Reservoir automatic history matching: Methods, challenges, and future directions
Keywords:
History matching, optimization algorithm, surrogate model, data-drivenAbstract
Reservoir history matching refers to the process of continuously adjusting the parameters of the reservoir model, so that its dynamic response will match the historical observation data, which is a prerequisite for making forecasts based on the reservoir model. With the development of optimization theory and machine learning algorithms, automatic history matching has made numerous breakthroughs for practical applications. In this perspective, the existing automatic history matching methods are summarized and divided into model-driven and surrogate-driven history matching methods according to whether the reservoir simulator needs to be run during the automatic history matching process. Then, the basic principles of these methods and their limitations in practical applications are outlined. Finally, the future trends of reservoir automatic history matching are discussed.
Document Type: Perspective
Cited as: Liu, P., Zhang, K., Yao, J. Reservoir automatic history matching: Methods, challenges, and future directions. Advances in Geo-Energy Research, 2023, 7(2): 136-140. https://doi.org/10.46690/ager.2023.02.07
ReferencesAshby, S. F., Falgout, R. D. A parallel multigrid preconditioned conjugate gradient algorithm for groundwater flow simulations. Nuclear Science and Engineering, 1996, 124(1): 145-159.
Bertolini, A. C., Schiozer, D. J. Influence of the objective function in the history matching process. Journal of Petroleum Science and Engineering, 2011, 78(1): 32-41.
Chen, J., Wang, L., Wang, C., et al. Automatic fracture optimization for shale gas reservoirs based on gradient descent method and reservoir simulation. Advances in Geo-Energy Research, 2021, 5(2): 191-201.
Chen, Y., Oliver, D. S., Zhang, D. Efficient ensemble-based closed-loop production optimization. SPE Journal, 2009, 14(4): 634-645.
Cullick, A. S., Johnson, W. D., Shi, G. Improved and more rapid history matching with a nonlinear proxy and global optimization. Paper SPE 101933 Presented at SPE Annual Technical Conference and Exhibition, San Antonio, Texas, 24-27 September, 2006.
Dachanuwattana, S., Jin, J., Zuloaga-Molero, P., et al. Application of proxy-based MCMC and EDFM to history match a Vaca Muerta shale oil well. Fuel, 2018, 220: 490-502.
Dunbar, W. S., Woodbury, A. D. Application of the Lanczos algorithm to the solution of the groundwater flow equation. Water Resources Research, 1989, 25(3): 551-558.
Efendiev, Y., Hou, T., Luo, W. Preconditioning markov chain monte carlo simulations using coarse-scale models. SIAM Journal on Scientific Computing, 2006, 28(2): 776-803.
Emerick, A. A., Reynolds, A. C. Ensemble smoother with multiple data assimilation. Computers & Geosciences, 2013, 55: 3-15.
Eydinov, D., Aanonsen, S. I., Hauk°as, J., et al. A method for automatic history matching of a compositional reservoir simulator with multipoint flux approximation. Computational Geosciences, 2008, 12(2): 209-225.
Gao, G., Wang, Y., Vink, J. C., et al. Distributed quasi-Newton derivative-free optimization method for optimization problems with multiple local optima. Computational Geosciences, 2022, 26(4): 847-863.
Ghommem, M., Presho, M., Calo, V. M., et al. Mode decomposition methods for flows in high-contrast porous media. Global-local approach. Journal of Computational Physics, 2013, 253: 226-238.
Gu, Y., Oliver, D. S. History matching of the PUNQ-S3 reservoir model using the ensemble Kalman filter. Paper SPE 89942 Presented at SPE Annual Technical Conference and Exhibition, Houston, Texas, 26-29 September, 2004.
Hussain, M. F., Barton, R. R., Joshi, S. B. Metamodeling: Radial basis functions, versus polynomials. European Journal of Operational Research, 2002, 138(1): 142-154.
Jiang, S., Durlofsky, L. J. Use of multifidelity training data and transfer learning for efficient construction of subsurface flow surrogate models. Journal of Computational Physics, 2023, 474: 111800.
Jo, S., Jeong, H., Min, B., et al. Efficient deep-learning-based history matching for fluvial channel reservoirs. Journal of Petroleum Science and Engineering, 2022, 208: 109247.
Kim, J., Kim, S., Park, C., et al. Construction of prior models for ES-MDA by a deep neural network with a stacked autoencoder for predicting reservoir production. Journal of Petroleum Science and Engineering, 2020, 187: 106800.
