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物探与化探, 2023, 47(4): 1048-1055 doi: 10.11720/wtyht.2023.1139

方法研究信息处理仪器研制

基于微地震连续裂缝网络模型的SRV研究

李秋辰,1, 陈冬2, 许文豪,1, 易善鑫2, 谢兴隆1, 关俊朋2, 崔芳姿1

1.中国地质调查局 水文地质环境地质调查中心,河北 保定 071051

2.江苏省地质调查研究院,江苏 南京 210008

Determining stimulated reservoir volume based on the microseismic continuous fracture network model

LI Qiu-Chen,1, CHEN Dong2, XU Wen-Hao,1, YI Shan-Xin2, XIE Xing-Long1, GUAN Jun-Peng2, CUI Fang-Zi1

1. Hydrogeological and Environmental Geological Survey,China Geological Survey,Baoding 071051,China

2. Nanjing Institute of Geology and Paleontology,Chinese Academy of Sciences,Nanjing 210008,China

通讯作者: 许文豪(1993-),男,硕士学位,工程师。Email:941734230@qq.com

第一作者: 李秋辰(1988-),男,硕士学位,工程师。Email:874033010@qq.com

责任编辑: 叶佩

收稿日期: 2022-06-11   修回日期: 2023-04-7  

基金资助: 江苏省碳达峰碳中和科技创新专项资金(重大科技示范)(BE2022859)

Received: 2022-06-11   Revised: 2023-04-7  

摘要

在干热岩的开发和改造阶段,需要对储层进行水力压裂改造,储层改造体积(stimulated reservoir volume,SRV)是评价压裂效果的主要标准。微地震监测作为水力压裂的技术环节之一,可以对储层改造体积进行有效估算。本文探讨了基于微地震连续裂缝网络模型的SRV计算方法,以指导干热岩的水力压裂工作。首先,基于微震事件时空分布特征与多项震源参数进行连续裂缝网络建模;其次,提取裂缝网格长度,并选取适当的裂缝高度与裂缝宽度以计算储层改造体积;最后,选取某双井型干热岩微地震监测数据对该方法进行实际应用。实例应用结果表明,该方法可有效估算储层改造体积,为干热岩后续开发与改造提供依据。

关键词： 干热岩; 微地震监测; 连续裂缝网络; 储层改造体积

Abstract

Hydraulic fracturing is required during the exploitation and stimulation of hot dry rock (HDR) reservoirs.The stimulated reservoir volume (SRV) is the main criterion for evaluating the hydraulic fracturing performance.As a technical link of hydraulic fracturing,Microseismic monitoring can be used to effectively estimate the SRV.This study explored the calculational method of SRV based on the microseismic continuous fracture network model with the purpose of guiding the hydraulic fracturing of HDRs.First,the continuous fracture network was modeled based on the temporal and spatial distribution and multiple source parameters of microseismic events.Second,the fracture grid length was extracted and the appropriate fracture height and width were selected to calculate the SRV.Last,the method proposed in this study was applied to the dual-well HDR microseismic monitoring data.The application results show that this method can effectively estimate the SRV,thus providing a basis for the subsequent exploitation and stimulation of HDRs.

Keywords： dry hot rock; microseismic monitoring; continuous fracture network; stimulated reservoir volume

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本文引用格式

李秋辰, 陈冬, 许文豪, 易善鑫, 谢兴隆, 关俊朋, 崔芳姿. 基于微地震连续裂缝网络模型的SRV研究[J]. 物探与化探, 2023, 47(4): 1048-1055 doi:10.11720/wtyht.2023.1139

LI Qiu-Chen, CHEN Dong, XU Wen-Hao, YI Shan-Xin, XIE Xing-Long, GUAN Jun-Peng, CUI Fang-Zi. Determining stimulated reservoir volume based on the microseismic continuous fracture network model[J]. Geophysical and Geochemical Exploration, 2023, 47(4): 1048-1055 doi:10.11720/wtyht.2023.1139

0 引言

随着人类生存发展观念的逐渐转变,绿色低碳已经成为中国的能源战略之一。在此时代背景下,干热岩的勘探开发日渐受到重视[1]。微地震监测技术可以对干热岩储层改造过程中引起的声发射事件进行识别解析[2],从而提取岩石力学参数、进行储层应力分析[3]与刻画裂缝的空间展布信息并对其成像[4-5],进而对储层改造体积(stimulated reservoir volume,SRV)进行估算。鉴于上述特点,微地震监测已经成为干热岩开采过程中的关键配套技术[6-7]。

