Intelligent modeling with physics-informed machine learning for petroleum engineering problems

Chiyu Xie, Shuyi Du, Jiulong Wang, Junming Lao, Hongqing Song

Abstract view|19|times       PDF download|15|times

Abstract


The advancement in big data and artificial intelligence has enabled a novel exploration mode for the study of petroleum engineering. Unlike theory-based solution methods, the data-driven intelligent approaches demonstrate superior flexibility, computational efficiency and accuracy for dealing with complex multi-scale, and multi-physics problems. However, these intelligent models often disregard physical laws in pursuit of error minimization, which leads to certain uncertainties. Therefore, physics-informed machine learning approaches have been developed based on data, guided by physics, and supported by machine learning models. This study summarizes four embedding mechanisms for introducing physical information into machine learning models, including input databased embedding, model architecture-based embedding, loss function-based embedding, and model optimization-based embedding mechanism. These “data + physics” dualdriven intelligent models not only exhibit higher prediction accuracy while adhering to physic laws, but also accelerate the convergence to improve computational efficiency. This paradigm will facilitate the guide developments in solving petroleum engineering problems toward a more comprehensive and efficient direction.

Document Type: Perspective

Cited as: Xie, C., Du, S., Wang, J., Lao, J., Song, H. Intelligent modeling with physics-informed machine learning for petroleum engineering problems. Advances in Geo-Energy Research, 2023, 8(2): 71-75. https://doi.org/10.46690/ager.2023.05.01


Keywords


Physics-informed machine learning, petroleum engineering, data-driven, embedding mechanism

Full Text:

PDF

References


Ashrafi, S. B., Anemangely, M., Sabah, M., et al. Application of hybrid artificial neural networks for predicting rate of penetration (ROP): A case study from Marun oil field. Journal of Petroleum Science and Engineering, 2019, 175: 604-623.

Chen, S., Doolen, G. D. Lattice Boltzmann method for fluid flows. Annual Review of Fluid Mechanics, 1998, 30(1): 329-364.

Chiu, P. H., Wong, J. C., Ooi, C., et al. CAN-PINN: A fast physics-informed neural network based on coupledautomatic–numerical differentiation method. Computer Methods in Applied Mechanics and Engineering, 2022, 395: 114909.

Choubineh, A., Ghorbani, H., Wood, D. A., et al. Improved predictions of wellhead choke liquid critical-flow rates: Modelling based on hybrid neural network training learning based optimization. Fuel, 2017, 207: 547-560.

Du, S., Wang, J., Wang, M., et al. A systematic datadriven approach for production forecasting of coalbed methane incorporating deep learning and ensemble learning adapted to complex production patterns. Energy, 2023, 263: 126121.

Golparvar, A., Zhou, Y., Wu, K., et al. A comprehensive review of pore scale modeling methodologies for multiphase flow in porous media. Advances in Geo-Energy Research, 2018, 2(4): 418-440.

Hughes, T. J. R. The Finite Element Method: Linear Static and Dynamic Finite Element Analysis. New York, USA, Courier Corporation, 2012.

Karplus, M., McCammon, J. A. Molecular dynamics simulations of biomolecules. Nature Structural Biology, 2002, 9(9): 646-652.

Karplus, M., Petsko, G. A. Molecular dynamics simulations in biology. Nature, 1990, 347: 631-639.

Kaydani, H., Mohebbi, A., Baghaie, A. Permeability prediction based on reservoir zonation by a hybrid neural genetic algorithm in one of the Iranian heterogeneous oil reservoirs. Journal of Petroleum Science and Engineering, 2011, 78(2): 497-504.

Kharazmi, E., Zhang, Z., Karniadakis, G. E. Variational physics-informed neural networks for solving partial differential equations. ArXiv preprint, 2019: 1912.00873.

Ling, J., Jones, R., Templeton, J. Machine learning strategies for systems with invariance properties. Journal of Computational Physics, 2016, 318: 22-35.

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.

Mariani, V., Pulga, L., Bianchi, G. M., et al. A Bayesian neural network methodology to predict the liquid phase diffusion coefficient. International Journal of Heat and Mass Transfer, 2020, 161: 120309.

Metropolis, N., Ulam, S. The monte carlo method. Journal of the American Statistical Association, 1949, 44(247): 335-341.

Owoyele, O., Pal, P., Vidal Torreira, A., et al. Application of an automated machine learning-genetic algorithm (AutoMLGA) coupled with computational fluid dynamics simulations for rapid engine design optimization. International Journal of Engine Research, 2022, 23(9): 1586-1601.

