Lithologic Reservoirs ›› 2026, Vol. 38 ›› Issue (3): 132-140.doi: 10.12108/yxyqc.20260311

• PETROLEUM EXPLORATION • Previous Articles     Next Articles

GPU-accelerated source scanning algorithm for real-time 3D microseismic monitoring and its application ​

CAO Libin1(), ZHENG Majia2,3, CHEN Qian1, WU Ya2, CHENG Limin1   

  1. 1 Chengdu Bohe Technology Co., Ltd.， Chengdu 610213, China
    2 Development Department, PetroChina Southwest Oil & Gasfield Company, Chengdu 610051, China
    3 Research Institute of Petroleum Exploration and Development, CNPC, Beijing 100083, China
  • Received:2025-08-12 Revised:2025-09-12 Online:2026-05-01 Published:2026-02-11

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Abstract:

Microseismic monitoring technology faces challenges, such as difficulty in weak signal identification, low real-time processing efficiency, and weak microseismic response to fractures in shale gas fracturing ope-rations. To address these issues, a high-precision microseismic event identification method was proposed based on source scanning algorithm (SSA), incorporating a multi-level collapse grid search approach. Real-time loca-lization was achieved using GPU parallel computing technology. The results show that:（1）Through the wavefield energy scanning and superposition mechanism, dynamic correction and coherent superposition are performed on virtual sources to enhance characteristics of weak P-wave signals. Combined with the multi-level collapsing grid search strategy of “coarse grid global scanning + fine grid local precise searching”, the positioning efficiency and accuracy are improved simultaneously. After introducing the GPU-parallelized narrow band fast marching method (FMM), the traveltime calculation efficiency is increased by about 16 times. （2） Numerical experiments show that under low signal-to-noise ratio conditions, the positioning result of the multi-level collapse grid search is close to the real source, and its accuracy is superior to that of the single coarse grid search. In the application of a drilling platform, event points obtained by this method and the conventional method show a northeastward zonal distribution, the event depth can extend up to 300-400 m, the hydraulic fracture event points are more concentrated around the wellbore, and more weak energy events can be identified. （3） In the monitoring of shale gas fracturing construction in Sichuan, characteristics of hydraulic fractures and induced natural fractures can be distinguished from multiple dimensions. Spatially, the former gathers near the wellbore of the current fracturing section with regular morphology, while the latter is far from the fracturing section with irregular and asymmetric distribution. Temporally, events of the former are concentrated during the fracturing construction period, while the latter has significant aftereffect and continues to occur within 30 minutes after pump shutdown. In terms of waveform, the former has high dominant frequency, clear direct wave and simple structure, while the latter has low dominant frequency, long energy duration and multiple subsequent waves. In terms of relative magnitude, the former is generally low, while the latter is high, corresponding to a larger fracture scale. Combined with the relative magnitude and spatial distribution characteristics of events, distribution models of “natural fracture response zone” and “fracturing main control zone” can be constructed, providing a reliable basis for fracturing effect evaluation and geological interpretation.

Key words: shale gas, GPU acceleration, source scanning algorithm (SSA), multi-level grid search, event identification, microseismic localization, fracture response, fracturing monitoring

CLC Number: 

  • TE02

Fig. 1

Schematic diagram of ground microseismic monitoring system and seismic source spatial subdivision"

Fig. 2

Schematic diagram of collapsed grid search"

Fig. 3

3D ray tracing during travel of fast marching method based on GPU parallel acceleration"

Fig. 4

Forward modeling velocity model"

Fig. 5

Synthetic seismic records based on forward modeling velocity model"

Fig. 6

Imaging results of microseismic based on forward modeling velocity model"

Fig. 7

Observed spatiotemporal domain gathers from microseismic monitoring of a drilling platform in Sichuan Basin"

Fig. 8

Plan view of microseismic localization results of a drilling platform in Sichuan Basin"

Fig. 9

Quality control chart for strong energy event localization of a drilling platform in Sichuan Basin"

Fig. 10

Quality control chart for medium energy event localization of a drilling platform in Sichuan Basin"

Fig. 11

Quality control chart for weak energy event localization of a drilling platform in Sichuan Basin"

Fig. 12

Spatial differences between induced natural fractures and hydraulic fracturing fractures of typical well group of a drilling platform in Sichuan Basin"

Fig. 13

Spatial differences between induced natural fractures and hydraulic fracturing fractures of a typical well of a drilling platform in Sichuan Basin"

Fig 14

Temporal difference between induced natural fractures and hydraulic fracturing fractures of a drilling platform in Sichuan Basin"

