Using Seismic Attributes

Slide 11

• Why are seismic attributes important?
• Our increasing reliance on seismic data requires that we extract the most information available from the seismic response
• Seismic attributes enable interpreters to extract more information from the seismic data
• Applications include:
  • hydrocarbon play evaluation,
  • prospect identification and risking,
  • reservoir characterization, and
  • well planning and field development

Slide 12

• There are several classes of seismic attributes
• The more common attributes are calculated trace-by-trace; they are single-trace attributes
• Single-trace attributes can be measurements for
  • a certain loop ( loop = a peak or a trough)
  • a specific time interval (e.g., from Horizon A to Horizon B)
  • a series of successive data samples – the instantaneous attributes

Slide 13

• Another class of seismic attribute involves the simultaneous analysis more than one trace
• For example, an interpreter will map (correlate) a peak or trough through an area
• Software can then calculate two attributes
  • the dip of the interpreted horizon (in msec/trace)
  • the azimuth – the compass direction of the maximum dip direction (e.g., 15° East of North)
• Coherency is a popular multi-trace attribute in industry
  • it is a measure of similarity between a window of one trace relative to its neighboring trace or traces
  • we discussed coherency in the structural lecture (Unit 10)
• it is a volume-based attribute – not needing an interpreted horizon

Slide 14

• This is a display of the DIP attribute calculated along an interpreted horizon
• Low dips are light; high dips are dark
• Two types of geologic features are revealed:
  • linear segments of high dip associated with fault offsets of the horizon
  • curvilinear segments of high dip associated with channels and other stratigraphic features
• How do we separate structural and stratigraphic features?
  • structural features are consistent over many time slices
  • stratigraphic features change rapidly from one time slice to the next (shallower or deeper)

Slide 15

• Seismic attributes serve two basic purposes:
  1. Qualitative evaluations
     • checking seismic data quality – identifying artifacts (non-geologic features) in the data
     • performing seismic facies mapping to predict depositional environments
  2. Quantitative analyses
     • using well data to provide calibration, we find an equation that uses one or more attributes to predict a rock or fluid property, such as:
       • Reservior thickness
       • Lithology
       • Porosity
       • Type of fluid fill (water, oil, gas)
• The next few slides will show some examples

Slide 16

• This slide outlines some of the things we can use qualitative attribute analysis for to terms of data quality
• We can identify such things as:
  • Acquisition gaps, Inline-parallel striping
  • Multiples, migration errors, incorrect velocities
  • Improper amplitude and phase balancing
  • Frequency attenuation
  • Artifacts due to the overlying geology (e.g., shallow gas, channel)

Slide 17

• Here is an amplitude-based attribute map at a level about 40 ms (10 data samples) below a shallow water bottom
• Note the east-west trending “stripes”
• This is an acquisition artifact associated with a multi-streamer boat collecting the data by sailing east-west and west-east
• These ”stripes” tend to “heal” with depth; they may not impact our analysis of a deep reservoir interval

Slide 18

• This is the same attribute that was displayed on the previous slide, but at a depth of 1000 msec (1 sec) below the seafloor
• The east-west “stripes” are still present, but at a much reduced level
• This demonstrates how the acquisition ”stripes” tend to “heal” with depth

Slide 19

• Another qualitative applications is seismic facies mapping
  • Facies are packages of rocks that …
  • Seismic facies are …
  • Environment of Deposition (EoD) can be …

Slide 20

• You will be doing a simple seismic facies mapping exercise
• This seismic line has been flattened (datumed) on the green horizon
• This removes post-depositional tilting
• Our interest is at the orange horizon
• We want to determine where there is good reservoir rocks below the orange horizon with good seal rock just above it

Slide 21

• This is the conceptual model for deposition during the period we are interested in
• This area was characterized by nearshore to offshore deposition
• Note at the orange horizon
  • Below the horizon – there are fluvial and nearshore deposits
  • Above the horizon – there are offshore shale deposits

Slide 22

• At any location:
  • We could have poor to good reservoir quality (sand vs. silt and shale, reworking by waves, etc)
  • We could have fair to excellent seal quality (marine shales diluted with some to no silt)

Slide 23

• These units were deposited in an environment like this – a barrier island
• The nearshore (beach) sands would be great reservoir rocks (medium to course sand with good sorting)
• Lagoonal muds and offshore shales would be good sealing lithologies

Slide 24

• Some seismic models were generated for this area
• The pulse is called quadrature phase – similar to a minimum phase response
• The orange horizon occurs at a zero-crossing
• Attribute of the peak above the orange horizon indicate the properties of the seal
• Attribute of the trough below the orange horizon indicate the properties of the reservoir
  • a strong (high positive amplitude) peak over a strong (high negative amplitude) trough is what we want to find
  • if the peak (trough) is moderate or low amplitude, it indicates less favorable seal (reservoir)

Slide 25

• So the objective of the exercise is to identify areas where good-quality seal rocks overlay good-quality reservoir rocks
• You are given:
  • Two seismic attribute maps
  • Orange time structure map
  • Depositional model and seismic response
  • Tracing paper and pencils

Slide 26

• Here again is the key – where is a strong peak followed by a strong trough centered on the orange horizon?

