Abstract
The presented paper is devoted to the numerical study of the applicability of 1D seismic inversion and 2D machine learning based inversion for fracture model reconstruction. Seismic inversion is used to predict reservoir properties. Standard version is based on a one-dimensional convolutional model, but real geological media are more complex, and therefore it is necessary to determine conditions where seismic inversion gives acceptable results. For this purpose, the work carries out a comparative analysis of one-dimensional and two-dimensional convolutional modeling. Also, machine learning methods have been adopted for 2D fracture model reconstruction. We use UNet architecture and 2D convolutional model to create a training dataset. We perform numerical experiments for a realistic synthetic model from Eastern Siberia and Sigsbee model.
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Acknowledgements
The work is supported by RSF grant 21-71-20002. The numerical results were obtained using the computational resources of Peter the Great Saint-Petersburg Polytechnic University Supercomputing Center (scc.spbstu.ru).
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Protasov, M., Kenzhin, R., Dmitrachkov, D., Pavlovskiy, E. (2023). Seismic Inversion for Fracture Model Reconstruction: From 1D Inversion to Machine Learning. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2023. ICCSA 2023. Lecture Notes in Computer Science, vol 13957. Springer, Cham. https://doi.org/10.1007/978-3-031-36808-0_7
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