智能录井技术研究进展及发展展望

王志战

doi:10.11911/syztjs.2024099

智能录井技术研究进展及发展展望

王志战^{1, 2,}

1.
中国石化测录井重点实验室，北京 102206
2.
中石化石油工程技术研究院有限公司，北京 102206

基金项目: 中国石化科技攻关项目“岩屑自动化录井及多元信息在线检测技术研究”（编号：P23158）资助。

详细信息

作者简介:
王志战（1969—），男，山东栖霞人，1991年毕业于西北大学岩石矿物学及地球化学专业，2002年获石油大学计算机应用技术专业硕士学位，2006年获西北大学矿产普查与勘探专业博士学位，正高级工程师，长期从事录井基础理论与新技术新方法研究。系本刊编委。E-mail：wangzz.sripe@sinopec.com

中图分类号: P631.81
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出版历程
- 收稿日期: 2024-05-07
- 网络出版日期: 2024-10-08
- 刊出日期: 2024-09-24

Research Progress and Development Prospect of Intelligent Surface Logging Technology

WANG Zhizhan^{1, 2,}

1.
Sinopec Key Laboratory of Well Logging, Beijing, 102206, China
2.
Sinopec Research Institute of Petroleum Engineering Co., Ltd., Beijing, 102206, China

摘要

摘要:
录井具有样品条件及制样工序复杂、采集项目多而离散、人工经验依赖性强且人均产值低等特点，亟需加强智能化转型，但相比于其他石油工程技术，智能录井技术进展缓慢，且局限于应用层面。为此，从智能钻井的进展与成效入手，分析了国内外智能钻井在硬件系统、控制系统、应用系统方面的进展与差距；然后，从地质录井、工程录井、智慧平台3个方面分析了智能录井的主要技术进展，包括“数据+”驱动和视觉驱动的岩性识别、流体识别、井下与地面风险识别及预警等。通过对比智能钻井与智能录井的现状，提出智能录井应强化井下智能录井、智能录井机器人等硬件系统及多场数字孪生、多元采集智能控制、多模态录井大模型、智能解释评价等软件系统的研发。同时强调，既要高度重视，又要理性看待智能录井的发展，要在回顾评价、横向对比的基础上，做好战略定位与研发流程优化，实现进度追赶与作用发挥。这些分析与观点，对推动智能录井实现良性、快速发展具有指导意义。
- 智能录井 /
- 智能钻井 /
- 机器人 /
- 数字孪生 /
- 大模型 /
- 智能控制
Abstract:
Surface logging has the characteristics of complex sample conditions and sample preparation process, numerous and discrete collection items, strong dependence on hands-on experience, and low per capita output. It is urgent to strengthen intelligentialization transformation. However, compared with other petroleum engineering technologies, intelligent surface logging is making slow advancement and facing application limits. Therefore, the progress and gap of hardware systems, control systems, and application systems of intelligent drilling in China and abroad were analyzed from the progress and achievement of intelligent drilling. Then, the main technical progress of intelligent surface logging was analyzed in terms of geological surface logging, engineering surface logging, and intelligent platform, covering “data +” driven and visually driven lithology identification, fluid identification, and downhole and surface risk identification and early alarming. Based on the comparison between intelligent drilling and intelligent surface logging, it was suggested that the research and development of hardware systems such as downhole intelligent surface logging, and intelligent surface logging robots, as well as software systems such as multi-field digital twins, multi-acquisition intelligent control, multimode large model of surface logging, and intelligent interpretation and evaluation should be strengthened. At the same time, it was emphasized that we should attach great importance to and rationally look at the development of intelligent surface logging and determine strategic positioning and process optimization on the basis of retrospective evaluation and horizontal comparison, so as to catch up with the progress and engagement. These analyses and viewpoints have guiding significance in promoting the benign and rapid development of intelligent surface logging.
- Intelligent surface logging /
- intelligent drilling /
- robots /
- digital twin /
- large model /
- intelligent control

