Abstract
In this article, a constraint interpretable double parallel neural network (CIDPNN) has been proposed to characterize the response relationships between inputs and outputs. The shortcut connecting synapses of the network are utilized to measure the association strength quantitatively, inferring the information flow during the learning process in an intuitive manner. To guarantee the physical significance of the model parameters, the weight matrices are constraint by the square function operator in the non-negative interval. Meanwhile, the sparsity of model parameters has been fully improved by the constraint. Hence, the proposed network can retrieval the critical geophysical parameters sparsely and robustly, through the injection and production signals from the reservoir recovery history. Finally, a synthetic reservoir experiment is elaborated to demonstrate the effectiveness of the proposed method.
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References
Yousef, A.A., et al.: A capacitance model to infer interwell connectivity from production and injection rate fluctuations. SPE Reservoir Eval. Eng. 9(06), 630–646 (2006)
Sayarpour, M., et al.: The use of capacitance–resistance models for rapid estimation of waterflood performance and optimization. J. Petrol. Sci. Eng. 69(3–4), 227–238 (2009)
de Holanda, R.W., et al.: A state-of-the-art literature review on capacitance resistance models for reservoir characterization and performance forecasting. Energies 11(12), 3368 (2018)
Zhao, H., et al.: A physics-based data-driven numerical model for reservoir history matching and prediction with a field application. SPE J. 21(06), 2175–2194 (2016)
Guo, Z., Reynolds, A.C., Zhao, H.: A physics-based data-driven model for history matching, prediction, and characterization of waterflooding performance. SPE J. 23(02), 367–395 (2017)
Guo, Z., Reynolds, A.C.: INSIM-FT-3D: a three-dimensional data-driven model for history matching and waterflooding optimization. In: SPE Reservoir Simulation Conference, Galveston, Texas, USA, Paper number, SPE-193841-MS. Society of Petroleum Engineers (2019)
Zhao, H., et al.: Flow-path tracking strategy in a data-driven interwell numerical simulation model for waterflooding history matching and performance prediction with infill wells. SPE J. 25(02), 1007–1025 (2020)
Panda, M.N., Chopra, A.K.: An integrated approach to estimate well interactions. In: SPE India Oil and Gas Conference and Exhibition, New Delhi, India, Paper number SPE-39563-MS. Society of Petroleum Engineers (1998)
Artun, E.: Erratum to: Characterizing interwell connectivity in waterflooded reservoirs using data-driven and reduced-physics models: a comparative study. Neural Comput. Appl. 28(7), 1905–1906 (2017)
Rosen, L.D., Weil, M.M.: The Organization of Behavior: A Neuropsychological Theory. Psychology Press (2005)
He, K., et al.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, pp. 770–778. IEEE (2016)
Ersoy, O.K., Deng, S.: Parallel, self-organizing, hierarchical neutral networks with continuous inputs and outputs. In: Proceedings of the 24th Annual Hawaii International Conference on System Sciences, Kauai, HI, USA, pp. 486–492 IEEE (1991)
Wang, J., et al.: Convergence of gradient method for double parallel feedforward neural network. Int. J. Numer. Anal. Model. 8(3), 484–495 (2011)
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Jiang, Y., Zhang, H., Wang, J., Zhang, K., Pal, N.R. (2021). Constraint Interpretable Double Parallel Neural Network and Its Applications in the Petroleum Industry. In: Huang, DS., Jo, KH., Li, J., Gribova, V., Bevilacqua, V. (eds) Intelligent Computing Theories and Application. ICIC 2021. Lecture Notes in Computer Science(), vol 12836. Springer, Cham. https://doi.org/10.1007/978-3-030-84522-3_34
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DOI: https://doi.org/10.1007/978-3-030-84522-3_34
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