干热岩的储层体积改造主要是通过泵入大量低黏度压裂液,形成由天然裂缝和水力裂缝组成的复杂网状裂缝结构[8-9]。SRV的大小直接影响干热岩产能的高低[10],因此前人对其进行了大量研究。2006年,Mayerhofer等[11]在研究页岩中的微地震事件与裂缝之间的关系时,首次提出了“油藏改造体积”的概念,2010年验证了页岩气增产效果随着储层改造体积变大而愈发明显的观点[7]。Warpinskif等[12]提出一种利用箱状体体积来近似估计SRV的方法,Baidurja等[13]对该方法进行改进,利用凹包体体积来计算储层改造体积。上述两种基于微震事件包裹体模型的SRV计算方法虽然方便快捷,但往往低估了地下裂缝的复杂性,人们逐渐偏向选择基于裂缝网络建模的方法对储层改造体积进行评估[10,14]。Dershowitz等[15]首次引出离散水力裂缝网络(discrete fracture network,DFN)建模的观点,Xu等[16-17]在此基础上加入流体与裂缝及裂缝与裂缝之间的相互作用,从而对SRV进行估算。Meyer等[18-19]通过模拟地下裂缝的扩展情况和支撑剂在裂缝网络中的运移方式,利用DFN模型计算出储层改造体积。Alexandre等[20]通过线状DFN建模,模拟出由射孔点向储层深处生长的裂缝网络。Seifollahi等[21]基于微地震事件产生的裂缝面,给出了面状DFN模型的研究方法。

上述几种DFN模型虽然考虑了裂缝的复杂性,但裂缝参数往往是随机分布的,不符合裂缝的真实情况,且反演效率很低。本文在线状DFN模型的基础上,基于微震事件的时间—空间分布特征进行裂缝网络模型重构,并在此基础上加入微震事件的震源机制解与地应力场分析结果进行约束,形成连续裂缝网络(continuous fracture network,CFN),对SRV进行合理估算。最后将该方法运用到某干热岩双井微震监测中,取得良好的实际应用效果。

1 算法流程

1.1 CFN模型建立流程

在对微震事件进行基本处理(时间—空间定位、矩震级计算、震源机制解及地应力反演)之后,可以获取事件的空间坐标、发震时刻、矩震级、震源机制解与局部地应力场等信息,对于所有给定的已经包含上述信息的微震事件点ex,y,z,t,m,s,d,r,设置事件点集合M。其中,x、y、z为震源的空间坐标,t为发震时刻,m为矩震级,s、d、r分别为事件破裂面的走向、倾角和滑移角。对M进行连续裂缝网络建模由以下步骤组成[10]:

1)按t由小到大的顺序,对M中的事件点进行排列。

2)定义连续裂缝网络N的种子点。当监测到射孔事件时,定义种子点为射孔事件点,否则定义发震时刻最早的事件点为种子点。

3)定义微震事件点ex,y,z,t,m,s,d,r和连续裂缝网络N之间的连接准则d(e,N):微震事件点以最短路径与网络进行连接,事件可以与事件进行连接,也可以与路径进行连接(图1)。种子点的距离定义为0。

图1

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图1   “事件点—网络”连接准则

Fig.1   “Event-network” connection principle


4)对连接准则d(e,N)进行约束,具体包括:时间约束、震源机制约束、最长扩展距离约束与最大间距约束。其中,时间约束为在给定时间范围内进行路径连接;震源机制约束为使用震源机制反演的事件破裂面走向、倾角与滑移角对其约束;最长扩展距离约束为给定一个最长扩展距离,大于该距离后路径不再扩展;最大间距约束为给定一个最大间距(参考矩震级设定),大于该间距后事件不再进行路径连接。

5)根据约束后的连接准则d(e,N),确定事件点集M中的第i个微震事件点ei至连续裂缝网络N的连接点c,生成ei与c之间的路径di。

6)从事件点集M中移除ei,将ei与di加入连续裂缝网络N后,在该时刻重定义N。

7)增量从i至i+1,循环3)~6)步骤,直至事件

点集M中没有事件点。至此,完成连续裂缝网络建模。

1.2 目标函数

设置目标函数一为[10,22]:

f1w=∑i=1mdi,j*2,
j*=acgmind2ei,Fj,
di,j=Axx+Ayy+Azz+MAx2+Ay2+Az2　,ei(Fj)∈Fj;+∞,　　其他。

