Stochastic discrete fracture network modeling in shale reservoirs via integration of seismic attributes and petrophysical data
Stochastic discrete fracture network modeling in shale reservoirs via integration of seismic attributes and petrophysical data
复制标题
通过整合地震属性和岩石物理数据进行页岩储层随机离散裂缝网络建模
DOI：
10.1190/int-2020-0210.1
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发表时间：
2021
期刊：
影响因子：
0.3
通讯作者：
Yongchae Cho
中科院分区：
文献类型：
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作者：
Yongchae Cho
天然裂缝网络及其地质力学性质的预测仍然是非常规油藏描述的一个挑战。由于天然裂缝是高度不均匀的并且是亚地震尺度的，因此将岩石物理数据（即，岩心和测井记录）与地震数据的结合对于建立可靠的天然裂缝模型是重要的。因此，我开发了一个集成的和随机的方法，离散裂缝网络建模与现场数据实验。在该方法中，我首先进行地震属性分析，突出地震数据中的不连续性。然后，我外推测井数据，包括本地化，但高置信度的信息。通过使用包括地震和测井的裂缝强度模型，我建立了最终的天然裂缝模型，该模型可以用作后续地质力学分析的背景模型，例如水力裂缝扩展的模拟。因此，我们的工作流程结合多尺度数据的随机方法构建了一个可靠的天然裂缝模型。通过与测井数据的良好吻合，验证了构造的裂缝分布。
The prediction of natural fracture networks and their geomechanical properties remains a challenge for unconventional reservoir characterization. Because natural fractures are highly heterogeneous and of subseismic scale, integrating petrophysical data (i.e., cores and well logs) with seismic data is important for building a reliable natural fracture model. Therefore, I have developed an integrated and stochastic approach for discrete fracture network modeling with field data experimentation. In the method, I first perform a seismic attribute analysis to highlight the discontinuity in the seismic data. Then, I extrapolate the well-log data that include localized but high-confidence information. By using the fracture intensity model including seismic and well logs, I build the final natural fracture model that can be used as a background model for the subsequent geomechanical analysis such as simulation of hydraulic fractures propagation. As a result, our workflow combining multiscale data in a stochastic approach constructs a reliable natural fracture model. I validate the constructed fracture distribution by its good agreement with the well-log data.