Slide 27

• We have seen a few qualitative applications of seismic attributes
• Now we will consider some more quantitative applications

Slide 28

• There are several requirements to perform a quantitative attribute analysis
• Some are related to the seismic data; others relate to calibration data from wells
• In terms of the seismic data:
  • The data processing should be Controlled Amplitude and Controlled Phase
  • Data quality should be high and checked by doing some reconnaissance
  • All wells should be tied to the seismic data (synthetics) and the results should be high-quality ties
• In terms of calibration:
  • There should be sufficient well control so that variations in rock or fluid properties can be related to variations in one or more seismic attributes
  • We can use 1-D models to give more calibration points (e.g., if the well has a 50 m gas zone, we could model the seismic response and attribute characteristics of a similar gas sand that is 100 m or 25 m thick)

Slide 29

• We will go through a quantitative exercise together
• Our goal is to build a correlation between seismic attributes and sand thickness
• If we can do this, it will allow us to predict areas of high reservoir producibility.
• We will use modeled seismic and well data

Slide 30

• Our modeled reservoir is shown on this slide
• There are two high sand-percent intervals (high Net:Gross) separated by a marine shale

Slide 31

• We can extract from the reservoir model the sand % and how the sands are stacked at various locations
• This slide shows 3 of the 9 locations we will use to calibrate seismic attributes to average sand thickness

Slide 32

• Here we have 6 cross-plots – each has sand thickness on the vertical axis and a seismic attribute on the horizontal axis
• The attributes for the top row: maximum time duration and maximum amplitude
• The attributes for the middle row: average time duration and average amplitude
• The attributes for the bottom row: minimum time duration and minimum amplitude

• QUESTION: Of these 6 attributes, does any show a simple trend (linear is nice) so that from the attribute value we can predict sand thickness with a fair degree of confidence?
• ANSWER: There is one – average amplitude – has a simple, near-linear trend

Slide 33

• This is the “well behaved” attribute plotted against sand thickness
• From the last slide, the “well behaved” attribute is average amplitude
• A linear regression was obtained (an equation) that fits the data fairly well (R2 value of 0.87)
• As an example of how we can use this, if at a given location the attribute value is 100, then we would predict the sand thickness to be 150 ft (from 100 on the X axis, go up to the yellow line and then look across to the Y value at that level)

Slide 34

• Here is the input seismic attribute – average amplitude
• It varies from 55 to 145 amplitude units
• We can put the amplitude values into our equation and predict the sand thickness

Slide 35

• Here is the result of using this equation
• Since the equation had only one attribute and a linear relationship, the colors are the same
• HOWEVER, the values are now predicted FEET of sand

Slide 36

• Next we will review an example of a quantitative attribute analysis using real data
• This study is for an oil field from onshore Alabama
• The reservoir is a carbonate interval called the Smackover formation
• In some locations, the upper Smackover is porous and contains oil; elsewhere it is tight (no porosity)
• We want to use seismic data to predict where the Smackover is porous

Slide 37

• Here is a seismic line that connects two well locations
• The well on the left has porosity (and oil); the well on the right has no porosity
• NOTE the black reflection cycle (a trough) associated with the upper Smackover
  • on the left, it is not as black (not as negative) as it is on the right
  • also, on the left there is a thin interval of white that dies out to the right
• These are subtle differences that may be related to the presence/absence of porosity
• It would be hard to visually use these observations; but seismic attributes make it relatively easy

Slide 38

• We can check to see if some attributes are sensitive to the amount of porosity
• Here we have a “real” well and two “pseudo-wells”
  • The real well had a 10 foot thick porous zone
  • We edited the well data to give us two “pseudo-wells” (modifications of a real well)
  • On the left is a pseudo well with 16 feet of porosity
  • On the right is a pseudo well with 3 feet of porosity
• We also show a modeled trace for all three
• NOTE how the trough varies in amplitude and shape as the thickness of the porous zone changes
• Using seismic attributes, we can capitalize on these subtle variations

Slide 39

• Here is a cross-plot in which:
  • The horizontal axis is porosity thickness (data points are only from actual wells)
  • The vertical axis is our predicted porosity thickness from an equation that uses 4 seismic attributes as terms (each attribute has a different weight)
• A best linear fit was derived (mathematically) and 95% confidence intervals are shown
• We could use pseudo-well to add more calibrations points, but that was deemed unnecessary for this case since we have 8 wells with a good spread of values

Slide 40

• We use the equation (based on 4 attributes) to derive a map of predicted feet of porosity throughout the area
• Hot colors are where we predict the thickest porosities exist
• There are some “edge effects” near the boundaries of the survey that have to be ignored
• The zoomed in area gives a high level of detail for a known producing zone
  • We can use this detail to position additional production wells
• This map was also used to identify other drill locations for near-field wildcats

Slide 41

• This slide lists some attribute pitfalls and some of the possible solutions/remedies

Slide 42

In summary ...