Print this pageAAPG GEO 2010 Middle East
Geoscience Conference & Exhibition
Innovative Geoscience Solutions – Meeting Hydrocarbon Demand in Changing Times
March 7-10, 2010 – Manama, Bahrain
Influence of 
Noise
from Passive Seismic Reservoir Detection
(1) Research & Development, Spectraseis AG, Zurich, Switzerland.
We present a numerical modeling study of the impact of surface noise on the ability to correctly detect low-frequency micro-tremors originating from the subsurface. The analysis is motivated by the need to assess the extent by which the empirically observed hydrocarbon (HC) micro-tremor can be masked by surface noise.
Subsurface micro-tremors and surface noise sources (hereon referred to as signal and noise) are placed within a p-wave velocity model of a producing field. Placement of the virtual signal and noise sources is based on known reservoir and noise locations. Signals are modeled by vertically polarized Ricker wavelets with 3Hz center frequency randomly distributed in time. Noises are modeled by either vertically or horizontally polarized white noise filtered between 1-18Hz. The sources are propagated using a staggered grid finite difference solver, and the particle velocities are recorded at ground level by virtual receivers. A series of forward simulations is run with different noise strengths to achieve various signal-to-noise ratios (SNRs). Placement and signature of the signal and noise sources are kept constant.
A spectral ratio attribute known to be indicative of hydrocarbons is computed for the synthetic results. From this attribute, the virtual receivers are classified into two groups, above HC and away from HC, using two classification methods: Jenks’ natural breaks and neural networks (NNs). The classification results from the two methods are consistent. The best performing NNs, trained on synthetic data for each noise level, are then used to generate hydrocarbon probability maps based on real passive seismic data acquired on the same producing field. These maps are compared with the actual oil-water contact (OWC). Results show that the predictions were most reliable for NNs trained on a SNR of 0.5 (or -3 dB). Predictions from NNs trained on higher or lower SNRs give inferior predictions for this dataset. A summary of the influence of noise on the ability to predict reservoir presence is provided.
This study shows that even though noise impairs the ability to correctly identify hydrocarbon related tremors, more reliable results can be achieved by applying processing methods that accommodate for variations from noise
.