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作者 | Siyuan Li a, Tianfu Xu a b, Zubin Chen c, Zhenjiao Jiang a b

标题 | Efficient fracture network characterization in enhanced geothermal reservoirs by the integration of microseismic and borehole logs data

来源 | Geothermics

编辑 | 地热小芯（添加微信号:geothermalAI,可获得相关资料）

→这是地热能在线AI地热小芯编辑的第1篇文章


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全文导读



近期的一项研究提出了一种高效的方法，用于从增强型地热储层中水力压裂诱发的微地震事件中表征裂缝网络。该方法包括使用基于密度的空间聚类算法（DBSCAN）对微地震事件进行去噪，通过蒙特卡洛算法进行随机裂缝定位，通过平面投影估计裂缝大小，并通过肘部分析确定裂缝数量。在这一方法中，可以基于微地震事件的源位置以及通过井下测井检测到的主要地应力和初始裂缝的先验方向，重建裂缝网络。该方法通过一个由两个裂缝和微地震事件组成的合成案例进行了验证，其中微地震事件的定位误差高达储层深度的1.0%。结果表明，该方法能够准确确定裂缝数量，并可靠地估计裂缝倾角和倾斜角度，误差低于10°。估计的裂缝大小的最大误差显著低于微地震事件源位置的噪声。在花岗岩热干岩储层的应用中，从2000多个微地震事件中识别出了两个主要的裂缝群：一个倾角大，走向东北；另一个倾角小，走向西北。在不同先验走向范围的情况下，裂缝几何形态被稳健地解释。此外，所提出的方法计算效率高，允许在分钟级的计算时间内实时生成裂缝。该裂缝网络表征方法建立在去噪微地震事件的空间分布基础上。随着微地震事件定位误差的增加（本研究中给出的误差低于40米），裂缝网络生成的误差也会增加。此外，微地震事件与裂缝网络的水力连接和裂缝内部结构的关系较弱。为了提高裂缝网络表征的质量，以更好地适应精确的流体和热传输模型，有必要通过融合微地震数据和水力测试数据进一步优化裂缝网络，这将在不久的将来进行研究。



02

HIGHLIGHT图片


Fig. 1 . Algorithm flowchart of characterizing the fracture network from microseismic events cloud.

Fig. 2 . (a)  Injection rates  during hydraulic fracturing simulation, (b) two stimulated fractures and microseismic events distribution on fracture planes, (c) and (d) microseismic events with white noises at the scale of 0.5% and 1.0% of burial depth, respectively.

Fig. 3 . Percentage of microseismic events fitted by fractures under varying number of fractures in responses to (a) 0.5% white noises and (b) 1.0% white noises in source locations of microseismic events; (c) and (d) best-fitted fracture networks generated from microseismic events with 0.5% and 1.0% white noises, respectively.

Fig. 4 . Percentage of microseismic events fitted by fractures under varying number of fractures in responses to the prior fracture orientation ranges estimated at (a) ±15° and (b) ±20°; (c) and (d) best-fitted fracture networks generated from microseismic events under varying prior fracture orientation ranges.

Fig. 5 . (a) Original microseismic events induced by hydraulic fracturing, (b) the microseismic events denoised by DBSCAN, (c) elbow analysis of relationship between the number of fractures and percentage of microseismic events fitted by fractures, and (d) the fracture network generated from microseismic events.

Fig. 6 . Fracture network characterization under the prior range of fracture strikes reduced by −6° (b), increased by 6° (d)and 12° (f), respectively, and number of fractures (a, c, e) identified stably by elbow method.

免责声明：本文仅用于学术交流和传播，不构成投资建议

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