式中:w表示等待优化的裂缝变量;m表示事件点的数量;i、j表示第i个点和第j条裂缝;di,j表示微地震事件ei到其对应的裂缝椭圆面Fj的垂直投影距离;A为m×3的矩阵;M为事件点集。该目标函数主要通过所有事件点到裂缝面的最短距离之和,来约束裂缝的数量、中心位置、倾向与倾角,从而确定整个裂缝的空间位置。

设置目标函数二为:

f2w=∑j=1nδ+aj×bj1+mj,

式中:n表示裂缝数量;aj、bj分别表示第j条裂缝的长短半轴;δ为正常数;mj表示与第j条裂缝相关联的微地震事件数量,该目标函数通过使建模过程中接受更小的裂缝面积和更多的相关联微地震事件点,来约束裂缝的数量、旋转角与裂缝椭圆面的长短半轴,从而确定裂缝的大小和方位。

设置目标函数三为:

f3w=∑j=1n∑i=1mjφi(j)-μj2,

式中:n表示裂缝数量;mj表示与第j条裂缝相关联的微地震事件数量;φi(j)表示第j条裂缝上的第i个事件点的倾向与倾角;μj是第j条裂缝的倾向与倾角。该目标函数约束了裂缝上事件点与该条裂缝的产状信息的偏差,从而确保生成的裂缝的产状信息与该条裂缝上事件点对应的信息一致。

最终目标函数为:

fw=γ1f1w+γ2f2w+γ3f3w,

式中:γ1、γ2与γ3分别表示相应子目标函数的权重因子。

1.3 SRV计算

建立连续裂缝网络模型之后,可统计裂缝分支指数与裂缝网络长度等参数。裂缝分支指数(branch index, BI)表示裂缝网络的复杂程度,将射孔点延伸出去的裂缝视为主缝,其分支指数为1;直接从主缝分支出去的裂缝,其分支指数为2;后续分支裂缝的分支指数依次增加1。裂缝网络长度表示裂缝网络中所有裂缝的累积长度,即所有单条裂缝长度的加和。其算法流程见图2。

图2

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图2   基于连续裂缝网络模型的SRV算法流程

Fig.2   Flow chart of SRV algorithm based on continuous fracture network model


在本文计算SRV时,认为震源半径为裂缝高度,震源半径可由震源机制解获得;认为裂缝影响半径为裂缝宽度,裂缝影响半径根据岩石模拟实验获得。结合CFN的统计结果,在对计算空间网格化之后,可获得储层改造体积如下所示[23]:

SRV=∫klk×rk×wk,

式中:l为第k个网格的裂缝长度;r为第k个网格的裂缝高度;w为第k个网格的裂缝宽度;k为裂缝的分布范围。

2 实际应用分析

2.1 事件定位

本实例从某干热岩工区水力压裂过程的4 621个双井微震监测事件当中,选取了矩震级Mw≥-2且具有高信噪比的4 180个微震事件,采用本文所述方法对该事件集进行研究。

首先结合工区地质资料与射孔校正速度模型(图3),对事件集进行定位,并给出微震事件点的时间—空间分布(图4),图4中带色圆球表示微震事件的时空演化过程。空间分布显示:微震事件主要分布于水平x向-500~350 m、水平y向-400~500 m、地下z向-4 810~-4 650 m之间。事件形态为近椭圆形,主轴为近NW向,短轴为近SE向。

图3

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图3   射孔校正速度模型

Fig.3   Model of perforation correction velocity


图4

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图4   微震事件时间—空间分布

a—xy向平面;b—xz向剖面

Fig.4   Spatio-temporal distribution of microseismic events

a—xy plan;b—xz section


定位结果的精度对连续裂缝网络建模的准确性有很大影响,该压裂段使用双井监测,相对于单井监测,定位目标函数更加收敛,且能有效解决多解性问题。选取某一微震事件,其定位目标函数等值线如图5所示,图5a与图5b分别表示单井定位与双井定位目标函数等值线。

图5

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图5   定位目标函数等值线

a—单井定位目标函数等值线;b—双井定位目标函数等值线

Fig.5   Contour map of positioning objective function

a—contour map of positioning objective function of single well;b—contour map of positioning objective function of double well


2.2 震源机制解、矩震级与地应力分析

为获取事件的更多震源参数信息,对实例中的全部事件的震源机制、矩震级与地应力场进行反演(图6)。反演结果显示:基于震源机制的裂缝方位为近NW向,裂缝倾角以高角度为主;垂向应力为该区段最大主应力,最大水平主应力为近NW向,最小水平主应力为近SE向;矩震级集中分布在0级以下。

