim SpotBright SpotBright Spot Phase ChangePhase Change Dim SpotDim Spot

    Amplitudes/DHI

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    Amplitude Not all hydrocarbon accumulations produce detectable

    amplitude changes. Not all changes in seismic amplitude are associated with

    changes in fluid saturation. Changes in lithology, bedthickness, porosity and other factors can cause changesin seismic amplitude.

    It only takes a small amount of gas to generate animpressive looking bright spot; not all are associated withcommercial accumulations of hydrocarbon.

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    Reflection strength is amplitude independent of phase.

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    Reflection strength is also sometimes referred to as instantaneous amplitude,amplitude envelope, or simply envelope.

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    Reflection strength, like seismic amplitude, shows acoustic impedancecontrasts and so is useful for identifying bright spots, tuning effects(although the maximum reflection strength occurs at a different thicknessthan the tuning thickness for seismic amplitude), interference, etc.

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    Instantaneous Phase Instantaneous phase is phase independent of amplitude,

    and its values are in degrees and range from +180 to -180.

    Because instantaneous phase contains no amplitudeinformation, it is commonly used to examine reflection(i.e., stratigraphic) continuity; changes in amplitude alonga reflection can sometimes give the impression of lateraldiscontinuity.

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    some interpreters prefer to pick horizons on instantaneous phase versions ofthe data.

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    Instantaneous frequency is the rate of change of phase. Its values are incycles/second (Hertz). Instantaneous frequency is useful for detecting tuning effects (although

    peak frequency occurs at a different thickness than for tuning of seismicamplitude), fractures, gas (see next slide) and other features.

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    The bright spots (highamplitudes) in map view at

    top correspond to lowfrequency shadows (yellowand orange) in the transectbelow.

    Both are qualitative indicatorsof gas.

    The presence of gas at thislevel is confirmed by wells

    (not shown) producing fromthis level.

    Tertiary deposits.

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    A whole seismic line/volume

    may be converted to anattribute line/volume, or anattribute may be derived andextracted with respect to ahorizon.

    Extraction along a horizon(e.g., horizon slice)

    Extraction in a windowabove/below a horizon(e.g., average amplitude)

    Extraction between twohorizons (e.g., averageabsolute amplitude, peakspectral frequency)

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    Attribute Studies In the mid-1990s it became apparent that

    empirical correlations could sometimes be found

    between seismic attributes and log-derivedphysical properties (e.g., Schultz et al., 1994),and that these correlations could be used topredict the distribution of physical propertiesaway from well control.

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    Attribute Studies Although simple linear correlations between a

    physical property and an attribute are

    sometimes found, relationships are more oftennon-linear (e.g., Hart and Chen, 2004) andmore than one attribute is needed predict thephysical property of interest

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    Sample cross-plots of a physical property (phi*h) against attributesextracted from the seismic data at the well locations (9 wells)

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    The graph at left shows no trend. The middle graph shows an inverse relationship (2nd order?) The graph at right shows a positive relationship (2nd order?)

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    Using more than one attribute (generally) gives a better correlation Using a 2nd-order polynomial gives a better result at least in this case

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    Neural networks are useful for finding hidden patterns among variables.Unfortunately they can sometimes find relationships that do not exist.

    Neural networks are particularly useful when working with non-linearrelationships, such as those that can sometimes be observed betweenattributes and physical properties.

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    Attribute study approach number 1: Horizon-based Extract physical property from wells (net sand, average porosity, etc.) Extract attributes from seismic data at corresponding TWT (i.e.,

    horizon slice) Look for correlations between seismic attributes and physical property

    at well locations Use correlations, if found, to produce a map of the physical property

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    Attribute study approach number 2: Interval-based Extract physical property from wells (net sand, average porosity, etc.) Extract attributes from seismic data in appropriate window Look for correlations between seismic attributes and physical property

    at well locations Use correlations, if found, to produce a map of the physical property

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    1 mile

    2 km

    Porosity in irregular pods; pods connectedPorosity in irregular pods; pods connected

