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
Bayesian DHI Using Passive Seismic Low Frequency Data
(1) Spectraseis AG, Zurich, Switzerland.
We present a procedure for producing a Bayesian DHI using low frequency passive seismic (LFPS) data. The approach utilizes two LFPS attributes to classify and determine the likelihood of hydrocarbon presence in the subsurface. These attributes are based on statistical characteristics of the empirically observed hydrocarbon (HC) tremor. It is shown that these characteristics provide a more accurate and complete description of the tremor energy as compared to an integrated single value measure. An interpreter-driven Bayesian classification is employed both to accommodate uncertainties in the data and to provide a risk estimate. The class models are built from a subset of exemplar receivers which are selected by an interpreter based on tremor quality and low noise interference. Prior knowledge from wells or structural information from active seismic can also be incorporated into the analysis.
The process is tested over four fields with known surface projection of the oil-water contact (OWC). Prediction results correlate well with reservoir locations. A classification success rate based on the proposed process is calculated. Due to the relatively small number of measurement locations (~50-100), the significances of the results are checked through standard statistical tests including Monte Carlo simulations.
The approach provides a rigorous method for producing quantitative HC probability maps that are easy to interpret and can be used for risk analysis. Possible applications for the method include: (1) more informed drilling decisions over fields with none or poor active seismic data, (2) expanding production into areas near existing wells (exploitation), (3) interpretation aid for ambiguous features in conventional seismic attributes.