Geodesic
A hybrid assistant history matching algorithm considering tracer studies
Matematičeskoe modelirovanie, Tome 33 (2021) no. 6, pp. 73-87.

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This paper presents a hybrid history matching algorithm based on separable natural evolution strategies (sNES) and Adjoint-based tracer studies gradient. We demonstrate the effectiveness of the approach conditioning porosity and permeability fields on the historical production data and information about tracer studies. Tracer studies and fluid flow in the reservoir is numerically modeled. This approach to the joint assistant history matching problem significantly reduces the number of reservoir simulator runs with similar quality to the resulting models.
Keywords: assistant history matching, hybrid algorithm, tracer studies, sNES.
Mots-clés : Adjoint 
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A. A. Kazakov; P. V. Lomovitskiy; A. N. Khlyupin. A hybrid assistant history matching algorithm considering tracer studies. Matematičeskoe modelirovanie, Tome 33 (2021) no. 6, pp. 73-87. http://geodesic.mathdoc.fr/item/MM_2021_33_6_a5/

[1] Margot G. Gerritsen, Louis J. Durlofsky, “Modeling fluid flow in oil reservoirs”, Ann. Rev. Fluid Mech., 37 (2005), 211–238 | DOI | Zbl

[2] G. Vadicharla, P. Sharma, “Optimization Techniques for History Matching and Production Forecasting”, Internat. J. of Recent Technology and Engineering (IJRTE), 8:4 (2019)

[3] Liang Baosheng, An Ensemble Kalman Filter Module for Automatic History Matching, The University of Texas at Austin, 2007

[4] P. Sarma, L. J. Durlofsky, K. Aziz, W. H. Chen, “Efficient real-time reservoir management using adjoint-based optimal control and model updating”, Computational Geosciences, 10:1 (2006), 3–36 | DOI | MR | Zbl

[5] A. Kadyrova, A. Khlyupin, “Application of adjoint-based optimal control to gas reservoir with a memory effect”, ECMOR XVI — 16th European Conf. on the Mathematics of Oil Recovery, v. 1, European Association of Geoscientists Engineers, 2018, 2641–2653 | DOI

[6] Li Guohua Gao Gaoming, Reynolds Albert Coburn, “A Stochastic Optimization Algorithm for Automatic History Matching”, SPE annual technical conference and exhibition, Society of Petroleum Engineers, 2004

[7] S. Sousa, C. Maschio, D. Schiozer, “Scatter Search Metaheuristic Applied to the History-Matching Problem”, SPE Ann. Technical Conf. Exhibition, Soc. of Petrol. Eng., 2007

[8] M. Kathrada, Uncertainty Evaluation of Reservoir Simulation Models Using Particle Swarms and Hierarchical Clustering, Degree of Doctor of Philosophy, Institute of Petroleum Engineering Heriot-Watt University, 2009

[9] J. Farhang, R. Fatemeh, “Oil Production Optimization in Petroleum Reservoirs by Ant Algorithm”, Chemical Product and Process Modeling, 3:1 (2008) | Zbl

[10] N. C. Chithra Chakra, N. Saraf Deoki, “History matching of petroleum reservoirs employing adaptive genetic algorithm”, J. of Petroleum Exp. Prod. Technology, 6:4 (2016), 653–674 | DOI

[11] M. Sayyafzadeh, R. Koochak, M. Barley, “Accelerating CMA-ES In History Matching Problems Using An Ensemble Of Surrogates With Generation-Based Management”, ECMOR XVI — 16th European Conf. on the Mathematics of Oil Recovery, v. 1, European Association of Geoscientists Engineers, 2018, 1592–1606

[12] G. M. Van Essen, A. Conn, G. D. Sippe, L. Horesh, E. Jimenez, J. K. Przybysz-jarnut, P. J. Van den Hoek, “Adjoint-based History-matching of Production and Time-lapse Seismic Data”, SPE Europec/EAGE Annual Conference, Society of Petroleum Engineers, 2012

[13] E. Ali, C. Chatzichristos, T. Aurdal, J. Muller, “Tracer Simulation to Improve the Reservoir Model in the Snorre Field”, Int. Oil Gas Conf. Exh. in China, 2000, SPE-64796-MS, Soc. of Petrol. Eng. | DOI

[14] D. Wierstra, T. Schaul, T. Glasmachers, Sun Yi, J. Peters, J. Schmidhuber, “Natural Evolution Strategies”, The J. of Machine Learning Res., 15:1 (2014), 949–980 | MR | Zbl

[15] Y. Liu, J. Li, S. Sun, B. Yu, “Advances in Gaussian random field generation: a review”, Computational Geosciences, 2019, 1–37 | MR

[16] W. Sun, L. J. Durlofsky, “A New Data-Space Inversion Procedure for Efficient Uncertainty Quantification in Subsurface Flow Problems”, Math. Geosciences, 49:6 (2017), 679–715 | DOI | MR | Zbl

[17] K.-A. Lie, User Guide to the MATLAB Reservoir Simulation Toolbox (MRST), Cambridge University Press, 2019 | Zbl

[18] K. H. Birdsell, L. F. Brown, A. E. Norris, G. A. Cederberg, B. J. Travis, P. G. Stringer, “Modeling tracer diffusion in fractured and unfractured, unsaturated, porous media”, Journal of contaminant hydrology, 3:2–4 (1988), 145–170 | DOI

[19] A. C. Petrusev, Raznostnye skhemy i ikh analiz, MFTI, M., 2004, 89 pp.

[20] R. N. Diiashev, A. Asklu, V. N. Akhmetov, “Differentsiatsiya terrigennyh kollektorov s ispolzovaniem indeksa perkolyatsii dlya posleduyuschey korrelyatsii ikh filtratsionnykh svoistv”, Georesursy, 2000, no. 2 (3)

[21] A. Slowik, H. Kwasnicka, “Evolutionary algorithms and their applications to engineering problems”, Neural Computing and Applications, 2020, 1–17

[22] B. Li, E. W. Bhark, S. J. Gross, T. C. Billiter, K. Dehghani, “Best Practices of Assisted History Matching Using Design of Experiments”, SPE J., 24:4 (2019), 1435–1451 | DOI