The high production potential of the Daqing oilfield in China is recognized for seismically thin sand bodies that usually are not resolved with conventional seismic data. The present study assesses the usefulness of applying seismic multi-attribute analysis to bandwidth extended data in resolving and making inferences about these thin layers. In thin layers, tuning can obscure relationships between seismic amplitude and rock properties. In such cases, the seismic phase varies with the layer impedance and may hence aid in reservoir characterization. A seismically derived relative geologic age may also be a useful attribute in predicting rock properties because it helps define the stratigraphic position of a layer. When utilized in multi-attribute analysis in the Daqing field, spectral decomposition amplitude, phase, and a relative geological age attribute to improved prediction of well log effective porosity from seismic data and are preferentially selected by stepwise regression. The study follows standard methodology by implementing seismic multi-attribute analysis and discusses the improvement of applying it to bandwidth extended data. This will include a combination of attributes such as relative geologic age, phase, amplitude, and the magnitude components of spectrally decomposed data.
The joint time-frequency and time-phase analysis applied to a field seismic data highlights lateral changes on preferential frequency and phase illumination at the target across secondary faults. Mutual thin-bed interference modeling suited for the case study area was performed using a well-tying well-based extracted wavelet assumed to be representative of the wavelet embedded on the input seismic data. The long coda of this wavelet is also present on the corresponding thin-bed waveform, indicating the possibility of more complex mutual interference patterns between thin beds and mutual interference at farther vertical separations between thin beds compared with what would occur for an embedded wavelet with a shorter coda. The observed lateral changes on preferential frequency and phase illumination on the seismic data are attributable to collocated lateral changes in the stacking patterns and variable occurrence of vertically adjacent thin beds, which are interpreted as lateral sediment deposition changes induced by the syndepositional activity of the secondary faults. This is a geologic scenario that had not been previously considered on the area until the evidence of this case study provide indirect support for it.
The Daqing field, located in the Songliao Basin in northeastern China, is the largest oil field in China. Most production in the Daqing field comes from seismically thin sand bodies with thicknesses between 1 and 15 m. Thus, it is not usually possible to resolve Daqing reservoirs using only conventional seismic data. We have evaluated the effectiveness of seismic multiattribute analysis of bandwidth extended data in resolving and making inferences about these thin layers. Multiattribute analysis uses statistical methods or neural networks to find relationships between well data and seismic attributes to predict some physical property of the earth. This multiattribute analysis was applied separately to conventional seismic data and seismic data that were spectrally broadened using sparse-layer inversion because this inversion method usually increases the vertical resolution of the seismic. Porosity volumes were generated using target porosity logs and conventional seismic attributes, and isofrequency volumes were obtained by spectral decomposition. The resulting resolution, statistical significance, and accuracy in the determination of layer properties were higher for the predictions made using the spectrally broadened volume.
We applied the seismic net-pay (SNP) method to an oil discovery and predicted thicknesses consistent with the actual thicknesses at the wellbore locations. This was accomplished by applying the method in a self-calibrating mode that did not require the direct use of well information. For net-pay estimation under a self-calibration scenario, the SNP method thickness estimates proved to be more accurate (mean absolute prediction error at well validation locations under [Formula: see text]) than estimates from a reflectivity-based detuning method ([Formula: see text]) or multiple linear regression ([Formula: see text]). Statistical [Formula: see text]-tests indicated that the correspondences of the predicted thickness estimates with actual net-pay values for the SNP and reflectivity methods (F approximately 5.5–6 for both) were statistically significant, whereas the multiple regression results did not prove to be statistically significant.
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