图6

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图6   微震事件震源参数反演结果

a—震源机制解玫瑰图;b—应力主轴置信区间图;c—矩震级统计直方图

Fig.6   Inversion results of source parameters of microseismic events

a—focal mechanism solution rose diagram;b—confidence of principal stress axis;c—statistical histogram of moment magnitude


2.3 连续裂缝网络(CFN)建模

在获取事件集的空间坐标、发震时刻、矩震级、震源机制解及地应力分析等信息后,采用本文所述方法,以射孔事件为种子点、最大扩展间距为700 m、最长间距为75 m,同时加以震源参数约束进行该压裂段的连续裂缝网络建模,最终给出连续裂缝网络模型的时间—空间分布(图7)。模型显示:裂缝水平方向上呈近椭圆形分布,长轴为近NW向,短轴为近SE向;垂直方向上集中分布在-4 800~-4 650 m之间;形成时间以射孔点为原点、呈均匀向远端扩散的规律。上述特征与事件时间—空间特征、震源机制解与地应力分析的结果吻合。裂缝网络长度统计结果为336.6×103 m,裂缝分支指数为5。

图7

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图7   连续裂缝网络(CFN)模型

Fig.7   Continuous fracture network(CFN) model


2.4 储层改造体积(SRV)计算

在该压裂段CFN模型的基础上,依据震源机制解中的震源半径参数,裂缝高度取值为5 m;参考相关岩石模拟实验的经验结果,裂缝影响半径取值为3 m。利用式(1)对该压裂段的储层改造体积进行计算,并给出储层改造体积示意(图8)。经计算,储层改造体积为504.9×104 m3,证明该压裂段水力压裂效果较好,可部署下一阶段工作。

图8

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图8   储层改造体积(SRV)示意

Fig.8   Sketch map of stimulated reservoir volume (SRV)


3 结论

本文探讨了基于连续网络裂缝模型计算SRV的方法:利用微震事件的时间—空间参数进行线状DFN模型重建,与此同时加入微震事件的多项震源参数进行约束,形成连续裂缝网络模型。参考模型统计结果设定裂缝网格长度,参考震源半径设定缝高,参考裂缝影响半径设定缝宽,对SRV进行合理估算,进而对后续井位的布设、水力压裂及储层改造的效果评价提供依据。

利用本文所述方法对某双井型干热岩微震事件进行实例分析,结果表明:①基于微震事件的时空分布,依据“事件点—网络”的连接准则与事件属性参数建立的连续裂缝网络,不仅可以对水力裂缝网络进行解释,在本文中同时也是储层改造体积计算的基础。②基于连续裂缝网络模型,根据震源半径与岩石模拟实验结果设置储层改造体积建模的关键参数,进而计算出该压裂段的SRV值。结合裂缝指数,可以对该压裂段水力压裂效果进行评估,指导干热岩开发的下一步工作。

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Back projection stacking of P and S-waves to determine location and focal mechanism of microseismic events recorded by a surface array

[J]. Geophysical Prospecting, 2016, 64(6):1428-1440.

DOI:10.1111/gpr.2016.64.issue-6      URL     [本文引用: 1]

Rutledge J T, Phillips W S.

Hydraulic stimulation of natural fractures as revealed by induced microearthquakes,Carthage Cotton Valley gas field,east Texas hydraulic stimulation of natural fractures

[J]. Geophysics, 2003, 68(2):441-452.

DOI:10.1190/1.1567214      URL     [本文引用: 1]

We produced a high‐resolution microseismic image of a hydraulic fracture stimulation in the Carthage Cotton Valley gas field of east Texas. We improved the precision of microseismic event locations four‐fold over initial locations by manually repicking the traveltimes in a spatial sequence, allowing us to visually correlate waveforms of adjacent sources. The new locations show vertical containment within individual, targeted sands, suggesting little or no hydraulic communication between the discrete perforation intervals simultaneously treated within an 80‐m section. Treatment (i.e., fracture‐zone) lengths inferred from event locations are about 200 m greater at the shallow perforation intervals than at the deeper intervals. The highest quality locations indicate fracture‐zone widths as narrow as 6 m. Similarity of adjacent‐source waveforms, along with systematic changes of phase amplitude ratios and polarities, indicate fairly uniform source mechanisms (fracture plane orientation and sense of slip) over the treatment length. Composite focal mechanisms indicate both left‐ and right‐lateral strike‐slip faulting along near‐vertical fractures that strike subparallel to maximum horizontal stress. The focal mechanisms and event locations are consistent with activation of the reservoir's prevalent natural fractures, fractures that are isolated within individual sands and trend subparallel to the expected hydraulic fracture orientation (maximum horizontal stress direction). Shear activation of these fractures indicates a stronger correlation of induced seismicity with low‐impedance flow paths than is normally found or assumed during injection stimulation.

