Geological model compression is crucial for making large and complex models more manageable.By reducing the size of these models,compression techniques enable efficient storage,enhance computational efficiency,making ...Geological model compression is crucial for making large and complex models more manageable.By reducing the size of these models,compression techniques enable efficient storage,enhance computational efficiency,making it feasible to perform complex simulations and analyses in a shorter time.This is particularly important in applications such as reservoir management,groundwater hydrology,and geological carbon storage,where large geomodels with millions of grid cells are common.This study presents a comprehensive overview of previous work on geomodel compression and introduces several autoencoder-based deep-learning architectures for low-dimensional representation of modified Bruggefield geomodels.The compression and reconstruction efficiencies of autoencoders(AE),variational autoencoders(VAE),vector-quantized variational autoencoders(VQ-VAE),and vector-quantized variational autoencoders 2(VQ-VAE2)were tested and compared to the traditional singular value decomposition(SVD)method.Results show that the deep-learning-based approaches significantly outperform SVD,achieving higher compression ratios while maintaining or even exceeding the reconstruction quality.Notably,VQ-VAE2 achieves the highest compression ratio of 667:1 with a structural similarity index metric(SSIM)of 0.92,far surpassing the 10:1 compression ratio of SVD with a SSIM of 0.9.The result of this work shows that,unlike traditional approaches,which often rely on linear transformations and can struggle to capture complex,non-linear relationships within geological data,VQ-VAE's use of vector quantization helps in preserving high-resolution details and enhances the model's ability to generalize across varying geological complexities.展开更多
基金supported by ConocoPhillips through the DataDriven Intelligent Engineering and Characterization(DICE)research group.
文摘Geological model compression is crucial for making large and complex models more manageable.By reducing the size of these models,compression techniques enable efficient storage,enhance computational efficiency,making it feasible to perform complex simulations and analyses in a shorter time.This is particularly important in applications such as reservoir management,groundwater hydrology,and geological carbon storage,where large geomodels with millions of grid cells are common.This study presents a comprehensive overview of previous work on geomodel compression and introduces several autoencoder-based deep-learning architectures for low-dimensional representation of modified Bruggefield geomodels.The compression and reconstruction efficiencies of autoencoders(AE),variational autoencoders(VAE),vector-quantized variational autoencoders(VQ-VAE),and vector-quantized variational autoencoders 2(VQ-VAE2)were tested and compared to the traditional singular value decomposition(SVD)method.Results show that the deep-learning-based approaches significantly outperform SVD,achieving higher compression ratios while maintaining or even exceeding the reconstruction quality.Notably,VQ-VAE2 achieves the highest compression ratio of 667:1 with a structural similarity index metric(SSIM)of 0.92,far surpassing the 10:1 compression ratio of SVD with a SSIM of 0.9.The result of this work shows that,unlike traditional approaches,which often rely on linear transformations and can struggle to capture complex,non-linear relationships within geological data,VQ-VAE's use of vector quantization helps in preserving high-resolution details and enhances the model's ability to generalize across varying geological complexities.
