Applications of Geological Features Style Mixing for Reservoir History Matching

Summary Ensemble-based data assimilation, when combined with generative adversarial networks (GANs), has proved effective for history matching based on production data in petroleum engineering research. However, the geological realizations produced are often constrained by the characteristics of the initial ensemble and require extensive training data sets to achieve diversity. To address these limitations, we introduce a novel method that enhances history matching in reservoir simulations by integrating a geological-style-mixing approach with GAN-based optimization (StyleGAN). This method addresses the challenges of capturing complex geological features and heterogeneities that influence reservoir performance. We use the StyleGAN architecture to generate diverse geological scenarios with enhanced style diversity. By adapting the style-mixing mechanism of StyleGAN for geological modeling, we developed a framework capable of producing a variety of geological styles. Each style possesses unique characteristics that are distinct from those of the initial ensemble. This approach combines the styles from assorted geological realizations to create new realizations that exhibit a broad spectrum of geological features, thereby significantly improving the history-matching process. The effectiveness of our method is demonstrated through case studies involving a 2D binary permeability field, a 2D Gaussian permeability field, and a 3D bimodal log permeability distribution. Our optimized models displayed considerable improvement over conventional GAN-based optimizations. The correlation with the reference model increased from 0.94 to 0.98 for the binary permeability field, from 0.97 to 0.99 for the Gaussian permeability field, and from 0.97 to 0.99 for the 3D bimodal permeability field. In addition, the production rate matching error improved from 66% to 86% for the binary permeability field, from 81% to 93% for the Gaussian permeability field, and from 81% to 88% for the 3D bimodal log permeability field, with substantial reductions in the root mean squared error (RMSE) compared with the initial model. The proposed method was compared with the previously developed convolutional neural network-principal component analysis (CNN-PCA) and demonstrated similar history-matching performance. However, it qualitatively showed better preservation of the geological style of the trained reservoir ensemble. These findings demonstrate that integrating a geological-style-mixing approach with GAN-based optimization presents a promising avenue for overcoming the limitations of current ensemble-based history-matching methods, particularly in scenarios characterized by high geological complexity and data uncertainties. This research advances history-matching methodologies and signifies the potential of machine learning and artificial intelligence in enhancing reservoir simulation and management.