We present a seismic inversion method driven by a petroelastic model, providing fine-scale geological models, in depth, fully compatible with pre-stack seismic measurements.
A permanent reservoir monitoring system has been installed for Shell at the end of 2010, on a medium heavy-oil onshore field situated in the north-east of The Netherlands, in the context of re-development of oil production by Gravity Assisted Steam Flood. The first challenge was to continuously monitor, with seismic reflection, the lateral and vertical expansion of the steam chest injected in the reservoir during production over more than a year. As the 4D seismic attributes obtained from monitoring fit the measurements made at observation, production and injector wells, the 2D monitoring system was extended to 3D in April 2012. The second challenge was quantify seismic amplitude variations in terms of petro-acoustic parameters. For that purpose 4D inversion was performed on continuous 2D and 3D seismic monitoring data in order to quantify the lateral and vertical expansion of the steam chest on a daily basis. The 4D inversion results not only point out that the inversion enables to quantify the 4D effects in terms of P-impedance variations, but also greatly improves the vertical location of these events. Moreover, the percentage of maximum impedance variations and the thickness where these variations are observed are in good agreement with the petro-elastic model.
Structural information in subsurface seismic images is critical for reservoir delineation, reserve estimation, and well planning. However, by its very nature, it is also uncertain. One cause of the image uncertainty is the migration velocity model that directly affects the position of migrated events, both laterally and vertically. (The term "velocity" is meant in the broad sense; i.e., it also includes the anisotropy parameters.) We present a method that accounts for uncertainties in a velocity model estimated by tomography and translates them into the migrated domain. Standard-deviation attributes on target horizon positions or layer thicknesses are extracted. The method includes quality controls for validating the estimated uncertainty attributes before integration with other downstream or interpretative information. The method is demonstrated on a North Sea area covered by data from multiple seismic surveys. We observe that uncertainties increase with model complexity or depth and decrease as the illumination diversity increases. The computed uncertainty maps constitute a valuable source of information for hierarchizing (both qualitatively and quantitatively) different areas in the survey. For the purpose of reservoir risk analysis, we combine our technique with other information (e.g., interpretation uncertainties) to map how uncertainties in the depth of the structural spill point impact the gross rock volume (GRV) estimation of a reservoir.
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