HTML全文

各向异性是指地层某种物理参数（如声波速度、电导率、渗透率等）沿不同方向测量的结果不一样^[1-3]。电阻率的各向异性是指水平方向电阻率（R_h）与垂直方向电阻率（R_v）的差异，通常用 ${ \lambda }\text{=} \sqrt{{{R}}_{\text{v}}\text{/}{{R}}_{\text{h}}}$ 来表征其大小^[4]。各向异性是地层的固有属性，成因复杂，矿物组成、孔隙分布、颗粒排列，以及裂缝和层理等，均会使地层表现出电各向异性特征^[5]。

在大斜度井、水平井中，随钻电磁波电阻率的相位差电阻率曲线和幅度比电阻率曲线受地层各向异性影响呈现明显差异，其差异程度与地层各向异性及井眼–地层夹角相关，利用这种差异能进行各向异性系数提取和电阻率反演校正^[6-8]。近年来，学者们进行电磁测井技术模拟，考察各向异性引起仪器测井响应的变化规律，建立了二维、三维快速反演方法^[9]，这些方法在大斜度井、水平井中应用效果显著，而对于井斜角小于30°的井，各向异性虽对相位电阻率、幅度比电阻率有影响，但电阻率曲线的差异不明显，无法提取地层的各向异性信息。基于此，笔者利用微电阻率成像测井的高分辨率和不同方位电阻率的差异性特征来表征地层的各向异性，与实钻井随钻电阻率计算的各向异性系数的一致性较好，并且微电阻率成像表征的各向异性具有更高的分辨率^[10-12]，对薄互层、裂缝性地层和非均质地层引起的各向异性有很好的指示作用。

1. 各向异性地层随钻电阻率测井响应特征

以斯伦贝谢公司的ARC675型随钻电阻率测井仪为例，其采用5发2收（T−T−T−R−R−T−T）非对称分布的天线，采用混合补偿的井眼补偿方法，除具有对称补偿仪器的优点外，还采用了2 MHz和400 kHz 2种频率及5种源距，可以测得径向上20条不同探测深度的电阻率曲线，仪器结构如图1所示。

图 1 ARC675型随钻电阻率测井仪示意

Figure 1. ARC675 instrument for resistivity logging while drilling

下载: 全尺寸图片幻灯片

基于ARC675型随钻电阻率测井仪，利用正演模拟^[13-15]分析各向异性地层随钻电阻率的测井响应特征。模型参数设置：水平电阻率R_h为20 Ω∙m，各向异性系数 $\lambda$ 分别为2和3，在无限厚地层条件下，不考虑钻井液侵入和井眼环境的影响，模拟井眼与地层呈不同夹角时随钻电阻率测井的响应特征，结果如图2所示（图2中，蓝色、绿色、红色、青色、粉色线分别代表406.4，558.8，711.2，863.6和1 016.0 mm源距的相位差、幅度比电阻率）。

图 2 井眼与地层不同夹角下随钻电阻率测井的响应特征

Figure 2. Response characteristics of resistivity logging while drilling with different included angle between formation and borehole

下载: 全尺寸图片幻灯片

对比图2中仪器的响应特征，可得如下规律性认识：1）井眼与地层的夹角大于30°时，相位差电阻率开始大于幅度比电阻率，且相位差电阻率曲线的差异随夹角增大而变大，显现出典型的各向异性特征；2）井眼与地层的夹角不大于30°时，相位差、幅度比电阻率曲线的差异均不明显，不能判断地层是否存在各向异性；3）各向异性系数和井眼与地层的夹角越大，相位差、幅度比电阻率曲线的差异越大。

国内外学者通常利用这种差异进行地层各向异性识别和电阻率反演^[16-18]，以获得地层水平电阻率。但是，当井眼与地层的夹角不大于30°时，地层各向异性虽对相位差电阻率、幅度比电阻率有影响，但影响不大，无法利用曲线的差异特征来表征地层的各向异性信息。

