BroadSeis
Enhancing Reservoir - AVO & Inversion
Benefits from BroadSeis for Reservoir Characterization:
- BroadSeis is AVO compliant
- No need for log-derived low frequency model and is therefore:
- More reliable when well data are sparse
- Less likely to yield biased estimates of absolute acoustic impedance
- Improved inversion quality and well tie
- Better delineation of thickness of gas-bearing zones
- Reduced uncertainty in lithology prediction
derived from well logs is not required
Improved Low Frequencies Remove the Need for Well Logs for Quantitative Inversion
The broad bandwidth of up to 6 octaves (2.5 Hz - 160 Hz), achieved using BroadSeis acquisition and processing, translates into more accurate and quantitative seismic inversion. The lack of low frequencies in conventional seismic data means that a low frequency model must be incorporated into the inversion process. With BroadSeis data, high-resolution NMO-derived seismic velocities are used to define the low frequency model in the 0-5 Hz range, while the reflectivity provides information from 2.5 Hz. This means that quantitative inversion can be performed, even in areas without existing well information
Case Study from Northwest Australia
In this example from northwest Australia, the inversion of BroadSeis data shows greater dynamic range and sharper boundaries than that from conventional data. There is an excellent tie with the well logs, which were not used in the inversion process. Both the conventional and BroadSeis inversions started from the same initial model.
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Sw Ip |
Ip |
Vp/Vs |
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quantitative estimates of elastic attributes.
Lithology Prediction
The elastic parameters derived from the seismic inversion were used to predict the lithology using LithoSI. In this technique, a supervised Bayesian classification is performed to produce probability cubes of the predicted lithology or rock properties.
| Crossplots of Ip and Vp/Vs | ||
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Well Data |
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BroadSeis |
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The gas sand prediction from the BroadSeis data matches the well data much better than the conventional data. This indicates a considerable reduction in the uncertainty of the lithology prediction, which is also shown in the cross-section below, where the BroadSeis data gas prediction matches the well logs with a high degree of probability. Note that the BroadSeis data also indicates a high probability of gas sands in a discrete volume between wells A and B, which is not apparent on the conventional data.
Gas Sand Probability
Cascaded LithoSI lithology classification using Ip and Vp/Vs from the inversion shows that
BroadSeis provides a better match with lithology logs from the wells and
reduces uncertainty in the prediction of the gas sands
