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Abstract

Seismic history matching (HM) has attracted increasing attention the last few years. With the increasing amount 4D seismic available, it becomes imminent to find efficient and robust ways of conditioning these data jointly with the production data. A common conception seems to be that the amount of data represented by geophysical observations and the complexity of working with 3D fields make the updating procedure hard. We investigate the nature of geophysical observations from a conditioning point of view by testing several data reduction techniques such as Principal Component Analysis (PCA), regression techniques such as forward stagewise, as well as elastic nets. We argue that simulated geophysical fields from the prior models are prone with spatial correlations and that their information content and effective dimensionality is much smaller than the size/rank of the observed field. The techniques are tested on a reservoir model of a real North Sea oil field, using two conditioning algorithms: The ensemble Kalman filter and a response surface Bayesian approach. In addition to production data we condition the model to the seismic time shift, i.e. the difference in travel time integrated over the reservoir between two surveys. We find that PCA is particularly promising, resulting from the versatility and robustness of the method. In practice this means that high dimensional geophysical data, e.g. seismic images or seismic cubes, can often be described using only a handful of scalars. We show how to assess the information content in the data, compress the data, and use this compressed data consistently with other data types in a reservoir conditioning setting. Our results are of high significance. The methods we present are generic; they apply equally well to any geophysical attribute regardless of representation and they can be applied with any history matching algorithm.

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/content/papers/10.3997/2214-4609-pdb.293.F039
2012-06-04
2024-03-29
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http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609-pdb.293.F039
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