Reservoir prediction is a significant issue in seismic interpretation, and it is difficult to reach a tradeoff point for the reservoir prediction accuracy and spatial continuity. Nowadays, though numerous machine learning methods have been widely applied in reservoir prediction, so few available well-logging labels are still a major obstacle for improving prediction performance. Considering for such a critical factor, we propose a semisupervised deep-learning framework, in which the closed-loop convolutional neural network (CNN). and virtual well-logging labels are used. The closed-loop CNN, which is consisting of the predictive and generative subnetworks, can be trained directly by using the seismic attribute data not only with well-logging labels but also without well-logging labels. The virtual well-logging labels (Vl) are generated by fusing the results of two existing reservoir predicting methods, one based on polynomial linear regression and the other based on CNN. Vl contributes to improve the spatial continuity and accuracy of the predicted reservoir as constraint items in network training process. Finally, cross-validation experiments on real-field data are carried out, and 3-D field reservoir prediction results show that the proposed method outperforms several existing machine-learning-based methods.

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基于闭环CNN和虚拟测井标签的储层预测

储层预测是地震解释中的一个重要问题，储层预测精度和空间连续性很难达到折衷点。如今，尽管许多机器学习方法已广泛应用于储层预测，但可用的测井标签很少仍然是提高预测性能的主要障碍。考虑到这样一个关键因素，我们提出了一个半监督的深度学习框架，其中闭环卷积神经网络（CNN）。和虚拟测井标签被使用。由预测子网络和生成子网络组成的闭环 CNN 可以直接使用地震属性数据进行训练，不仅可以使用测井标签，也可以不使用测井标签。虚拟测井标签 (VI) 是通过融合两种现有储层预测方法的结果生成的，一种基于多项式线性回归，另一种基于 CNN。Vl作为网络训练过程中的约束项，有助于提高预测储层的空间连续性和准确性。最后，对实场数据进行了交叉验证实验，3-D 油田储层预测结果表明，所提出的方法优于现有的几种基于机器学习的方法。