Abstract
Ensemble-based data assimilation methods have been extensively investigated for inverse problems of fluid flow in porous media. However, when the permeability field is characterized by fine-scale gridblocks, the problem can be ill-posed and result in non-unique solutions. To address this issue, the principal component analysis with truncation was presented, but it may lead to biased estimation. In this paper, we propose to keep all eigenfunctions without truncation and add an additional sorting step after principal component analysis: sorting the initial samples according to the dimensional variability and assigning the dimensions with large variances to the leading eigenfunctions. The estimation is expected to be more accurate as the subspace spanned by the ensemble favors the dominant components. The proposed method is tested for multiple synthetic flow and transport cases. The results show that it provides more accurate estimation of the permeability fields and generates better history matching and prediction results for the production data (by 10–15%) than the results from the standard ensemble smoother, with the same computational cost. This sorting approach can be readily extended to the ensemble Kalman filter as well, for inverse modeling and estimating reservoir properties.
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs13369-022-07343-x/MediaObjects/13369_2022_7343_Fig1_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs13369-022-07343-x/MediaObjects/13369_2022_7343_Fig2_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs13369-022-07343-x/MediaObjects/13369_2022_7343_Fig3_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs13369-022-07343-x/MediaObjects/13369_2022_7343_Fig4_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs13369-022-07343-x/MediaObjects/13369_2022_7343_Fig5_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs13369-022-07343-x/MediaObjects/13369_2022_7343_Fig6_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs13369-022-07343-x/MediaObjects/13369_2022_7343_Fig7_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs13369-022-07343-x/MediaObjects/13369_2022_7343_Fig8_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs13369-022-07343-x/MediaObjects/13369_2022_7343_Fig9_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs13369-022-07343-x/MediaObjects/13369_2022_7343_Fig10_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs13369-022-07343-x/MediaObjects/13369_2022_7343_Fig11_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs13369-022-07343-x/MediaObjects/13369_2022_7343_Fig12_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs13369-022-07343-x/MediaObjects/13369_2022_7343_Fig13_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs13369-022-07343-x/MediaObjects/13369_2022_7343_Fig14_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs13369-022-07343-x/MediaObjects/13369_2022_7343_Fig15_HTML.png)
Similar content being viewed by others
Abbreviations
- C :
-
Covariance
- f :
-
Eigenfunction
- h :
-
Hydraulic head, m
- k :
-
Absolute permeability, mD
- krα :
-
Relative permeability for a phase fluid
- N :
-
Normal distribution
- p c :
-
Capillary pressure, psia
- p α :
-
Pressure of αα phase fluid, psia
- q α :
-
Source/sink term, kg/s
- S α :
-
Saturation of the α phase fluid
- S or :
-
Residual oil saturation
- S wc :
-
Irreducible water saturation
- t :
-
Time, day
- u α :
-
Velocity of α phase fluid, m/s
- x :
-
Location in space, m
- Y :
-
Log-permeability, mD
- λ :
-
Eigenvalue
- ξ :
-
Independent random variable
- η :
-
Correlation length, ft
- σ 2 :
-
Variance
- μ α :
-
Viscosity of α phase fluid, Pa·s
- ρ α :
-
Density of α phase fluid, kg/m3
