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Abstract. The Enfield rock physics model was constructed to enable 4D feasibility studies and interpretation of the 2007 Enfield 4D seismic monitor survey. The rock physics model links reservoir static and dynamic parameters to impedances, using log data from five wells in the field, laboratory core measurements taken from cores on Enfield and neighbouring fields, and theoretical rock models from the literature. The reservoir is modelled by a sand-shale mix: sand properties are described using a modified critical porosity model whereas shale properties are generated from log data averaging. The dynamic properties in the model include saturation and pressure. Saturation is modelled using Gassmann's formula assuming homogeneous mixing. The reservoir sand velocity-pressure relationship is described by an empirical model fitted to dry core plug measurements. An assessment of the effect of uncertainty is included for both the saturation and pressure elements of the model. The resultant rock physics model was used before the acquisition of the seismic monitor survey to assess the likelihood of detecting a 4D seismic signal only 7 months after production start-up. Our modelling results indicate that the strong pressure build-up around the water injectors would result in a detectable 4D seismic signal and this prediction is confirmed by the successful 4D seismic monitor data acquired in 2007. The rock physics model has been validated against the 4D monitor data and is being used to quantify the 4D interpretation, linking the observed 4D response back to predicted pressure and saturations changes in the field.

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Full-waveform inversion (FWI) has the potential to be a game changer for the seismic industry since it produces accurate velocity models at a resolution that cannot be matched by conventional traveltime tomography, with the additional advantage that it runs on basically raw data. Today, these two features should be better exploited to maximize the business value that FWI can deliver to an oil and gas operator. Provided that we acquire adequate data sets, FWI can be run starting from a smooth velocity model so that traveltime tomography is completely bypassed. As very little processing is required, FWI can be executed in parallel with the main preprocessing efforts, with the results of shortening considerably the overall project turnaround. For imaging purposes, FWI can be limited to the low-frequency range (∼ 10 Hz) but if run in the high-frequency range (∼ 30 Hz), FWI velocities have such a resolution that they can be used directly for interpretation. In particular, applications in the field of pore-pressure prediction and shallow hazard have great potential. In the current implementation, the main limitations are linked to data quality and computational power, but both these issues can be adequately addressed.