    Patchy dolomitizationPatchy dolomitization

    Pods generally overlie basement structuresPods generally overlie basement structures

    Roll of fractures in fluid flow/dolomitizationRoll of fractures in fluid flow/dolomitization

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    Attribute study approach number 3: Volume-based Convert target log (porosity, Vsh, etc.) to time domain for all wells Look for correlations between seismic attributes and physical property

    on a sample-by-sample basis within a specific interval Use correlations, if found, to produce a volume of the physical property

    (e.g., replacing the amplitude traces with predicted porosity logs)

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    Trenton

    Rose Run

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    AttributeAttribute--Based Porosity PredictionBased Porosity Prediction

    Trenton

    Rose Run

    Porosity developed between overlappingPorosity developed between overlappingfaults of leftfaults of left--lateral system.lateral system.

    Apparent porosity at top and base is an artifactApparent porosity at top and base is an artifact

    0%

    8%

    4%

    6%

    2%

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    Bright SpotBright Spot Phase ChangePhase Change Dim SpotDim SpotBright SpotBright Spot Phase ChangePhase Change Dim SpotDim Spot

    Amplitudes/DHI

    AVO AnomalyClass III Class II Class I

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    Horizon Slices (Amplitude Maps)

    Show how amplitudes vary along a horizon (unique to 3-D)

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    Time-Lapse Seismic The seismic signature of a reservoir depends on two

    primary elements, static reservoir rock properties (e.g.,porosity and lithology) and dynamic time-varyingproperties (e.g., fluid saturation and pore pressure). Thecomparison of two or more 3-D surveys over the samearea in effect cancels the static contribution. Therefore,any observable change is due to dynamic changes of thereservoir and effects of fluid flow.

    Changes in pore fluid composition (e.g., water/gas/oilsaturation), pressure and temperature can all affect thevelocity and density of rocks. Any of these changesmight be expected when a field is being produced.

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    Time-Lapse Seismic Under the right circumstances, changes in density or

    velocity might be detectable seismically. These changesmight manifest themselves in seismic data as changes inamplitude, changes in traveltime or changes in waveform.

    Some of the technical considerations for assessing thetechnical risk of a time-lapse study include: a) porosity,b) rock compressibility, c) change in fluid saturation, d)fluid properties, e) seismic image quality, f) repeatability

    of seismic imaging. Also need to consider economics.

    G t C 4D I t t ti

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    Gannet-C 4D InterpretationDifference amplitude superimposed on topDifference amplitude superimposed on top

    reservoir depth mapreservoir depth map

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    Multi- Component Seismic

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    Some Applications

    Improved P-wave imaging Improved lithology and pore fluid prediction

    Improved imaging in areas with low P-wave impedance contracts,or high P-wave attenuation

    Structural imaging beneath gas-invaded zones

    Seismic reservoir characterization and monitoring Imaging base of salt, subsalt structure and stratigraphy

    Th D h Fi ld

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    The Doughnut FieldObjectives

    Improve imaging beneath gas

    Delineate faults to aid drilling in 99

    Acquire 3D multi-component data

    - seabed receivers

    - shear waves

    Conventional seismic

    Gas

    ?

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    RESULTS - 3D MIG COMPARISON

    PP PS

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    Top BalderTime Map, PP Data

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    Top BalderTime Map, PS Data

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    Converted-Wave Seismic Converted-wave seismic data are also collected

    on land.

    S-wave sources exist, but: a) are expensive,and b) cause environmental damage or mayotherwise not be useable in some situations

    Use converted waves

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    Timeslicethrough a p-pwave volume

    at theapproximatelevel of aCretaceouschannel.

    Courtesy CREWES

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    Timeslicethrough a p-swave volumeat theapproximate

    level of aCretaceouschannel.Note the gooddefinition ofthe channel(red/green).

    Courtesy CREWES

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    Multicomponent Seismic Issues:

    Processing: where are the common

    conversion points? Need to know p- and s-wave velocity field

    Relation to p-wave data?

    Different travel times

    Different reflectors