Fracture networks in underground reservoirs are important pathways for fluid flow and can therefore be a deciding factor in the development of such reservoirs for geothermal energy, oil and gas production or underground storage. Yet, they are difficult to characterize since they usually cannot be directly accessed. We propose a new method to compute the likelihood of having a fracture at a given location from induced seismic events and their source parameters. The result takes the form of a so-called pseudo-probabilistic fracture network (PPFN). In addition to the hypocentres of the seismic events used to image the fracture network, their magnitudes and focal mechanisms are also taken into account, thus keeping a closer link with the geophysical properties of the rupture and therefore the geology of the reservoir. The basic principle of the PPFN is to estimate the connectivity between any spatial position in the cloud and the seismic events. This is done by applying weighting functions depending on the distance between a seismic event and any location, the minimum size of the rupture plane derived from the event magnitude, and the orientation of the rupture plane provided by the focal mechanism. The PPFN is first tested on a set of synthetic data sets to validate the approach. Then, it is applied to the seismic cloud induced by the deep hydraulic stimulation of the well GPK2 of the enhanced geothermal site of Soultz-sous-Forêts (France). The application on the synthetic data sets shows that the PPFN is able to reproduce fault planes placed in a cloud of randomly distributed events but is sensitive to the free parameters that define the shape of the weighting functions. When these parameters are chosen in accordance with the scale of investigation, that is, the typical size of the structures of interest, the PPFN is able to determine the position, size and orientation of the structure quite precisely. The application of the PPFN to the GPK2 seismic cloud reveals a large prominent fault in the deep-northern part of the seismic cloud, supporting conclusions from previous work, and a minor structure in the southern upper part, which could also be a branch of the main fault.

中文翻译：

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源参数驱动的地震云裂缝网络的伪概率识别

地下储层中的裂缝网络是流体流动的重要途径，因此可以成为开发此类储层用于地热能，石油和天然气生产或地下储藏的决定性因素。但是，由于通常无法直接访问它们，因此很难对其进行表征。我们提出了一种新方法，可以根据诱发的地震事件及其震源参数来计算在给定位置发生裂缝的可能性。结果采用所谓的伪概率断裂网络（PPFN）的形式。除了用于记录裂缝网络的地震事件的震源外，还考虑了震级的大小和震源机制，因此与破裂的地球物理性质以及储层的地质状况保持着密切联系。PPFN的基本原理是估计云中任何空间位置与地震事件之间的连通性。这是通过根据地震事件与任何位置之间的距离，从事件大小得出的破裂平面的最小大小以及由震源机构提供的破裂平面的方向应用加权函数来完成的。首先在一组综合数据集上测试PPFN，以验证该方法。然后，将其应用于由Soultz-sous-Forêts（法国）增强的地热站点的GPK2井的深水力刺激引起的地震云。综合数据集上的应用表明，PPFN能够重现放置在随机分布的事件云中的断层平面，但对定义加权函数形状的自由参数敏感。当根据研究规模（即感兴趣的结构的典型尺寸）选择这些参数时，PPFN能够非常精确地确定结构的位置，尺寸和方向。PPFN在GPK2地震云中的应用表明，在地震云的深北部有一个较大的突出断层，支持了先前的研究结论，而在南部的南部有一个较小的构造，也可能是该构造的一个分支。主要故障。根据所关注结构的典型尺寸，PPFN能够非常精确地确定结构的位置，尺寸和方向。PPFN在GPK2地震云中的应用表明，在地震云的深北部有一个较大的突出断层，支持了先前的研究结论，而在南部的南部有一个较小的构造，也可能是该构造的一个分支。主要故障。根据所关注结构的典型尺寸，PPFN能够非常精确地确定结构的位置，尺寸和方向。PPFN在GPK2地震云中的应用表明，在地震云的深北部有一个较大的突出断层，支持了先前的研究结论，而在南部的南部有一个较小的构造，也可能是该构造的一个分支。主要故障。