Optimizing the integration of 4D seismic data in history matching: which data should we compare?

History matching by integrating time-lapse seismic with production data can become a more complex process. The additional constraints make it harder to find good models and this is made more difficult due to the nonlinearities encountered when predicting seismic behavior. A key aspect of this is the choice of domain in which to compare seismic data such as time domain or domain of inverted petro-elastic properties. We extend previous work where we examined the misfit surface from various domains by adding quantitative measures of nonlinearity in function of seismic on model parameters and analysing uncertainties in parameter estimates.
In this study we apply history matching to the models on the Schiehallion field. We compare the attributes of acoustic impedance derived from coloured inversion products to predicted acoustic impedance from a petro-elastic model. We call this a cross-domain comparison. To perform an alternative same-domain comparison, seismic prediction is based on the 1D convolution method. We then derive predicted pseudo impedance attributes, equivalent to those observed, using ‘coloured inversion’. Models obtained in history matching using these two schemes are then examined.
Our results show that the outcome of history matching to seismic data is affected by the underlying static model conditioned to baseline 3D seismic in different domains which makes comparison of domains more difficult. It was demonstrated that cross-domain comparison of predicted impedances to observed seismic data increases non-uniqueness of parameter estimates. On the other hand, comparison in the same domain requires more modeling steps which adds to the nonlinearity because of narrowing frequency band.
Accurate reconciliation of predicted and observed seismic and production data via history matching is necessary for maximising the forecast capability of a simulation model. This significantly improves decision making by reducing risks and uncertainties in a field development.