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作者 | Xu Liang a b c d, Tianfu Xu a b c, Jingyi Chen a b c, Zhenjiao Jiang a b c

标题 | A deep-learning based model for fracture network characterization constrained by induced micro-seismicity and tracer test data in enhanced geothermal system

来源 | Renewable Energy

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


01

全文导读



近期，科研人员提出了一种新的解释框架，旨在明确刻画通过水力刺激增强型地热系统（EGS）中复杂的裂隙网络。该框架基于诱发的微地震活动数据，结合水力刺激和示踪剂测试监测数据，通过长短期记忆（LSTM）-多目标哈里斯鹰优化（MOHHO）算法反演裂隙参数。优化后的LSTM模型可以作为水力刺激和示踪剂测试的数值模型的替代品，有效预测监测数据，并为裂隙网络反演提供约束。此外，通过扩大训练数据集和采用其他深度学习模型，可以进一步提高预测精度。研究还对比分析了微地震监测（MSM）模型和随机（Stoch）模型，强调了诱发微地震活动的时空特征对于储层特征化的重要性。此外，裂隙网络作为压裂流体流动的优先通道，直接控制了在特定协议条件下的注入井口压力。由于水力裂缝与天然裂缝之间的复杂相互作用，水力刺激裂隙网络的形成受到裂隙分布、大小和水力开口的共同决定。该储层特征化方法构建了一个核心框架，可以根据不同的过程模型进行填充，例如电磁调查和热提取测试。该方法已应用于澳大利亚的Habanero EGS，并通过水力刺激和示踪剂测试的现场观测进行了验证。这种方法对监测数据的要求较低，反演效率高。裂隙网络特征化是EGS项目开发的基础。基于反演的裂隙网络，可以估计生产性能，并进一步讨论和优化井位布局和生产策略。



02

HIGHLIGHT图片


Fig. 1 . Workflow of the fracture network characterization method for hydraulically stimulated EGS reservoirs.

Fig. 2 . Location and satellite view of the Habanero EGS with locations of four EGS wells (a), illustration of the conceptual model with the discretized fractured reservoir, and the vertical distribution of the  maximum horizontal stress  (b).

Fig. 3 . Injection protocol (a), and injection wellhead pressure (b) during the hydraulic stimulation at H04 in 2012.

Fig. 4 . The spatio-temporal distribution of the induced micro-seismicity during the stimulation at H01 in 2003 (a) and at H04 in 2012 (b), and the calculated hydraulic diffusivity (c) [ 33 ].

Fig. 5 .  Tracer  breakthrough curve  during the inter-well tracer test in 2012 [ 24 ].

Fig. 6 . Data sets of the fracture network parameters (a), and the modeling results of WP during the hydraulic stimulation (b), TC (c) and ΔP (d) during the tracer test for the LSTM models training and testing.

Fig. 7 . Comparison of the modeling results and LSTM model predictions of the WH (a) and TC (b) at the minimum, mean, and maximum level of errors, and ΔP (c) in the testing sets.

Fig. 8 . Recorded and selected Pareto optimal solutions of the MSM model parameters (a), and comparison between monitoring data and modeling results of the WP during the hydraulic stimulation (b) and the TC during the tracer test (c).

Fig. 9 . Inversed Stoch model (a) and MSM model (b) after the hydraulic stimulation.

Fig. 10 . Comparison of the solute transport processes in the inversed Stoch model (a) and MSM model (b).

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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