Special report: Low frequency seismic has numerous E&D applications
Published in
Oil And Gas Journal
Date of publication
26 October 2009
Abstract
Economic uncertainty, volatile oil and gas prices, and frozen capital markets, combined with growing reservoir complexity, are having a significant effect on oil and gas exploration and development, causing a renewed focus on managing risk and reducing costs. Restricted access to locations, environmental sensitivities, high drilling costs, and problematic production options are all driving the demand for new technologies that increase the probability of success in discovering, delineating, and developing oil and gas reservoirs. At the same time, companies must effectively reduce costs through efficient project execution. One such technology that addresses this industry need is low frequency (IF) seismic, the spectral analysis of the natural seismic wavefield of the earth between 0.1 and 10 Hz. The methodology uses very sensitive broadband seismometers-not the standard 3C geophones-to directly acquire the earth's low frequency «10 Hz) seismic background data (Fig. 1).Each instrument station includes a portable, ultrasensitive three-component broadband seismometer, battery pack, a GPS unit, and a hand-held controller. The IF data are analyzed to study small lateral variations. Empirical observations suggest that multiphase fluids in hydrocarbon reservoirs directly affect these small variations and generate energy anomalies in the earth's ambient seismic wavefield. In this article, innovations in extracting attributes from low frequency «10Hz) seismic wavefields are examined. also, potential applications for frontier exploration as well as in mature field development are considered. Citing recent case study examples, some of the challenges and developments in LF seismic will also be reviewed with regard to the quantitative integration of IF data with the reservoir's rock properties. Significant advances in processing and interpreting the LF data have been made using classical statistical analysis and predictive noise filtering.