The value and impact of Streaming Depth Imaging (SDI) is demonstrated using field data with imbedded synthetic signals. The real noise in the field data is required for valid testing of the method because reflectivity noise and coherent surface wave noise dominate passive data and cannot be easily modeled. These noises are suppressed using the trace filtering workflow that includes cepstral filtering and low cut frequency bandpass. The cepstral filter is the most important noise suppression filter employed. SDI fails when using traces that do not have noise suppression. However, the increase in the signal to noise is proportional to the integration time for images computed with SDI using traces that have high quality noise suppression. The combined gain in signal to noise for trace filtering and SDI is more than 25 decibels compared to images computed using the unfiltered field traces and without SDI.
Streaming Depth Imaging (SDI) uses the passive seismic traces recorded over the reservoir area before drilling and after noise suppression to image seismic emissions from the reservoir depth. Maps of the natural fractures in the rocks are extracted from these image volumes and the image volumes are used to: determine the location of fracture permeability zones before drilling; predict frac treatment performance; and predict the locations of best potential production in unconventional reservoirs along proposed well paths.
Imaging the fractures in the reservoir rock volumes using passive seismic data reveals: the zones of high fracturing in a fractured reservoir in Colombia; zones of fracture permeability in the Eagle Ford; and zones of fractures in the New Albany Shale that control the frac treatment performance. Imaging the fractures before drilling shows the zones of best production and the predicted results are confirmed by subsequent drilling.
Imaging the very weak signals emitted from the reservoir, requires the use of high quality trace filtering to suppress noise. The objective of the processing is to suppress reflectivity noise and surface wave noise without modifying the phase of the signal waveforms that are emitted at depth and travel to the receivers at the surface. Using a trace filtering workflow that includes cepstral filtering and low cut band pass, combined with SDI increases the Signal-to-Noise (S/N) ratio in the images by up to 25 dB compared to the S/N ratio for images computed using unfiltered the field data.