2. 微电阻率成像各向异性表征方法

相对于随钻电阻率测井，微电阻率成像测井具有更高的分辨率，可进行井周测量，对地层层理、倾角、裂缝、破碎特征等具有很好的指示性。

以斯伦贝谢公司FMI电成像工具为例，该工具每个井深点周向上有 $S（S=192）$ 个电阻率测量值，分别记为 ${R}_{\mathrm{t}\mathrm{p}1}$ ， ${R}_{\mathrm{t}\mathrm{p}2}$ ，…， ${R}_{\mathrm{t}\mathrm{p}{S}}$ ；在一定窗长内有M个井深取样点，对应的微电阻率成像有 $M\times S$ 个电阻率测量值。利用微电阻率成像进行各向异性表征时，首先将窗长内 $M\times S$ 个测量值按其测量值代表的物理量（电导率）大小划分为 $N$ 个区间，每个区间用对应的灰度等级值 ${a}_{n}(n=1,\cdots ,N)$ 代替，形成离散型变量，记为 $X$ ，其中：

$X=\left\{{x}_{1},{x}_{2},\cdots ,{x}_{i},\cdots ,{x}_{M\times S}\right\}\quad(i=1,2,\cdots ,M\times S)$

(1)

${x}_{i}$ 必然有且仅有一个灰度区间 ${a}_{n}$ （ $n$ 为 $1～N$ 中的某一个值）与之对应，设离散型变量 $X$ 的分布数列 $P$ 可表示为：

$P\left\{X={x}_{{i}}\right\}{|}_{i\in [1,2,\cdots ,M\times S]}=P\left\{X={a}_{n}\right\}={p}_{n}\quad(n=1,2,\cdots ,N)$

(2)

样本是有限的，结合概率的可列可加性，其分布函数可表示为：

$F\left(x\right)=P\left\{X \leqslant x\right\}=\sum _{{x}_{i} \leqslant x}P\left\{X={x}_{i}\right\}=\sum _{{x}_{i} \leqslant x}{p}_{n}$

(3)

利用差分代替微分，步长为h，通过近似公式计算离散变量 $X$ 取值在 ${x}_{\mathrm{i}}$ 处的概率密度：

${q}_{i}=f\left({x}_{i}\right)=F{'}\left({x}_{i}\right)\approx \frac{F\left({x}_{i}+h\right)-F\left({x}_{i}-h\right)}{2h}{|}_{h\to 0}$

(4)

将电阻率成像图像转换为灰度图像，依据灰度等级值 ${a}_{n}\left(n=1,2,\cdots ,N\right)$ 把窗长内 $M\times S$ 个测量值划分为 $N$ 个区间，累计落在同一区间的测量点，计算各区间测点数占总测点数的比例 ${l}_{n}\left(n=1,2,\cdots ,N\right)$ 。

根据测量点的概率密度函数 ${q}_{n}\left(n=1,2,\cdots ,N\right)$ 、区间测点数占总测点数的比例 ${l}_{\mathrm{n}}\left(n=1,2,\cdots ,N\right)$ ，对导电介质体积进行加权赋值，计算导电介质的等效体积，加权方法为：

$E=-{\sum }_{n=1}^{N}{q}_{n}\mathrm{lg}{q}_{n}$

(5)

$a =\frac{GR-{GR}_{\mathrm{m}\mathrm{i}\mathrm{n}}}{{GR}_{\mathrm{m}\mathrm{a}\mathrm{x}}-{GR}_{\mathrm{m}\mathrm{i}\mathrm{n}}}$

(6)

${L}_{\mathrm{s}\mathrm{h}\mathrm{a}\mathrm{l}\mathrm{e}}={\sum }_{n=1}^{N}{(\delta }_{\mathrm{s}\mathrm{h}\mathrm{a}\mathrm{l}\mathrm{e}} {l}_{n})$

(7)

${V}_{{\rm{shale}}}=\frac{{2}^{3.7 a}-1}{{2}^{3.7}-1} {\left(\frac{{L}_{\mathrm{s}\mathrm{h}\mathrm{a}\mathrm{l}\mathrm{e}}}{{L}_{\mathrm{s}\mathrm{h}\mathrm{a}\mathrm{l}\mathrm{e}}+{L}_{\mathrm{s}\mathrm{a}\mathrm{n}\mathrm{d}}} E\right)}^{2}$

(8)