Li, B., Bhark, E. W., Gross, S. I., et al. Best practices of assisted history matching using design of experiments. SPE Journal, 2019, 24(4): 1435-1451.
Liu, N., Oliver, D. S. Evaluation of Monte Carlo methods for assessing uncertainty. SPE Journal, 2003, 8(2): 188-195.
Ma, X., Zhang, K., Wang, J., et al. An efficient spatialtemporal convolution recurrent neural network surrogate model for history matching. SPE Journal, 2022, 27(2): 1160-1175.
Ma, X., Zhang, K., Zhang, L., et al. Data-driven niching differential evolution with adaptive parameters control for history matching and uncertainty quantification. SPE Journal, 2021, 26(2): 993-1010.
McPhee, J., Yeh, W. W. -G. Groundwater management using model reduction via empirical orthogonal functions. Journal of Water Resources Planning and Management, 2008, 134(2): 161-170.
Oliver, D. S., Fossum, K., Bhakta, T., et al. 4D seismic history matching. Journal of Petroleum Science and Engineering, 2021, 207: 109119.
Raissi, M., Perdikaris, P., Karniadakis, G. E. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics, 2019, 378: 686-707.
Rao, X., Xu, Y., Liu, D., et al. A general physics-based data-driven framework for numerical simulation and history matching of reservoirs. Advances in Geo-Energy Research, 2021, 5(4): 422-436.
Romero, C., Carter, J. Using genetic algorithms for reservoir characterisation. Journal of Petroleum Science and Engineering, 2001, 31(2-4): 113-123.
Rwechungura, R., Dadashpour, M., Kleppe, J. Advanced history matching techniques reviewed. Paper SPE 142497 Presented at SPE Middle East Oil and Gas Show and Conference, Manama, Bahrain, 25-28 September, 2011.
Shorten, C., Khoshgoftaar, T. M. A survey on image data augmentation for deep learning. Journal of Big Data, 2019, 6: 60.
Stone, N. Gaussian process emulators for uncertainty analysis in groundwater flow. Nottingham, University of Nottingham, 2011.
Tang, M., Liu, Y., Durlofsky, L. J. A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems. Journal of Computational Physics, 2020, 413: 109456.
Wang, N., Zhang, D., Chang, H., et al. Deep learning of subsurface flow via theory-guided neural network. Journal of Hydrology, 2020, 584: 124700.
Weiss, K., Khoshgoftaar, T. M., Wang, D. A survey of transfer learning. Journal of Big Data, 2016, 3: 9. Winter, C., Tartakovsky, D., Guadagnini, A. Moment differential equations for flow in highly heterogeneous porous media. Surveys in Geophysics, 2003, 24(1): 81-106.
Woodbury, A. D., Dunbar, W. S., Nour-Omid, B. Application of the arnoldi algorithm to the solution of the advection-dispersion equation. Water Resources Research, 1990, 26(10): 2579-2590.
Yang, P. -H., Watson, A. T. Automatic history matching with variable-metric methods. SPE Reservoir Engineering, 1988, 3(4): 995-1001.
Yazdanpanah, A., Rezaei, A., Mahdiyar, H., et al. Development of an efficient hybrid GA-PSO approach applicable for well placement optimization. Advances in Geo-Energy Research, 2019, 3(4): 365-374.
Yeung, Y. H., Barajas-Solano, D. A., Tartakovsky, A. M. Physics-informed machine learning method for largescale data assimilation problems. Water Resources Research, 2022, 58(5): e2021WR031023.
Yin, F., Xue, X., Zhang, C., et al. Multifidelity genetic transfer: an efficient framework for production optimization. SPE Journal, 2021, 26(4): 1614-1635.
Zenke, F., Poole, B., Ganguli, S. Continual learning through synaptic intelligence. Proceedings of Machine Learning Research, 2017, 70: 3987-3995.
Zha, W., Gao, S., Li, D., et al. Application of the ensemble Kalman filter for assisted layered history matching. Advances in Geo-Energy Research, 2018, 2(4): 450-456.
Zhang, K., Wang, X., Ma, X., et al. The prediction of reservoir production based proxy model considering spatial data and vector data. Journal of Petroleum Science and Engineering, 2022, 208: 109694.
Zhao, H., Kang, Z., Zhang, X., et al. A physics-based data-driven numerical model for reservoir history matching and prediction with a field application. SPE Journal, 2016, 21(6): 2175-2194.
Zhong, C., Zhang, K., Xue, X., et al. Historical window-enhanced transfer gaussian process for production optimization. SPE Journal, 2022, 27(5): 2895-2912.