UM3_SeismicWellTie.ppt
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Transcript of UM3_SeismicWellTie.ppt
![Page 1: UM3_SeismicWellTie.ppt](https://reader034.fdocuments.in/reader034/viewer/2022051218/55cf85f9550346484b93577c/html5/thumbnails/1.jpg)
Establishing Well to Establishing Well to Seismic TieSeismic Tie3
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Objectives
Establish Well-Seismic Tie
- Determine key marker that need to be interpreted
- Understand seismic response in respect to the wells
- Determine the seismic phase
- Check the log quality
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Convolution Model & Polarity Convention
European Normal European Reverse (SEG Normal)
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Seismic Bandwidth and Phase
• The seismic trace is composed of energy that has a range of frequencies.
• The seismic wavelet is the result of addition of the sin wave functions at each frequency
• Amplitude and phase spectrum are another wavelet representation
Phase Spectrum Amplitude Spectrum
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Seismic Bandwidth and Earth Filter
• It is a characteristic of the seismic data that the bandwidth of the input signal is modified by earth filter
• Thus shallow targets will generally be characterized by good bandwidth whilst deeper targets will have poorer bandwidth
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Zero Phase – Minimum Phase Wavelet
European Normal European Reverse (SEG Normal)
Zero Phase Wavelet Two Minimum Phase Wavelets
• Sound source (explosive, airgun) has minimum phase wavelet
• Most wavelet extracted from seismic data have mixed phase
• Minimum phase is a condition of a wavelet rather than a description of its shape. Thus this wavelet is NOT desirable for interpretation
• Minimum phase is used to describe a ‘causal’ wavelet (ie: no energy before zero) in which the phase is closest to zero but which displays the most rapid build up of energy.
• Thus there is a unique minimum phase wavelet for a given amplitude spectrum
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Zero Phase – Minimum Phase Wavelet
Illustration of phase rotation in respect to zero phase wavelet
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Wavelet Shape Changes Due To Earth Filter
-120o phase rotated zero phase wavelet
Zero phase wavelet
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Idealized Wavelet
Question:
Which idealized wavelet is suitable for these two different amplitude spectrums….?
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Effect of Various Processing Step to Wavelet Shape
Processing Steps Affecting Wavelet Shape (Courtesy R.E. White)
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Zero Phasing and Wavelet Shaping
Whitening (broadening of frequency spectrum)
Zero Phasing
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Well-Seismic Tie
Log Calibration
Vp (Sonic
)
d (Densit
y)
RC AI (Vp.d)
*
-------------------V2d2 – V1d1V2d2 +V1d1
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Wavelet Estimation
20 ms
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Wavelet Validation
Consistent Wavelet
1
Wavelet MUST have center of energy
2 3
Synthetic match beyond interval of
interest
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Log Calibration
Objective:
To calibrate sonic transit time with travel time from checkshot in order to match with the seismic
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Log CalibrationCalibration Methods:
- Linear trends with knee points
- Spline or polynomial
Resolving differences in seismic and sonic travel times
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Quantifying Well Tie
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Reasons For Poor Well Tie
1. Different propagation paths for sonic and seismic
2. Relative scales of measurement
3. Frequency of measurement
4. Spatial sampling
5. Error in seismic migration
6. Problem in log measurement (eg: invasion, cycle skipping)
7. Bad checkshot value
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Seismic Resolution and DetectionTemporal vertical resolution (tuning tickness):
- Separation by the seismic method of two features which are close together (1/4 wavelength of the signal)
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Vertical resolution in respect to depth
T = Seismic period (peak to peak or trough to trough
Fd (Dominant frequency) = 1/T * 1000
Tr (Temporal Thickness) = 1/2.31 Fd * 1000
Vertical Resolution = Tr*Vp/2000
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Which sand is resolved?
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Seismic Response on different geologic boundary
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What is the resolution of this seismic data?
Seismic
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Temporal Resolution
1. At ‘A’ the two reflections are solved
2. As the units is thinned the two reflection begin to interfere resulting in a composite response that progressively increases in amplitude to the tuning thickness at ‘B’
3. Progressive thinning of the unit results in destructive interference and the composite decreases the amplitude
4. The reflections never get much closer together in time than the tuning thickness
5. For thickness below ‘tuning’ the time separation measured from the trace would greatly over-estimated the thickness
6. Interestingly, the amplitude in the zone below tuning are approximately linearly related to the actual time thickness of the bed
7. Between A and B the time separation is more or less a reasonable guide to thickness
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Example Of Tuning
Phenomenon
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Thickness Prediction from seismic
Thickness above tuning:
(time thickness (ms)/2000 * (Vp)
Thickness below tuning:
(Utilizing plot between amplitude and temporal thickness) /2000 * Vp
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Lateral Resolution
Key factors: migration aperture, geometry, fold and sampling
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Resolution
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The Danger Of Un-corrected Sonic Log
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Resolution