$\,其中\qquad\qquad\quad {\delta }_{\mathrm{s}\mathrm{h}\mathrm{a}\mathrm{l}\mathrm{e}}=\left\{\begin{array}{l}1\quad{a}_{i}\in {\{\boldsymbol{V}}_{\mathrm{m}}\}\\ 0\quad{a}_{i}\in \left\{{\boldsymbol{V}}_{\mathrm{s}}\right\}\end{array}\right.\qquad$

(9)

式中： ${q}_{n}$ 为图像第n级灰度值的概率密度； $N$ 为图像的总灰度级； $E$ 为所有灰度等级区域的熵增系数； $GR$ 为测量点的自然伽马值，API； ${GR}_{\mathrm{m}\mathrm{a}\mathrm{x}}，{GR}_{\mathrm{m}\mathrm{i}\mathrm{n}}$ 分别为自然伽马的最大值和最小值，API；a为泥质含量指数； ${\delta }_{\mathrm{s}\mathrm{h}\mathrm{a}\mathrm{l}\mathrm{e}}$ 为泥质识别函数； ${\{\boldsymbol{V}}_{\mathrm{s}}\}，{\{\boldsymbol{V}}_{\mathrm{m}}\}$ 分别为砂岩测点集合和泥岩测点集合； ${l}_{n}$ 为测量值各灰度在总灰度划分区域所占比例； ${L}_{\mathrm{s}\mathrm{h}\mathrm{a}\mathrm{l}\mathrm{e}}，{L}_{\mathrm{s}\mathrm{a}\mathrm{n}\mathrm{d}}$ 分别为泥岩、砂岩测量值各灰度在总灰度划分区域所占比例； $M，S$ 为窗长内测点的行数和列数； ${V}_{\mathrm{s}\mathrm{h}\mathrm{a}\mathrm{l}\mathrm{e}}$ 为泥质含量。

根据加权系数计算结果，计算每个等级灰度区域的泥质含量，然后计算水平电阻率和垂直电阻率：

$\frac{1}{{R}_{\mathrm{h}}}=\frac{{V}_{\mathrm{s}\mathrm{a}\mathrm{n}\mathrm{d}}}{{R}_{\mathrm{s}\mathrm{a}\mathrm{n}\mathrm{d}}}+\frac{{V}_{\mathrm{s}\mathrm{h}\mathrm{a}\mathrm{l}\mathrm{e}}}{{R}_{\mathrm{s}\mathrm{h}\mathrm{a}\mathrm{l}\mathrm{e}}}$

(10)

${R}_{\mathrm{v}}={R}_{\mathrm{s}\mathrm{a}\mathrm{n}\mathrm{d}} {V}_{\mathrm{s}\mathrm{a}\mathrm{n}\mathrm{d}}+{R}_{\mathrm{s}\mathrm{h}\mathrm{a}\mathrm{l}\mathrm{e}} {V}_{\mathrm{s}\mathrm{h}\mathrm{a}\mathrm{l}\mathrm{e}}$

(11)

${V}_{\mathrm{s}\mathrm{h}\mathrm{a}\mathrm{l}\mathrm{e}}=f\left[\frac{GR-{GR}_{\mathrm{m}\mathrm{i}\mathrm{n}}}{{GR}_{\mathrm{m}\mathrm{a}\mathrm{x}}-{GR}_{\mathrm{m}\mathrm{i}\mathrm{n}}} {\left(\frac{{L}_{\mathrm{s}\mathrm{h}\mathrm{a}\mathrm{l}\mathrm{e}}}{{L}_{\mathrm{s}\mathrm{h}\mathrm{a}\mathrm{l}\mathrm{e}}+{L}_{\mathrm{s}\mathrm{a}\mathrm{n}\mathrm{d}}}\right)}^{2}\right]$

(12)

不考虑孔隙度时，等效体积关系满足：

${V}_{\mathrm{s}\mathrm{h}\mathrm{a}\mathrm{l}\mathrm{e}} + {V}_{\mathrm{s}\mathrm{a}\mathrm{n}\mathrm{d}} =1$

(13)

考虑孔隙度时，砂泥岩体积关系满足：

${V}_{\mathrm{s}\mathrm{h}\mathrm{a}\mathrm{l}\mathrm{e}} + {V}_{\mathrm{s}\mathrm{a}\mathrm{n}\mathrm{d}} =1- \phi$

