Rule-based reservoir modeling methods integrate geological depositional process concepts to generate reservoir models that capture realistic geologic features for improved subsurface predictions and uncertainty models to support development decision making. However, the robust and direct conditioning of these models to subsurface data, such as well logs, core descriptions, and seismic inversions and interpretations, remains as an obstacle for the broad application as a standard subsurface modeling technology. We implement a machine learning-based method for fast and flexible data conditioning of rule-based models. This study builds on a rule-based modeling method for deep-water lobe reservoirs. The model has three geological inputs: (1) the depositional element geometry, (2) the compositional exponent for element stacking pattern, and (3) the distribution of petrophysical properties with hierarchical trends conformable to the surfaces. A deep learning-based workflow is proposed for robust and non-iterative data conditioning. First, a generative adversarial network learns salient geometric features from the ensemble of the training rule-based models. Then, a new rule-based model is generated and a mask is applied to remove the model near local data along the well trajectories. Last, semantic image inpainting restores the mask with the optimum generative adversarial network realization that is consistent with both local data and the surrounding model. For the deep-water lobe example, the generative adversarial network learns the primary geological spatial features to generate reservoir realizations that reproduce hierarchical trend as well as the surface geometries and stacking pattern. Moreover, the trained generative adversarial network explores the latent reservoir manifold and identifies the ensemble of models to represent an uncertainty model. Semantic image inpainting determines the optimum replacement for the near-data mask that is consistent with the local data and the rest of the model. This work results in subsurface models that accurately reproduce reservoir heterogeneity, continuity, and spatial distribution of petrophysical parameters while honoring the local well data constraints.

中文翻译：

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通过机器学习和生成对抗网络将井数据调节为基于规则的叶模型

基于规则的储层建模方法整合了地质沉积过程概念，以生成捕捉现实地质特征的储层模型，以改进地下预测和不确定性模型，以支持开发决策。然而，这些模型对地下数据（例如测井、岩心描述以及地震反演和解释）的稳健和直接调节仍然是作为标准地下建模技术广泛应用的障碍。我们实现了一种基于机器学习的方法，用于对基于规则的模型进行快速灵活的数据调节。本研究建立在基于规则的深水叶状储层建模方法之上。该模型具有三个地质输入：(1) 沉积元素几何形状，(2) 元素堆积模式的成分指数，(3) 具有符合地表的分级趋势的岩石物理特性分布。提出了一种基于深度学习的工作流程，用于稳健且非迭代的数据调节。首先，生成对抗网络从训练基于规则的模型的集合中学习显着的几何特征。然后，生成一个新的基于规则的模型，并应用掩码来移除沿井轨迹靠近局部数据的模型。最后，语义图像修复使用与本地数据和周围模型一致的最佳生成对抗网络实现来恢复掩码。对于深水波瓣的例子，生成对抗网络学习主要的地质空间特征，以生成再现层次趋势以及地表几何形状和堆积模式的储层实现。此外，经过训练的生成对抗网络探索潜在的储层流形并识别模型集合以表示不确定性模型。语义图像修复确定了与局部数据和模型其余部分一致的近数据掩码的最佳替换。这项工作产生的地下模型可以准确再现储层非均质性、连续性和岩石物理参数的空间分布，同时遵守当地井数据的限制。训练有素的生成对抗网络探索潜在的储层流形并识别模型集合以表示不确定性模型。语义图像修复确定了与局部数据和模型其余部分一致的近数据掩码的最佳替换。这项工作产生的地下模型可以准确再现储层非均质性、连续性和岩石物理参数的空间分布，同时遵守当地井数据的限制。训练有素的生成对抗网络探索潜在的储层流形并识别模型集合以表示不确定性模型。语义图像修复确定了与局部数据和模型其余部分一致的近数据掩码的最佳替换。这项工作产生的地下模型可以准确再现储层非均质性、连续性和岩石物理参数的空间分布，同时遵守当地井数据的限制。