SPE Journal
Volume 15, Number 2, June 2010, pp. 382-394

SPE-118963-PA

Data Assimilation of Coupled Fluid Flow and Geomechanics Using the Ensemble Kalman Filter

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DOI  More information 10.2118/118963-PA http://dx.doi.org/10.2118/118963-PA

Citation

  • Chang, H., Chen, Y., and Zhang, D. 2010. Data Assimilation of Coupled Fluid Flow and Geomechanics Using the Ensemble Kalman Filter. SPE J.  15 (2): 382-394. SPE-118963-PA. doi: 10.2118/118963-PA.

Discipline Categories

  • 6.8 Fundamental Research in Reservoir Description and Dynamics
  • 6.5 Reservoir Simulation
  • 6.7 Reserves Evaluation
  • 6.3 Fluid Dynamics
  • 6.2 Fluids Characterization

Keywords

  • ensemble Kalman filter

Abstract

In reservoir history matching or data assimilation, dynamic data such as production rates and pressures are used to constrain reservoir models and to update model parameters. As such, even if under certain conceptualization the model parameters do not vary with time, the estimate of such parameters may change with the available observations and thus with time. In reality, the production process may lead to changes in both the flow and geomechanics fields, which are dynamically coupled. For example, the variations in the stress/strain field lead to changes in porosity and permeability of the reservoir and hence in the flow field. In weak formations such as the Lost Hills oilfield, fluid extraction may cause a large compaction to the reservoir rock and a significant subsidence at the land surface, resulting in huge economic losses and detrimental environmental consequences. The strong nonlinear coupling between reservoir flow and geomechanics poses a challenge to constructing a reliable model for predicting oil recovery in such reservoirs. On the other hand, the subsidence and other geomechanics observations can provide additional insight into the nature of the reservoir rock and help constrain the reservoir model if used wisely. In this study, the Ensemble Kalman filter (EnKF) approach is used to estimate reservoir flow and material properties by jointly assimilating dynamic flow and geomechanics observations. The resulting model can be used for managing and optimizing production operations and for mitigating the land subsidence. The use of surface displacement observations improves the match to both production and displacement data. Localization is used to facilitate the assimilation of a large amount of data and to mitigate the effect of spurious correlations resulting from small ensembles. Since the stress, strain, and displacement fields are updated together with the material properties in the EnKF, the issue of consistency at the analysis step of the EnKF is investigated. A 3D problem with reservoir fluid-flow and mechanical parameters close to those of the Lost Hills oilfield is used to test the applicability.

© 2010. Society of Petroleum Engineers

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History

  • Original manuscript received: 2 February 2009
  • Meeting paper published: 2 February 2009
  • Revised manuscript received: 4 May 2009
  • Manuscript approved: 15 July 2009
  • Published online: 1 February 2010
  • Version of record: 17 June 2010