Time-lapse (4D) seismic data can be integrated into history matching by comparing predicted and observed data in various domains. These include time domain, seismic attributes, or petro-elastic properties such as acoustic impedance. Each domain requires different modelling methods and assumptions as well as data handling workflows. The aim of this work is to investigate the degree to which the choice of domain influences outcome of history matching on the choice of best model and associated uncertainties. Another aspect of history matching is that long simulations often pose an obstacle for an automatic approach. In this study we use appropriately upscaled models manageable in the automatic history matching loop. We apply manual and assisted seismic history matching to the Schiehallion field. In the assisted approach, the optimization loop is driven by a stochastic algorithm, while the manual workflow is based on qualitative comparison of seismic maps. By upscaling we obtained an order of magnitude gain in performance. Accurate upscaling was ensured by thorough volume and transmissibility calculation within regions. The parameterisation of the problem is based on a pattern of seismically derived geobodies with specified transmissibility multipliers between the regions. Seismic predictions are made through petro-elastic modelling, 1D convolution, coloured inversion and calculation of different attributes. We were able to achieve a reasonable match of production and 4D seismic data using coarse scale models in manual and assisted approaches. We observed that the misfit surfaces are different when working in the various seismic domains considered. Use of equivalent domains for observed and predicted data was found to give a more unique misfit response and better result. Accurate comparison of predicted and observed 4D seismic data in different domains is necessary for tackling non-uniqueness of the inverse problem and hence reducing the uncertainty of field development predictions.


Article metrics loading...

Loading full text...

Full text loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error