- ϕ :
-
Porosity
- RMSE:
-
Root mean square error
- BHP:
-
Bottom hole pressure, psia or bar
- OPR:
-
Oil production rate, bbl/day or m3/day
- WPR:
-
Water production rate, bbl/day or m3/day
- GOR:
-
Gas–oil ratio
- WCT:
-
Water cut
- FOPT:
-
Field oil production total, bbl/day or m3/day
- FGPT:
-
Field gas production total, bbl/day or m3/day
- FWPT:
-
Field water production total, bbl/day or m3/day
References
Zhong, H.; Li, Y.; Zhang, W.; Yin, H.; Lu, J.; Guo, D.: Microflow mechanism of oil displacement by viscoelastic hydrophobically associating water-soluble polymers in enhanced oil recovery. Polymers 10, 628 (2018)
Chen, S.Y.; Hsu, K.C.; Fan, C.M.: Improvement of generalized finite difference method for stochastic subsurface flow modeling. J. Comput. Phys. 429, 110002 (2021)
Lei, G.; Liao, Q.; Lin, Q.; Zhang, L.; Xue, L.; Chen, W.: Stress dependent gas-water relative permeability in gas hydrates: a theoretical model. Adv. Geo Energy Res. 4, 326–338 (2020)
Wang, S.; Qin, C.; Feng, Q.; Javadpour, F.; Rui, Z.: A framework for predicting the production performance of unconventional resources using deep learning. Appl. Energy 295, 117016 (2021)
El-Amin, M.; Kou, J.; Sun, S.; Salama, A.: Adaptive time-splitting scheme for two-phase flow in heterogeneous porous media. Adv. Geo Energy Res. 1, 182–189 (2017)
Rostami, A.; Daneshi, A.; Miri, R.: Proposing a rigorous empirical model for estimating the bubble point pressure in heterogeneous carbonate reservoirs. Adv. Geo Energy Res. 4, 126–134 (2020)
Liao, Q.; Zeng, L.; Chang, H.; Zhang, D.: Efficient history matching using the Markov-chain Monte Carlo method by means of the transformed adaptive stochastic collocation method. SPE J. 24, 1468–1489 (2019)
Yang, E.; Fang, Y.; Liu, Y.; Li, Z.; Wu, J.: Research and application of microfoam selective water plugging agent in shallow low-temperature reservoirs. J. Pet. Sci. Eng. 193, 107354 (2020)
Wang, Y.; Li, X.J.: Physicochemical modeling of barium and sulfate transport in porous media and its application in seawater-breakthrough monitoring. SPE J. 26, 1–22 (2021)
Tang, Y.; Hou, C.; He, Y.; Wang, Y.; Chen, Y.; Rui, Z.: Review on pore structure characterization and microscopic flow mechanism of CO2 flooding in porous media. Energy Technol. 9, 2000787 (2021)
Reichle, R.H.; McLaughlin, D.B.; Entekhabi, D.: Hydrologic data assimilation with the ensemble Kalman filter. Mon. Weather Rev. 130, 103–114 (2002)
Houtekamer, P.; Zhang, F.: Review of the ensemble Kalman filter for atmospheric data assimilation. Mon. Weather Rev. 144, 4489–4532 (2016)
Chen, Y.; Zhang, D.: Data assimilation for transient flow in geologic formations via ensemble Kalman filter. Adv. Water Res. 29, 1107–1122 (2006)
Xue, L.; Zhang, D.: A multimodel data assimilation framework via the ensemble Kalman filter. Water Resour. Res. 50, 4197–4219 (2014)
Xue, L.; Zhang, D.; Guadagnini, A.; Neuman, S.P.: Multimodel Bayesian analysis of groundwater data worth. Water Resour. Res. 50, 8481–8496 (2014)
Zhang, J.; Vrugt, J.A.; Shi, X.; Lin, G.; Wu, L.; Zeng, L.: Improving simulation efficiency of MCMC for inverse modeling of hydrologic systems with a Kalman-inspired proposal distribution. Water Resour. Res. 56, e2019WR025474 (2020)
Zhu, P.; Shi, L.; Zhu, Y.; Zhang, Q.; Huang, K.; Williams, M.: Data assimilation of soil water flow via ensemble Kalman filter: infusing soil moisture data at different scales. J. Hydrol. 555, 912–925 (2017)
Aanonsen, S.I.; Nævdal, G.; Oliver, D.S.; Reynolds, A.C.; Vallès, B.: The ensemble Kalman filter in reservoir engineering-a review. SPE J. 14, 393–412 (2009)
Gu, Y.; Oliver, D.S.: An iterative ensemble Kalman filter for multiphase fluid flow data assimilation. SPE J. 12, 438–446 (2007)
