Bayesian DHI using passive seismic low frequency data
Published in
SEG Houston 2009
Date of publication
29 October 2009
Abstract
We present a procedure for producing a Bayesian DHI for low frequency passive seismic (LFPS) data. The approach utilizes two LFPS attributes to classify and determine the likelihood of hydrocarbon existence in the subsurface. The attributes are based on strength and variability of the empirically observed hydrocarbon tremor. An improved, more robust tremor energy measure based on the temporal characteristics of the signal is presented and used. Bayesian classification is employed both to accommodate uncertainties in the data and to provide a risk estimate.
The process was tested over four fields with known surface projection of the oil-water contact (OWC). Prediction results correlate well with reservoir locations. Accuracy and significance of results will be discussed along with possible extensions. The approach provides a rigorous method for producing hydrocarbon probabilities based on LFPS data.
The process was tested over four fields with known surface projection of the oil-water contact (OWC). Prediction results correlate well with reservoir locations. Accuracy and significance of results will be discussed along with possible extensions. The approach provides a rigorous method for producing hydrocarbon probabilities based on LFPS data.