李大军, 杨晓, 王小兰, 等.

四川盆地W地区龙马溪组页岩气压裂效果评估和产能预测研究

[J]. 石油物探, 2017, 56(5):735-745.

DOI:10.3969/j.issn.1000-1441.2017.05.014      [本文引用: 1]

应用水平井H1,H2和H3的微地震、压裂及地震数据综合分析了四川盆地W地区下志留统龙马溪组页岩储层特征。由微地震监测数据分析可知,H1井的压裂主要以激活先期天然裂缝为主,H2井的压裂以形成人工压裂裂缝网络为主,H3井的压裂则以激活先期天然裂缝和人工压裂裂缝网络共同出现为主。分析了有无天然裂缝以及天然裂缝与井筒不同产状关系情况下不同的压裂效果,进而评价了3口水平井的压裂效果,H2井压裂效果最好,H3井次之,H1井较差。最后,利用神经网络技术建立了页岩四大主控因素(总含气量、孔隙度、脆性指数和裂缝密度)地质模型,获得页岩产能数据并将其与具有生产测井数据的H1水平井进行了对比,结合微地震压裂数据对H2井、H3井产能进行了预测,预测结果为H2井产能是H3井的2倍,实际生产测试为2.3倍,吻合性好,页岩产能剖面优化了水平井部署、井轨迹设计、压裂方案参数以及更好的完井设计。

Li D J, Yang X, Wang X L, et al.

Estimating the fracturing effect and production capacity of the Longmaxi formation of the Lower Silurian in area W,Sichuan Basin

[J]. Geophysical Prospecting for Petroleum, 2017, 56(5):735-745.

DOI:10.3969/j.issn.1000-1441.2017.05.014      [本文引用: 1]

The shale gas reservoir characterization in area W of the Sichuan Basin is analyzed using seismic,fracturing,and micro-seismic data from three horizontal wells.From the analysis of micro-seismic monitoring data,we know that after fracturing,pre-existing natural faults were reactivated in H1,an artificial fracture network was formed in H2,and both pre-existing faults and artificial fractures were reactivated in H3.The fracturing effect is mainly affected by natural fractures and the relationship between natural fractures and wellbore occurrence.The analysis of fracturing results for the three wells indicates that H2 had the best fracturing effect,H3 had a medium effect,and H1 had the worse effect.Finally,geological models based on four key driver factors,total gas content,porosity,brittleness index and fracture density,are built using a neural network to obtain the production capacity of shale reservoirs.The trend of the obtained production capacity data is coincident with H1s productive logging data.By integrating that result with the microseismic fracturing monitoring data,the predicted production capacity of the shale reservoir in H2 could be twice that of H3;the actual result is 2.3 times that of H3.The resulting production capacity profiles of the shale reservoir can be used to optimize well deployment,horizontal well track design,hydraulic fracturing geometry layout,and improve well completion programs.

Mayerhofer M J, Lolon E P, Warpinski N R, et al.

What is stimulated reservoir volume?

[J]. Spe Production & Operations, 2010, 25(1):89-98.

[本文引用: 2]

柳占立, 王涛, 高岳, 等.

页岩水力压裂的关键力学问题

[J]. 固体力学学报, 2016, 61(1):34-49.

[本文引用: 1]

Liu Z L, Wang T, Gao Y, et al.

Key mechanics problems of hydraulic fracturing of shale

[J]. Chinese Jourmnal of Solid Mechanics, 2016, 61(1):34-49.

[本文引用: 1]

陈旭日, 杨康, 张公社.

基于页岩储层的离散裂缝网络建模技术

[J]. 能源与环保, 2017, 39(10):172-175.

[本文引用: 1]

Chen X R, Yang K, Zhang G S.

Discrete fracture network modeling technology based on shale reservoirs

[J]. China Energy and Environmental Protection, 2017, 39(10):172-175.

[本文引用: 1]

许德友. 基于微地震DFN模型的SRV研究[D]. 北京: 中国石油大学, 2019.

[本文引用: 4]

Xu D Y. Research on SRV based on microseismic DFN model[D]. Beijing: China University of Petroleum, 2019.

