George Bunge

From pore-pressure prediction to reservoir characterization: A combined geomechanics-seismic inversion workflow using trend-kriging techniques in a deepwater basin

To optimize drilling decisions and well planning in overpressured areas, it is essential to carry out pore-pressure predictions before drilling. Knowledge of pore pressure implies knowledge of the effective stress, which is a key input for several geomechanics applications, such as fault slip and fault seal analysis and reservoir compaction studies. It is also a required input for 3D and 4D seismic reservoir characterization. Because the seismic response of shales and sand depends on their compaction history, the effective stress will govern the sedimentary seismic response. This is in contrast to normally pressured regimes, where the depth below mudline (or overburden stress) is typically used to characterize the compaction effect.

Trend-kriged seismic velocities to predict pore pressure and to model effective stress for reservoir characterization in a deepwater basin

Summary To optimize drilling decisions and well planning in overpressured areas, predrill pore pressure predictions are essential. Knowledge of pore pressure implies knowledge of the effective stress, which is one of the key inputs for 3D and 4D seismic reservoir characterization. This case study focuses on how high-resolution seismic velocities (Banik et al., 2003) were used to predict pore pressure and effective stress in a deepwater environment. The velocity to pore pressure transform was calibrated using existing well log data. To allow for consistency between well and seismic data and stratigraphic layers, geostatistical mapping (trendkriging) techniques were applied using several key horizons. Thus, the final trend-kriged model is constrained by the structural framework and the geology of the basin.

Effective -stress-based reservoir characterization in an offshore basin

Summary Effective stress is a key attribute that enables the prediction of subsurface lithology units in overpressured basins. Because the seismic response of shales and sand depends on their compaction history, the effective stress will govern the sedimentary seismic response. This is in contrast to normally pressured regimes, where the depth below mudline (or overburden stress) is typically used to characterize the compaction effect. Effective stress enables one to map nonstationary sedimentary compaction in space. We use seismically derived effective stress as an additional attribute in the Bayesian Lithofacies classification (Bachrach et al., 2004; Mukerji et al., 2001). The other attributes in the process are elastic parameters such as acoustic and shear impedances and density obtained from the multiattribute seismic inversion (Roberts et al., 2005). The modified reservoir characterization method has been applied to a deepwater basin that contains reservoir units of the Pleistocene to the mid-Miocene age extending over a large area and is known to contain overpressure zones.

Quantifying Reservoir Properties of the East Texas Woodbine Through Rock Physics And Multiattribute Seismic Inversion

Multiattribute seismic inversion (MASI) is a newly developed prestack inversion technique for inverting seismic data into elastic parameter attributes. The method integrates several major technologies (Roberts, et al., 2005), including AVO processing and analysis, well-log editing and calibration, prestack waveform inversion (PSWI) (Mallick, et al., 2000), wavelet processing, and poststack inversion. The outputs are high-resolution absolute acoustic and shear impedance and density volumes consistent with the seismic data and the well-log data. The inverted elastic parameter volumes are used for detailed interpretation of lithofacies and pore-fluid content in the subsurface. Combined with rock physics modeling and rock property mapping through LithoCube (Bachrach, et al., 2004) and joint porosity-saturation inversion (Bachrach and Dutta, 2004), the method provides a powerful tool for quantitative reservoir description and characterization. The results are the most-probable litho-class, porosity, and saturation with uncertainties of prediction at every sample point in the 3-D volume. Recently we applied this method to identify and characterize Woodbine sandstone reservoirs in an onshore East Texas field.