Fig.15

Distribution difference between induced natural fractures and hydraulic fracturing fractures of a drilling platform after 30 minutes’ fracturing of Sichuan Basin"

Fig. 16

Response waveform differences between induced natural fractures and hydraulic fracturing fractures of a drilling platform in Sichuan Basin"

Fig. 17

Prediction superposition map of induced natural fractures and 3D fractures of a drilling platform in SichuanBasin (red solid line represents the extension direction of microseismic induced natural fractures)"

[1] MAXWELL S. Microseismic:Growth born from success[J]. The Leading Edge, 2010, 29(3):338-343.
[2] BELYADI H, FATHI E, BELYADI F. Hydraulic fracturing in unconventional reservoirs:Theories,operations,and economic analysis[M]. 2nd ed. Cambridge: Gulf Professional Publishing, 2019.
[3] 罗志锋, 黄静云, 何天舒, 等. 碳酸盐岩储层酸压裂缝高度延伸规律:以川西栖霞组为例[J]. 岩性油气藏, 2020, 32(2):169-176.
LUO Zhifeng, HUANG Jingyun, HE Tianshu, et al. Extending regularity of fracture height by acid fracturing in carbonate re-servoir:A case study of Qixia Formation in western Sichuan[J]. Lithologic Reservoirs, 2020, 32(2):169-176.
[4] 杜金玲, 林鹤, 纪拥军, 等. 地震与微地震融合技术在页岩油压后评估中的应用[J]. 岩性油气藏, 2021, 33(2):127-134.
DU Jinling, LIN He, JI Yongjun, et al. Application of seismic and microseismic data fusion technology in postfracturing evaluation of shale oil reservoir[J]. Lithologic Reservoirs, 2021, 33(2):127-134.
[5] 胡俊杰, 宋毅, 朱炬辉, 等. 基于微地震监测的长宁区块页岩气水平井压裂效果评估[J]. 油气井测试, 2025, 34(1):35-42.
HU Junjie, SONG Yi, ZHU Juhui, et al. Evaluating hydraulic-fracture effectiveness in Changning shale gas block from microseismic monitoring[J]. Well Testing, 2025, 34(1):35-42.
[6] 赵超峰, 贾振甲, 田建涛, 等. 基于井中微地震监测方法的压裂效果评价:以吉林探区Y22井为例[J]. 岩性油气藏, 2020, 32(2):161-168.
ZHAO Chaofeng, JIA Zhenjia, TIAN Jiantao, et al. Fracturing effect evaluation based on borehole microseismic monitoring method:A case study from well Y22 in Jilin exploration area[J]. Lithologic Reservoirs, 2020, 32(2):161-168.
[7] 刘博, 徐刚, 纪拥军, 等. 页岩油水平井体积压裂及微地震监测技术实践[J]. 岩性油气藏, 2020, 32(6):172-180.
LIU Bo, XU Gang, JI Yongjun, et al. Practice of volume fracturing and microseismic monitoring technology in horizontal wells of shale oil[J]. Lithologic Reservoirs, 2020, 32(6):172-180.
[8] 刘晗, 张建中. 微震信号自动检测的STA/LTA算法及其改进分析[J]. 地球物理学进展, 2014, 29(4):1708-1714.
LIU Han, ZHANG Jianzhong. STA/LTA algorithm analysis and improvement of microseismic signal automatic detection[J]. Progress in Geophysics, 2014, 29(4):1708-1714.
[9] MA Ju, WU Shuang, ZHAO Yuan, et al. Cooperative P-wave velocity measurement with full waveform moment tensor inversion in transversely anisotropic media[J]. Sensors, 2022, 22(5):1-12.
[10] ROSS Z E, MEIER M A, HAUKSSON E, et al. Generalized seismic phase detection with deep learning[J]. Bulletin of the Seismological Society of America, 2018, 108(5A):2894-2901.
[11] LIU Lei, SONG Weiqi, ZENG Chao, et al. Microseismic event detection and classification based on convolutional neural network[J]. Journal of Applied Geophysics, 2021, 192:1-10.
[12] HAN Jiajun, VAN DER BAAN M. Microseismic and seismic denoising via ensemble empirical mode decomposition and adaptive thresholding[J]. Geophysics, 2015, 80(6):KS69-KS80.
[13] LI Huijian, WANG Runqiu, CAO Siyuan, et al. Weak signal detection using multiscale morphology in microseismic monitoring[J]. Journal of Applied Geophysics, 2016, 133:39-49.