Peaceman, D. W. Fundamentals of Numerical Reservoir Simulation. Houston, USA, Elsevier, 2000.

Qi, J., Zhang, K., Xue, X., et al. An evolutionary sequential transfer optimization algorithm for well placement optimization based on task characteristics. SPE Journal, 2022, in press, https://doi.org/10.2118/212870-PA.

Raissi, M., Perdikaris, P., Karniadakis, G. E. Physics informed deep learning (Part I): Data-driven solutions of nonlinear partial differential equations. ArXiv preprint, 2017, 1711.10561.

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.

Rubinstein, R. Y., Kroese, D. P. Simulation and the Monte Carlo Method. Hoboken, USA, JohnWiley & Sons, 2016.

Shan, L., Liu, Y., Tang, M., et al. CNN-BiLSTM hybrid neural networks with attention mechanism for well log prediction. Journal of Petroleum Science and Engineering, 2021, 205: 108838.

Shukla, K., Jagtap, A. D., Karniadakis, G. E. Parallel physics-informed neural networks via domain decomposition. Journal of Computational Physics, 2021, 447: 110683.

Song, H., Du, S., Yang, J., et al. Evaluation of hydraulic fracturing effect on coalbed methane reservoir based on deep learning method considering physical constraints. Journal of Petroleum Science and Engineering, 2022a, 212: 110360.

Song, H., Zhu, J., Wei, C., et al. Data-driven physics-informed interpolation evolution combining historical-predicted knowledge for remaining oil distribution prediction. Journal of Petroleum Science and Engineering, 2022b, 217: 110795.

Sultana, N., Hossain, S. Z., Abusaad, M., et al. Prediction of biodiesel production from microalgal oil using Bayesian optimization algorithm-based machine learning approaches. Fuel, 2022, 309: 122184.

Sun, L., Wang, J. X. Physics-constrained bayesian neural network for fluid flow reconstruction with sparse and noisy data. Theoretical and Applied Mechanics Letters, 2020, 10(3): 161-169.

Tang, P., Zhang, D., Li, H. Predicting permeability from 3D rock images based on CNN with physical information. Journal of Hydrology, 2022, 606: 127473.

Versteeg, H. K., Malalasekera, W. An Introduction to Computational Fluid Dynamics: The Finite Volumn Method. London, UK, Pearson Education, 2007.

Wang, N., Zhang, D., Chang, H., et al. Deep learning of subsurface flow via theory-guided neural network. Journal of Hydrology, 2020, 584: 124700.

Wang, Z., Zhang, K., Chen, G. D., et al. Evolutionary-assisted reinforcement learning for reservoir real-time production optimization under uncertainty. Petroleum Science, 2022, in press, https://doi.org/10.1016/j.petsci.2022.08.016.

Wei, C., Huang, R., Ding, M., et al. Characterization of saturation and pressure distribution based on deep learning for a typical carbonate reservoir in the Middle East. Journal of Petroleum Science and Engineering, 2022, 213: 110442.

Wu, J., Yin, X., Xiao, H. Seeing permeability from images: Fast prediction with convolutional neural networks. Science Bulletin, 2018, 63(18): 1215-1222.

Xue, X., Chen, G., Zhang, K., et al. A divide-and-conquer optimization paradigm for waterflooding production optimization. Journal of Petroleum Science and Engineering, 2022, 211: 110050.

Yan, B., Harp, D. R., Chen, B., et al. A gradient-based deep neural network model for simulating multiphase flow in porous media. Journal of Computational Physics, 2022, 463: 111277.

Yue, Z., Songzheng, Z., Tianshi, L. Bayesian regularization BP Neural Network model for predicting oil-gas drilling cost. Paper Presented at 2011 International Conference on Business Management and Electronic Information, Guangzhou, China, 13-15 May, 2011.

Zeng, L., Ren, W., Shan, L. Attention-based bidirectional gated recurrent unit neural networks for well logs prediction and lithology identification. Neurocomputing, 2020, 414: 153-171.

Zhang Z. A physics-informed deep convolutional neural network for simulating and predicting transient Darcy flows in heterogeneous reservoirs without labeled data. Journal of Petroleum Science and Engineering, 2022, 211: 110179.




DOI: https://doi.org/10.46690/ager.2023.05.01

Refbacks

  • There are currently no refbacks.


Copyright (c) 2023 The Author(s)

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Copyright ©2018. All Rights Reserved