[论文解读] Generating Geological Facies Models with Fidelity to Diversity and Statistics of Training Images using Improved Generative Adversarial Networks

Lingchen Zhu, Tuanfeng Zhang|arXiv (Cornell University)|Sep 23, 2019

Drilling and Well Engineering被引用 4

一句话总结

本文提出Info-WGAN，一种改进的生成对抗网络，结合InfoGAN、Wasserstein距离和梯度惩罚，可生成地质上合理的相带模型，实现无偏、多样化且等概率的实现。通过解耦潜在码以表示沉积类型（如河流相、三角洲相），该方法确保了训练数据的忠实统计再现，并在储层建模中实现客观的不确定性量化。

ABSTRACT

This paper presents a methodology and workflow that overcome the limitations of the conventional Generative Adversarial Networks (GANs) for geological facies modeling. It attempts to improve the training stability and guarantee the diversity of the generated geology through interpretable latent vectors. The resulting samples are ensured to have the equal probability (or an unbiased distribution) as from the training dataset. This is critical when applying GANs to generate unbiased and representative geological models that can be further used to facilitate objective uncertainty evaluation and optimal decision-making in oil field exploration and development. We proposed and implemented a new variant of GANs called Info-WGAN for the geological facies modeling that combines Information Maximizing Generative Adversarial Network (InfoGAN) with Wasserstein distance and Gradient Penalty (GP) for learning interpretable latent codes as well as generating stable and unbiased distribution from the training data. Different from the original GAN design, InfoGAN can use the training images with full, partial, or no labels to perform disentanglement of the complex sedimentary types exhibited in the training dataset to achieve the variety and diversity of the generated samples. This is accomplished by adding additional categorical variables that provide disentangled semantic representations besides the mere randomized latent vector used in the original GANs. By such means, a regularization term is used to maximize the mutual information between such latent categorical codes and the generated geological facies in the loss function. Furthermore, the resulting unbiased sampling by Info-WGAN makes the data conditioning much easier than the conventional GANs in geological modeling because of the variety and diversity as well as the equal probability of the unconditional sampling by the generator.

研究动机与目标

• 解决传统GAN在地质相带建模中出现的模式崩溃和采样偏差问题。
• 确保生成的地质实现具有统计代表性且概率相等，从而实现客观的不确定性评估。
• 通过解耦的潜在码实现对地质特征（如河流相与三角洲相）的可解释性控制。
• 通过生成多样化、无偏的实现，提升储层模拟中的数据条件化效果，加快收敛速度。
• 通过与训练数据和生成样本之间的e-type图及直方图对比，验证模型保真度。

提出的方法

• 将InfoGAN与Wasserstein GAN（WGAN-GP）结合，学习解耦的类别潜在码（c）以及随机噪声（z），以实现可解释的生成。
• 在损失函数中引入互信息最大化，确保每个类别潜在码（c）对应有意义的地质特征，如河流相或三角洲相。
• 应用Wasserstein距离和梯度惩罚（GP）以稳定训练并防止模式崩溃。
• 采用共享主干网络用于判别器和分类器，分别设置真实/虚假分类和标签预测的输出头。
• 采用一种新型带动量的随机梯度下降与梯度向量归一化方法，加速数据条件化过程中的收敛。
• 通过比较训练数据与生成样本之间的e-type图和像素级直方图，验证模型质量。

实验结果

研究问题

• RQ1 Info-WGAN能否生成保留训练图像统计多样性与分布的地质相带模型？
• RQ2 解耦的类别潜在码（c）是否能实现对生成模型中沉积相类型（如河流相与三角洲相）的可解释性控制？
• RQ3 当训练数据包含混合或不平衡的相类型时，Info-WGAN是否仍能避免模式崩溃？
• RQ4 生成的实现是否具有相等的概率分布，从而支持储层建模中的客观不确定性量化？
• RQ5 与传统GAN相比，Info-WGAN在井数据条件化中如何提升收敛速度和精度？

主要发现

• Info-WGAN成功生成了与训练数据在多样性和统计保真度方面一致的地质相带模型，有效避免了模式崩溃。
• 解耦的潜在码（c）实现了对地质特征的可解释性控制，例如将[0,1]分配给河流相，[1,0]分配给三角洲相。
• 生成样本表现出相等的概率分布，支持通过e-type图实现客观的不确定性评估。
• 从生成样本计算得到的e-type图与训练数据高度一致，证实了统计一致性。
• 由于实现的多样性和无偏性，该方法在数据条件化中实现了更快的收敛速度，即使在井点密集区域亦然。
• e-type图的直方图对比结果表明，Info-WGAN忠实再现了真实数据分布，验证了模型的可靠性。