[14] LIU K, LAW S S, XIA Y, et al. Singular spectrum analysis for enhancing the sensitivity in structural damage detection[J]. Journal of Sound and Vibration, 2014, 333(2):392-417.
[15] ZHANG Xingli, ZHAO Zhenhua, JIA Ruisheng, et al. Identification of microseismic signals based on multiscale singular spectrum entropy[J]. Shock and Vibration, 2020, 2020(4):1-12.
[16] 李文军, 李丽, 陈棋福. 用震源扫描算法(SSA)研究列车源的运动[J]. 地球物理学报, 2008, 51(4):1146-1151.
LI Wenjun, LI Li, CHEN Qifu. Research on the movement of vibration source of train by means of SSA[J]. Chinese Journal of Geophysics, 2008, 51(4):1146-1151.
[17] LI Lei, TAN Jingqiang, SCHWARZ B, et al. Recent advances and challenges of waveform‐based seismic location methods at multiple scales[J]. Reviews of Geophysics, 2020, 58(1):1-47.
[18] HOON K, SHAN Shaoju. The source-scanning algorithm:Mapping the distribution of seismic sources in time and space[J]. Geophysical Journal International, 2004, 157(2):589-594.
[19] ZOU An, LI Jing, GILL C D, et al. RTGPU:Real-time GPU scheduling of hard deadline parallel tasks with fine-grain utilization[J]. IEEE Transactions on Parallel and Distributed Systems, 2023, 34(5):1450-1465.
[20] 刘帅, 季晓慧, 芦俊, 等. CPU与GPU协同并行的多分量地震数据各向异性叠前时间偏移[J]. 石油地球物理勘探, 2019, 54(1):65-72.
LIU Shuai, JI Xiaohui, LU Jun, et al. Multi-component aniso-tropy prestack time migration based on collaborative parallel computing with CPU and GPU[J]. Oil Geophysical Prospec-ting, 2019, 54(1):65-72.
[21] XIE Jiangang, GUO Zichao, LIU Hai, et al. GPU acceleration of time gating based reverse time migration using the pseudospectral time-domain algorithm[J]. Computers and Geosciences, 2018, 117:57-62.
[22] 巴振宁, 赵靖轩, 吴孟桃, 等. 基于CPU-GPU异构并行的复杂场地近断层地震动谱元法模拟[J]. 地震学报, 2022, 44(1):182-193.
BA Zhenning, ZHAO Jingxuan, WU Mengtao, et al. Simulation of near-fault ground motions in complex sites based on CPU-GPU heterogeneous parallelism by spectral element method[J]. Acta Seismologica Sinica, 2022, 44(1):182-193.
[23] XUE Qingfeng, WANG Yibo, ZHAN Yi, et al. An efficient GPU implementation for locating micro-seismic sources using 3D elastic wave time-reversal imaging[J]. Computers and Geosciences, 2015, 82:89-97.
[24] 张猛. 基于GPU并行加速的黏声最小二乘逆时偏移及应用[J]. 岩性油气藏, 2022, 34(1):148-153.
ZHANG Meng. Least-squares reverse time migration in visco-acoustic medium based on GPU parallel acceleration and its app-lication[J]. Lithologic Reservoirs, 2022, 34(1):148-153.
[25] 徐田录, 吴承美, 张金凤, 等. 吉木萨尔凹陷二叠系芦草沟组页岩油储层天然裂缝特征与压裂模拟[J]. 岩性油气藏, 2024, 36(4):35-43.
XU Tianlu, WU Chengmei, ZHANG Jinfeng, et al. Natural fracture characteristics and fracture network simulation in shale reservoirs of Permian Lucaogou Formation in Jimsar Sag[J]. Lithologic Reservoirs, 2024, 36(4):35-43.
[26] GRIGOLI F, CESCA S, AMOROSO O, et al. Automated seismic event location by waveform coherence analysis[J]. Geophysical Journal International, 2014, 196(3):1742-1753.
[27] 赵忠华, 王成, 初海红, 等. 基于等效慢度的快速推进法求解三维地震波走时[J]. 地球物理学报, 2024, 67(10):3904-3914.
ZHAO Zhonghua, WANG Cheng, CHU Haihong, et al. 3D tra-veltime computation using the effective-slowness-based fast marching method[J]. Chinese Journal of Geophysics, 2024, 67(10):3904-3914.
[28] GHARTI H N, OYE V, ROTH M, et al. Automated microearthquake location using envelope stacking and robust global optimization[J]. Geophysics, 2010, 75(4):MA27-MA46.
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