Interpretation of low-frequency seismic sensing includes:
- Analysis of anomaly’s stability during the day;
- Typification of spectra;
- Cluster analysis;
- Calculation of predicted frequencies;
- Comparative Analysis;
- Numerical modeling;
Analysis of anomaly’s stability during the day. Stability of the anomaly during the day is studied here using the method of recording the temporal variations of the anomaly’s “signal-to-noise” parameter.
Fig.1. Daily variations of anomalies’ parameters (amplitude)
Typification of spectra. Visual assessment of the type and quality of the spectra;
Cluster analysis of the parameters of spectral anomalies (frequency, width and "quality" of anomaly), which enables to:
1) visually assess the distribution of parameter values that form the cluster;
2) separate stable spectral anomalies over the survey area from random ones;
3) automate the generation of the physical fields of the values of anomalies’ parameters;
4) numerically compare the parameters of anomalies recorded on different territories.
Fig. 2. Clustering of anomaly parameters
Calculation of predicted frequencies is carried out in accordance with the results of cluster analysis based on the well-velocity survey and VSP data on the distribution of longitudinal velocities in the section. As a result of comparison of the actual anomaly frequency with the set of predicted frequencies attributed to stratigraphic horizons, possible interval of oil pool occurrence in the section is determined.
Fig. 3. Relationship between the spectrum shape and the depth of an oil-bearing horizon
Comparative analysis of the acquired spectra with the spectra from the territories with proven oil bearing capacity as per deep drilling data;
Numerical modeling of the seismic waves’ distribution in the geological environment is carried out in order to calculate the synthetic spectra of the microseismic vibrations assuming both absence of the oil saturation in the section and the presence of oil saturation in one or more target horizons. Acquired synthetic seismograms are compared with the field data which allows determining the most probable type of hydrocarbon accumulations in the section.
Fig. 4. An example of wave distribution in the environment using numerical modeling
Mapping and comparative analysis of the spatial distribution of anomalies’ parameters for evaluation of oil potential of the objects in question.
Fig. 5. An example of the resulting map of oil prospectivity as per LFS