Liao, Q.; Zhang, D.: Data assimilation for strongly nonlinear problems by transformed ensemble Kalman filter. SPE J. 20, 202–221 (2015)
Oliver, D.S.; Chen, Y.: Recent progress on reservoir history matching: a review. Comput. Geosci. 15, 185–221 (2011)
Zeng, L.; Zhang, D.: A stochastic collocation based Kalman filter for data assimilation. Comput. Geosci. 14, 721–744 (2010)
Liao, Q.; Alsamadony, K.; Lei, G.; Awotunde, A.; Patil, S.: Reservoir history matching by ensemble smoother with principle component and sensitivity analysis for heterogeneous formations. J. Pet. Sci. Eng. 198, 108140 (2021)
Gu, Y.; Oliver, D.S.: The ensemble Kalman filter for continuous updating of reservoir simulation models. J. Energy Resour. 128, 79–87 (2006)
Li, G.; Reynolds, A.C.: Iterative ensemble Kalman filters for data assimilation. SPE J. 14, 496–505 (2009)
Bailey, R.; Baù, D.: Ensemble smoother assimilation of hydraulic head and return flow data to estimate hydraulic conductivity distribution. Water Resour. Res. 46, W12543 (2010)
Emerick, A.A.; Reynolds, A.C.: Ensemble smoother with multiple data assimilation. Comput. Geosci. 55, 3–15 (2013)
Evensen, G.: Data Assimilation: the Ensemble Kalman Filter. Springer, Berlin (2007)
Skjervheim, J.A.; Evensen, G.: An ensemble smoother for assisted history matching. In: SPE Reservoir Simulation Symposium, The Woodlands, Texas (2011)
Evensen, G.; Eikrem, K.S.: Conditioning reservoir models on rate data using ensemble smoothers. Comput. Geosci. (2018). https://doi.org/10.1007/s10596-018-9750-8
Chang, H.; Zhang, D.: History matching of statistically anisotropic fields using the Karhunen-loeve expansion-based global parameterization technique. Comput. Geosci. 18, 265–282 (2014)
He, J.; Sarma, P.; Durlofsky, L.J.: Reduced-order flow modeling and geological parameterization for ensemble-based data assimilation. Comput. Geosci. 55, 54–69 (2013)
Reynolds, A.C.; He, N.; Chu, L.; Oliver, D.S.: Reparameterization techniques for generating reservoir descriptions conditioned to variograms and well-test pressure data. SPE J. 1, 413–426 (1996)
Sarma, P.; Durlofsky, L.J.; Aziz, K.: Kernel principal component analysis for efficient, differentiable parameterization of multipoint geostatistics. Math. Geosci. 40, 3–32 (2008)
Oliver, D.S.; Chen, Y.: Improved initial sampling for the ensemble Kalman filter. Comput. Geosci. 13, 13 (2009)
Uyeda, J.C.; Caetano, D.S.; Pennell, M.W.: Comparative analysis of principal components can be misleading. Syst. Biol. 64, 677–689 (2015)
Bear, J.: Dynamics of Fluids in Porous Media. Elsevier, New York (1972)
Dagan, G.: Flow and Transport in Porous Formations. Springer, New York (1989)
Ghanem, R.G.; Spanos, P.D.: Stochastic Finite Elements: a Spectral Approach. Springer, New York (1991)
Jafarpour, B.; McLaughlin, D.B.: History matching with an ensemble Kalman filter and discrete cosine parameterization. Comput. Geosci. 12, 227–244 (2008)
Floris, F.J.T.; Bush, M.D.; Cuypers, M.; Roggero, F.; Syversveen, A.-R.: Methods for quantifying the uncertainty of production forecasts. Pet. Geosci. 7, S87–S96 (2001)
Gu, Y.; Oliver, D.S.: History matching of the PUNQ-S3 reservoir model using the ensemble Kalman filter. SPE J. 10, 51–65 (2005)
PUNQ-S3 model: https://www.imperial.ac.uk/earth-science/research/research-groups/perm/standard-models/(2021)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Liao, Q. Ensemble Smoother with Enhanced Initial Samples for Inverse Modeling of Subsurface Flow Problems. Arab J Sci Eng 48, 9535–9548 (2023). https://doi.org/10.1007/s13369-022-07343-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13369-022-07343-x