[本文引用: 4]

Mayerhofer M J, Lolon E P, Youngblood J E, et al.

Integration of microseismic fracture mapping results with numerical fracture network production modeling in the Barnett Shale

[C]// San Antonio:Paper SPE 102103 presented at the SPE Annual Technical Conference and Exhibition, 2006.

[本文引用: 1]

Warpinski N R, Mayerhofer M J, Vincent M C, et al.

Stimulating conventional reservoirs maximizing network growth while optimizing fracture conductivity

[C]// Keystone:Paper SPE 114173 presented at the SPE Unconventional Reservoirs Conference, 2008.

[本文引用: 1]

Baidurja R, Avi L, Jianfu M, et al.

Unconventional micro-seismicity based enhanced 3D SRV estimator using advanced parameter-free concave methodology

[C]// SEG Technical Program Expand Abstracts, 2014:2304-2308.

[本文引用: 1]

赵争光, 李磊, 张洪亮.

基于微地震事件属性的水力裂缝网络建模方法

[C]// 中国地球科学联合学术年会, 2020:317-319.

[本文引用: 1]

Zhao Z G, Li L, Zhang H L.

Modeling method of hydraulic fracture network based on microseismic event attributes

[C]// China Earth Science Joint Academic Annual Meeting, 2020:317-319.

[本文引用: 1]

Dershowitz W S, Fidelibus C.

Derivation of equivalent pipe network analogues for three-dimensional discrete fracture networks by the boundary element method

[J]. Water Resources Research, 1999, 35(9):2685-2691.

DOI:10.1029/1999WR900118      URL     [本文引用: 1]

Discrete fracture network (DFN) models generally require solution of flow and transport equations in three‐dimensional networks of either disc, polygonal, or pipe elements. Pipe network elements have significant advantages in computation for both flow and transport. However, there is a need to develop an efficient procedure for derivation of the properties of these pipes to ensure that they are hydraulically equivalent to the DFN network of polygonal elements. In this study a boundary element procedure for derivation of pipe properties is developed and demonstrated. The results show that the hydraulic behavior of pipe networks can be equivalent to that of polygonal‐element DFN models.

Xu W X, Calvez J L, Thiercelin M.

Characterization of hydraulically-induced fracture network using treatment and microseismic data in a tight-gas sand formation:A geomechanical approach

[C]// SanAntonio:Paper SPE 125237 Presented at the SPE Tight Gas Completions Conference, 2009.

[本文引用: 1]

Xu W Y, Thiercelin M, Ganguly U, et al.

Wiremesh:A novel shale fracturing simulator

[C]// Bejjing:Paper 140514 presented at the CPS/SPE International Oil&Gas Conference and Exhibition, 2010.

[本文引用: 1]

Meyer B R, Bazan L W.

A discrete fracture network model for hydraulically induced fractures:Theory parametric and case studies

[C]// Woodlands:Paper 140514 Presented at the SPE Hydraulic Fracturing Technology Conference and Exhibition, 2011.

[本文引用: 1]

Meyer B R, Bazan L W, Jacot R H, et al.

Optimization of multiple transverse hydraulic fractures in horizontal welbores

[C]// Pittsburgh:Paper 131732 Presented at the SPE Unconventional Gas Conference, 2010.

[本文引用: 1]

Alexandre H, Jean C D, Gringarten E, et al.

Connecting the dots:microseismic derived connectivity for estimating volumes in low-permeability reservoirs

[C]// San Antonio:Unconventional Resources Technology Conference, 2015.

[本文引用: 1]

Seifollahi S, Dowd P A, Xu C, et al.

A spatial clustering approach for Stochastic fracture network modelling

[J]. Rock Mechanics and Rock Engineering, 2014, 47(4):1225-1235.

DOI:10.1007/s00603-013-0456-x      URL     [本文引用: 1]

余毓敏. 基于震源机制解的DFN模型优化[D]. 北京: 中国石油大学, 2020.

[本文引用: 1]

Yu Y M. Optimization of DFN model based on focal mechanism[D]. Beijing: China University of Petroleum, 2020.

[本文引用: 1]

邵媛媛. 基于水力压裂模拟及微地震监测信息的SRV研究[D]. 成都: 西南石油大学, 2020.

[本文引用: 1]

Shao Y Y. SRV research based on fracking simulation and microseismic monitoring information[D]. Chengdu: Southwest Petroleum University, 2020.

[本文引用: 1]

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