SPE Journal
Volume 15, Number 4, December 2010, pp. 1077-1088

SPE-121210-PA

Faster Convergence in Seismic History Matching by Dividing and Conquering the Unknowns

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DOI  More information 10.2118/121210-PA http://dx.doi.org/10.2118/121210-PA

Citation

  • Sedighi, F. and Stephen, K.D. 2010. Faster Convergence in Seismic History Matching by Dividing and Conquering the Unknowns. SPE J. 15 (4): 1077-1088. SPE-121210-PA. doi: 10.2118/121210-PA.

Discipline Categories

  • 6.5.6 Dynamic Model Update Algorithms
  • 6.6.6 Seismic (Four Dimensional) Monitoring
  • 6.5.8 History Matching
  • 6.8 Fundamental Research in Reservoir Description and Dynamics
  • 6.1.5 Geologic Modeling

Keywords

  • Reservoir Simulation, History matching, 4D seismic, Model updating, Faster convergence

Summary

Seismic history matching is the process of modifying a reservoir simulation model to reproduce the observed production data in addition to information gained through time-lapse (4D) seismic data. The search for good predictions requires that many models be generated, particularly if there is an interaction between the properties that we change and their effect on the misfit to observed data. In this paper, we introduce a method of improving search efficiency by estimating such interactions and partitioning the set of unknowns into noninteracting subspaces. We use regression analysis to identify the subspaces, which are then searched separately but simultaneously with an adapted version of the quasiglobal stochastic neighborhood algorithm. We have applied this approach to the Schiehallion field, located on the UK continental shelf. The field model, supplied by the operator, contains a large number of barriers that affect flow at different times during production, and their transmissibilities are highly uncertain. We find that we can successfully represent the misfit function as a second-order polynomial dependent on changes in barrier transmissibility. First, this enables us to identify the most important barriers, and, second, we can modify their transmissibilities efficiently by searching subgroups of the parameter space. Once the regression analysis has been performed, we reduce the number of models required to find a good match by an order of magnitude. By using 4D seismic data to condition saturation and pressure changes in history matching effectively, we have gained a greater insight into reservoir behavior and have been able to predict flow more accurately with an efficient inversion tool. We can now determine unswept areas and make better business decisions.

© 2010. Society of Petroleum Engineers

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History

  • Original manuscript received: 28 February 2009
  • Meeting paper published: 9 June 2009
  • Revised manuscript received: 16 January 2010
  • Manuscript approved: 2 March 2010
  • Published online: 1 July 2010
  • Version of record: 2 December 2010