1887

Abstract

Summary

In this work we present two unique workflows for assisted history matching of seismic and production data, and demonstrate the methods on a real field case. Both workflows use an iterative ensemble smoother for the data assimilation, but differ in data representation and localization method. Further, publicly available seismic data are inverted for acoustic impedance using two different approaches. In addition, correlated data noise is estimated for the 4D attributes using different techniques. History matching results are presented for selected production and seismic data, and estimated parameters are shown for one layer in the model. Both workflows demonstrate that ensemble based iterative smoothers can successfully assimilate large amounts of correlated data. Despite methodological differences in the workflows, both methods are able to make significant improvements to the data match. The work demonstrates promising advances towards assisted assimilation of big data-sets for real field cases.

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/content/papers/10.3997/2214-4609.201902180
2019-09-02
2020-07-08
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References

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