1887

Abstract

Summary

In this study, the neural network is used to estimate the amount of water stored in a porous reservoir from seismic data. To generate the training data for the neural network, a coupled poroviscoelastic-viscoelastic wave propagation model is solved using a three-dimensional (3D) discontinuous Galerkin method coupled with an Adams-Bashforth time stepping scheme. In addition, the effect of the unknown source wavelet is normalized using a deconvolution- based approach. Results indicate that the proposed neural network approach is applicable to estimate the wave content of a porous reservoir with a variety of noise amplitudes while uninteresting parameters can be successfully ignored.

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/content/papers/10.3997/2214-4609.202310514
2023-06-05
2026-01-23
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References

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