In-situ stress is essential in shale reservoir fracturing, driving oil and gas migration and informing wellbore stability and drilling optimization. The accurate prediction of 3D in-situ stress is inseparable from seismic data. However, existing methods predominantly rely on empirical formulas or simplified assumptions, which limit their accuracy in representing the real distribution of in-situ stress. Furthermore, these methods often predict in-situ stress from a single factor, leading to high uncertainty. To address these, we propose a method for predicting 3D in-situ stress that leverages a hybrid Capsule Network-Bidirectional Long Short-Term Memory (CapsNet-BiLSTM) model. This approach takes into account various factors, such as geological features and seismic attributes, to achieve more accurate predictions. First, we analyze the structural characteristics of shale formations and use rock petrophysical knowledge to reasonably filter input data, eliminating the impact of redundant parameters on in-situ stress prediction. Then, to overcome the limitations of traditional deep learning models in capturing correlations within complex data structures, we construct a CapsNet-BiLSTM network model. This model integrates the spatial relationship modeling capability of CapsNet and the temporal modeling capability of BiLSTM, better accounting for the anisotropic features and temporal sequence information of shale reservoirs. Applying this method to a study area in the Sichuan Basin demonstrates that the constructed CapsNet-BiLSTM hybrid model accurately predicts in-situ stress values, effectively capturing the spatial distribution patterns of complex in-situ stress within shale reservoirs, thus proving the effectiveness and potential of our method in geological engineering applications for shale oil and gas reservoirs. This hybrid model-based prediction method not only improves the accuracy of in-situ stress prediction but also provides a valuable methodological and technical support for scientific research and engineering practices in related fields.

中文翻译：

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基于 CapsNet-BiLSTM 混合模型的页岩储层三维原位应力预测


原位应力在页岩储层压裂中至关重要，可驱动石油和天然气迁移，并为井眼稳定性和钻井优化提供信息。三维原位应力的准确预测离不开地震数据。然而，现有方法主要依赖于经验公式或简化假设，这限制了它们在表示现场应力实际分布方面的准确性。此外，这些方法通常从单个因素预测原位应力，从而导致高度不确定性。为了解决这些问题，我们提出了一种预测 3D 原位应力的方法，该方法利用混合胶囊网络-双向长短期记忆 （CapsNet-BiLSTM） 模型。这种方法考虑了各种因素，例如地质特征和地震属性，以实现更准确的预测。首先，分析页岩地层的结构特征，并利用岩石岩石物理知识对输入数据进行合理过滤，消除冗余参数对原位应力预测的影响。然后，为了克服传统深度学习模型在捕获复杂数据结构中的相关性方面的局限性，我们构建了一个 CapsNet-BiLSTM 网络模型。该模型融合了 CapsNet 的空间关系建模能力和 BiLSTM 的时间建模能力，更好地考虑了页岩储层的各向异性特征和时间序列信息。 将该方法应用于四川盆地某研究区，表明所构建的CapsNet-BiLSTM混合模型能够准确预测原位应力值，有效捕捉页岩油气藏内复杂原位应力的空间分布模式，从而证明了该方法在页岩油气藏地质工程应用中的有效性和潜力。这种基于混合模型的预测方法不仅提高了原位应力预测的准确性，而且为相关领域的科学研究和工程实践提供了有价值的方法论和技术支持。