Multiple Removal Technology
PGS has lead the seismic industry developing sophisticated multiple removal technologies for various acquisition strategies and for different geological environments, having now established a powerful demultiple portfolio.
Various developments over the last few years are described below:
For years geologists and geophysicists have struggled with the challenge of removing surface multiples from seismic data during processing, and with highly varying degrees of accuracy and success. Now, improved image quality with virtually no multiples can be achieved through 2-D Surface Related Multiple Elimination (2-D SRME) from PGS data processing.
Accurate Prediction and Multiple Removal
2-D SRME is a fully data-driven tool that relies upon spatial convolutions to accurately predict and remove surface multiples. Since 2-D SRME is fully data-driven, no subsurface model assumption is required, and good amplitude preservation is assured.
2-D SRME Benefits:
Comparison of deep water input (left) vs. 2D SRME result (right).
Comparison of shallow water input (left) vs. 2D SRME result (right).
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The latest 3-D SRME demultiple technology is now available from PGS data processing. 3-D SRME offers a full three-dimensional, data-driven wavefield solution to multiple prediction and removal.
3-D SRME Benefits:
- Superior amplitude preservation
- Completely velocity independent
- No a priori model required
- Limited user interaction
- Accurate prediction of multiples from complex 3-D subsurfaces
Examples of comparisons between 2D and 3D SRME:
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Observation:
A set of predicted multiples can be very complex because all different orders of multiples are predicted at once.
Aim:
To constrain the adaptive subtraction process in SRME processing.
Method:
By identifying frequency- or dip trends at target level, predominant multiples can be selected, to optimize the adaptive subtraction.
Frequency constrained adaptive subtraction
Dip constrained adaptive subtraction
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In theory, SRME is an iterative process. In practice, very often only the first iteration is used. The first iteration predicts the phase characteristics of the multiples quite accurately, but is not able to predict the amplitudes correctly. In the first iteration, the input data itself is used as estimate of the multiple-free data. In the second iteration, the multiple-free data from the first iteration is used. Because there is a better estimate of the multiple-free data in the second iteration, the amplitudes of the predicted multiples are more closely balanced with the amplitudes that are present in the input data. As a result, the adaptive subtraction has fewer difficulties matching the amplitudes of the predicted multiples with the multiples in the input data. This may lead to a better removal of multiples. PGS now offers further iterations of all its SRME processes.
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Historically, interbed multiples have been some of the most difficult multiples to remove. Recent developments by PGS have now extended the SRME technique to interbeds:
- No a priori subsurface information is used
- User interaction is limited to the identification of the multiple generator only
- Integrated visualization tools (holoSeisTM) can contribute to an increased efficiency of the multiple identification process
- Performance and speed is comparable to free-surface SRME
For an arbitrary interbed multiple:
Interbed multiples are predicted by (cross) convolving (muted) common shot gathers with (muted) common receiver gathers in time and space.
The multiple generating interface may be interpreted inholoSeisTM (left).
This approach significantly reduces processing turnaround and cost.
A Middle East example of IME is on the right.
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There are a number of High-Resolution radon demultiple algorithms on the market. However, the improved AVO preservation can be compromised by strong multiples and the high-resolution transform can introduce smearing of energy.
With the PGS Enhanced HR-Radon, the smearing of energy is strongly suppressed and amplitude preservation is improved – even with strong multiples.
Amplitude Preservation – Strong Multiples:
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Seismic data collected on land presents a unique set of demultiple problems. In addition to the use of an extensive suite of specialist demultiple algorithms, PGS has led the market in the use of SRME, IME and TOAST on land data.
TOAST – application to interbed multiples in land data:
SRME/IME – Application to land data:
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