Abstract
Understanding subsurface distribution features is crucial for reliable sedimentary facies modeling. Obtaining the distribution feature of complex subsurface sedimentary facies is challenging, especially non-stationary sedimentary facies which are location-specific, well-ordered, facies sequence (e.g., deltas). The application of machine learning algorithms has potential to assist with this imaging problem, particularly using the Generative Adversarial Networks (GANs) method. Recently, GANs have proven to be outstanding for unsupervised learning on complex distributions of training images. However, in most GAN-based model convolution processes, the information in a local area is computationally invalid for reproducing the global features of training images. To remedy this, we introduce an advanced Self-Attention Generative Adversarial Network (SAGAN) for subsurface geological facies modeling. Compared with the basic GANs, SAGANs introduce a self-attention mechanism to attain details from a long distance in the image, reproducing global features of training images. SAGAN case studies involve stationary channels and non-stationary delta facies. We use probability maps, variograms, connectivity functions, and visualization results to evaluate and compare our simulation realizations. For channel cases, SAGANs' realizations can reproduce different distributions of channels and point bars in different river systems. For the delta case, the SAGAN method shows a better ability to reproduce delta non-stationary characteristics than the MPS and basic GAN methods. All results are of high quality and diversity, reproduced the known geological sedimentary patterns, and compared with the basic GANs, SAGANs can better reproduce the global features of non-stationary training images. It is demonstrated that our first proposed SAGANs for geological facies modeling represent a powerful method for reproducing depositional facies distribution pattern.
NSTL