A modified generative adversarial nets integrated with stochastic approach for realizing super-resolution reservoir simulation

Simulations and seismic inversions exhibit good performance in reservoir modeling task for the steady performance of conventional techniques. However, they still hardly meet the high demand of petroleum exploration since the impossibility of reaching high resolution both in vertical and lateral directions. Furthermore, simulations can only provide high-resolution results near loggings, while seismic inversions usually contain band-limited problems. Therefore, we present the modified generative adversarial nets with a decoder (DeGAN) as a novel approach, which is integrated with sequential simulation to realize super-resolution reservoir simulation. Specifically, the proposed method provides a geological model with high vertical resolution and optimized by the Zeoppritz function, introducing logging and seismic data simultaneously. After resampling and warping, DeGAN can be trained by these data sets and supplies a structure to generate high-frequency parts for reconstructing a super-resolution simulation of a subsurface profile. The proposed method presents the three-flow architecture of DeGAN to balance the contributions of three neural network models and utilizes this strategy in an offshore area successfully. By introducing multiple data sets, density experiments demonstrate that this approach can provide density profile with super-resolution for revealing possible thin layers, and the frequency distribution is in accord with loggings. The positive result verifies the effectiveness of this approach for providing a super-resolution simulation to supply a solution to the problem of the band-limited profile in seismic inversion