We study the applicability of Generative Adversarial Networks (GANs) for generating the synthetic data related to well construction and geological characterisation of the near-wellbore area. We focus on 1D mud logs (time-series) and 2D core images. GANs are known to have difficulties with their quality assessment in general. Moreover, generic GAN’s performance assessment methods cannot be suitable for the petroleum domain. A petroleum engineer expects the GANs to generate data with specific physical and geological properties, not just a colourful picture. We have trained over 40 GAN models and generated synthetic data with them. Then, we have involved several experts to analyse the generated data in order to address the question of whether it is possible to substitute human analysis with a mathematical metric. We found that some quantitative mathematical metrics can represent our experts’ perceptions. In particular, we show that for 2D core images, Mode Score metric with standard inception v3 model is the best proxy for all considered qualitative metrics of expert’s perception according to the Kendall correlation (for two qualitative metrics the correlation is strong, the absolute value is above 0.7, and for other two it is moderate, the absolute value is between 0.5 and 0.7); for mud logs time-series, Mode Score and Frechet Inception Distance with the InceptionTime model provide the strong (above 0.7) correlation with objects reconstruction quality, whereas Inception Score has almost strong correlation (with Kendalls’-tau coefficient 0.69) with experts’ perception of objects generation quality. With these results, experts manual annotation of generated objects during GAN model selection process can be reduced to calculating the corresponding quantitative metrics.

我们研究了生成对抗网络 (GAN) 在生成与井建设和近井筒区域地质特征相关的合成数据方面的适用性。我们专注于一维泥浆测井（时间序列）和二维岩心图像。众所周知，GAN 通常在质量评估方面存在困难。此外，通用 GAN 的性能评估方法不适用于石油领域。一位石油工程师希望 GAN 生成具有特定物理和地质特性的数据，而不仅仅是彩色图片。我们已经训练了 40 多个 GAN 模型并用它们生成了合成数据。然后，我们邀请了几位专家来分析生成的数据，以解决是否可以用数学度量代替人工分析的问题。我们发现一些定量的数学指标可以代表我们专家的看法。特别是，我们表明，对于 2D 核心图像，具有标准 inception v3 模型的 Mode Score 度量是根据 Kendall 相关性考虑的专家感知的所有定性度量的最佳代理（对于两个定性度量，相关性很强，绝对值是0.7以上，其他两个为中等，绝对值在0.5-0.7之间）；对于泥浆测井时间序列，InceptionTime 模型的 Mode Score 和 Frechet Inception Distance 与对象重建质量具有很强的相关性（高于 0.7），而 Inception Score 与专家的感知几乎有很强的相关性（Kendalls'-tau 系数为 0.69）对象生成质量。有了这些结果，