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Artificial neural networks have been widely applied in reservoir engineering. As a powerful tool, it changes the way to find solutions in reservoir simulation profoundly. Deep learning networks exhibit robust learning capabilities, enabling them not only to detect patterns in data, but also uncover underlying physical principles, incorporate prior knowledge of physics, and solve complex partial differential equations. This work presents the latest research advancements in the field of petroleum reservoir engineering, covering three key research directions based on artificial neural networks: data-driven methods, physics driven artificial neural network partial differential equation solver, and data and physics jointly driven methods. In addition, a wide range of neural network architectures are reviewed, including fully connected neural networks, convolutional neural networks, recurrent neural networks, and so on. The basic principles of these methods and their limitations in practical applications are also outlined. The future trends of artificial intelligence methods for oil and gas reservoir development are further discussed. The large language models are the most advanced neural networks so far, it is expected to be applied in reservoir simulation to predict the development performance.
Almajid, M. M., Abu-Al-Saud, M. O. Prediction of porous media fluid flow using physics informed neural networks. Journal of Petroleum Science and Engineering, 2022, 208: 109205.
Arps, J. J. Analysis of decline curves. Transactions of the AIME, 1945, 160(1): 228-247.
Breiman, L. Random forests. Machine Learning, 2001, 45: 5-32.
Brown, M., Ozkan, E., Raghavan, R., et al. Practical Solutions for Pressure-Transient Responses of Fractured Horizontal Wells in Unconventional Shale Reservoirs. SPE Reservoir Evaluation & Engineering, 2011, 14(6): 663-676.
Cui, Q., Zhao, Y., Zhang, L., et al. A semianalytical model of fractured horizontal well with hydraulic fracture network in shale gas reservoir for pressure transient analysis. Advances in Geo-Energy Research, 2023, 8(3): 193-205.
Du, E., Liu, Y., Cheng, Z., et al. Production forecasting with the interwell interference by integrating graph convolutional and long short-term memory neural network. SPE Reservoir Evaluation & Engineering, 2022a, 25(2): 197-213.
Du, L., Liu, Y., Xue, L., et al. A deep learning framework using graph convolutional networks for adaptive correction of interwell connectivity and gated recurrent unit for performance prediction. SPE Reservoir Evaluation & Engineering, 2022b, 25(4): 815-31.
Feng, J., He, X., Teng, Q., et al. Reconstruction of porous media from extremely limited information using conditional generative adversarial networks. Physical Review E, 2019, 100(3): 033308.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., et al. Generative adversarial nets. Advances in Neural Information Processing Systems, 2014, 27: 139-144.
Gringarten, A. C., Ramey, H. J. The use of source and Green’s functions in solving unsteady-flow problems in reservoirs. Society of Petroleum Engineers Journal, 1973, 13(5): 285-296.
Huang, H., Gong, B., Sun, W. A deep-learning-based graph neural network-long-short-term memory model for reservoir simulation and optimization with varying well controls. SPE Journal, 2023, in press, https://doi.org/10.2118/215842-PA.
Karniadakis, G. E., Kevrekidis, I. G., Lu, L., et al. Physics-informed machine learning. Nature Reviews Physics, 2021, 3(6): 422-40.
Kochkov, D., Smith, J. A., Alieva, A., et al. Machine learning-accelerated computational fluid dynamics. Proceedings of the National Academy of Sciences of the United States of America, 2021, 118(21): e2101784118.
Li, D., Shen L., Zha, W., et al. Physics-constrained deep learning for solving seepage equation. Journal of Petroleum Science and Engineering, 2021b, 206: 109046.
Li, J., Zhang, D., Wang, N., et al. Deep learning of two-phase flow in porous media via theory-guided neural networks. SPE Journal, 2022, 27(2): 1176-1194.
Liu, X., Zha, W., Li, D., et al. Automatic well test interpretation method for circular reservoirs with changing wellbore storage using one-dimensional convolutional neural network. Journal of Energy Resources Technology, 2023, 145(3): 033201.
Mattar, L., McNeil, R. The “flowing” gas material balance. Journal of Canadian Petroleum Technology, 1998, 37(2).
Mosser, L., Dubrule, O., Blunt, M. J. Reconstruction of three-dimensional porous media using generative adversarial neural networks. Physical Review E, 2017, 96(4): 043309.
Park, J., Datta-Gupta, A., Singh, A., et al. Hybrid physics and data-driven modeling for unconventional field development and its application to US onshore basin. Journal of Petroleum Science and Engineering, 2021, 206: 109008.
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.
Silva, V. L. S., Salinas, P., Jackson, M. D., et al. Machine learning acceleration for nonlinear solvers applied to multiphase porous media flow. Computer Methods in Applied Mechanics and Engineering, 2021, 384: 113989.
Wang, Y., Lin, G. Efficient deep learning techniques for multiphase flow simulation in heterogeneous porousc media. Journal of Computational Physics, 2020, 401: 108968.
Wen, G., Li, Z., Azizzadenesheli, K., et al. U-FNO—An enhanced Fourier neural operator-based deep-learning model for multiphase flow. Advances in Water Resources, 2022, 163: 104180.
Xue, L., Gu, S., Mi, L., et al. An automated data-driven pressure transient analysis of water-drive gas reservoir through the coupled machine learning and ensemble Kalman filter method. Journal of Petroleum Science and Engineering, 2022, 208: 109492.
Xue, L., Liu, Y., Xiong, Y., et al. A data-driven shale gas production forecasting method based on the multi-objective random forest regression. Journal of Petroleum Science and Engineering, 2021, 196: 107801.
Xue, L., Wang, J., Han, J., et al. Gas well performance prediction using deep learning jointly driven by decline curve analysis model and production data. Advances in Geo-Energy Research, 2023, 8(3): 159-169.
Yan, B., Harp, D. R., Pawar, R. J. A gradient-based deep neural network model for simulating multiphase flow in porous media. Journal of Computational Physics, 2021b, 464: 111277.
Yang, R., Liu, X., Yu, R., et al. Long short-term memory suggests a model for predicting shale gas production. Applied Energy, 2022, 322: 119415.
Zha, W., Li, X., Xing, Y., et al. Reconstruction of shale image based on Wasserstein Generative Adversarial Networks with gradient penalty. Advances in Geo-Energy Research, 2020, 4(1): 107-114.
Zha, W., Liu, Y., Wan, Y., et al. Forecasting monthly gas field production based on the CNN-LSTM model. Energy, 2022, 260: 124889.
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.
Zhang, K., Lin, N., Yang, J., et al. Predicting gas-bearing distribution using DNN based on multi-component seismic data: Quality evaluation using structural and fracture factors. Petroleum Science, 2022, 19(4): 1566-1581.
Zhang, N., Wei, M., Fan, J., et al. Development of a hybrid scoring system for EOR screening by combining conventional screening guidelines and random forest algorithm. Fuel, 2019, 256: 115915.
Zhang, Q., Su, Y., Wang, W., et al. Performance analysis of fractured wells with elliptical SRV in shale reservoirs. Journal of Natural Gas Science and Engineering, 2017, 45: 380-390.
Zhang, T., Xia, P., Lu, F. 3D reconstruction of digital cores based on a model using generative adversarial networks and variational auto-encoders. Journal of Petroleum Science and Engineering, 2021, 207: 109151.
Zhu, Y., Zabaras, N., Koutsourelakis, P. -S., et al. Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data. Journal of Computational Physics, 2019, 394: 56-81.
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