(14)

式中： ${R}_{\mathrm{s}\mathrm{h}\mathrm{a}\mathrm{l}\mathrm{e}}，{R}_{\mathrm{s}\mathrm{a}\mathrm{n}\mathrm{d}}$ 分别为泥岩、砂岩电阻率，Ω∙m； ${R}_{\mathrm{h}}，{R}_{\mathrm{v}}$ 分别为等效水平、垂直电阻率，Ω∙m； ${V}_{\mathrm{s}\mathrm{h}\mathrm{a}\mathrm{l}\mathrm{e}}， {V}_{\mathrm{s}\mathrm{a}\mathrm{n}\mathrm{d}}$ 分别为泥岩、砂岩等效体积； $\phi$ 为地层孔隙度。

计算得到 ${R}_{\mathrm{h}}$ 和 ${R}_{\mathrm{v}}$ 后，即可计算出地层的各向异性系数。

3. 应用效果分析

微电阻率成像各向异性表征处理流程为：1）基于微电阻率成像测井进行对比与分层，分层时根据自然伽马曲线或电阻率曲线，将测井值处于同一测量值附近的连续井段划分为一层，以曲线半幅点位置作为分层界面；2）在层内根据成像数据划分图像灰度等级，并在选定窗长内统计同等级灰度测量区域体积，计算其总体积比例，并建立体积模型；3）计算窗长内测量点灰度等级概率密度，将概率密度与灰度值的积分作为窗长内均质性的权系数，与体积模型结合形成各向异性评价的体积模型；4）根据体积模型中不同灰度区域代表的电阻率，利用体积比例和权系数计算出水平电阻率和垂直电阻率；5）计算地层各向异性系数。

目前该方法在东海累计应用超过30井次。采用微电阻率成像计算的地层各向异性信息对随钻电磁波电阻率进行校正处理，为含水饱和度定量计算提供了技术支持。图3为A井微电阻率成像各向异性表征成果图，其中，第1道为井深，第2道为微电阻率静态图像，第3道为微电阻率动态图像，第4道为各向异性表征方法获取的各向异性系数（ $\lambda$ ）。

图 3 A井微电阻率成像各向异性表征

Figure 3. Anisotropy characterization of micro-resistivity image of Well A

下载: 全尺寸图片幻灯片

从图3可以看出，非均质性强区域、裂缝区域、薄互层区域的各向异性系数大，其他均质区域的各向异性系数小，微电阻率成像提取的各向异性系数能清晰地表征地层的非均质性。

为进一步检验微电阻率成像提取各向异性系数的准确性和适用性，选择与大斜度井随钻电阻率提取得到的各向异性系数进行对比和统一处理。各向异性统一评价流程为：在大斜度井中分别进行随钻电阻率、微电阻率成像各向异性系数提取和统计，对比同深度井段各向异性系数变化，建立微电阻率成像各向异性系数与随钻电阻率各向异性系数的相关关系。

B井分别利用随钻电阻率、微电阻率成像提取的各向异性系数如图4所示。图4中，第6道、第7道随钻电磁波电阻率测井曲线差异明显，呈现相位差电阻率大于幅度比电阻率、长源距测量值大于短源距测量值的各向异性响应特征，指示地层存在明显的各向异性。微电阻率成像显示，存在薄互层特征。图4中第2道成像各向异性曲线为微电阻率成像提取的各向异性系数，第5道随钻计算各向异性曲线为随钻电阻率提取的各向异性系数。

图 4 B井各向异性系数对比

Figure 4. Anisotropy coefficient comparison of Well B

下载: 全尺寸图片幻灯片

以随钻电阻率提取的各向异性系数为横轴，以微电阻率成像计算的各向异性系数为纵轴，建立回归方程：

$y=0.885x+0.615$

(15)

基于上述回归方程进行各向异性一致性评价，结果如图5所示。

图 5 一致性条件下的各向异性系数对比

Figure 5. Comparison of anisotropic coefficient under consistency condition

下载: 全尺寸图片幻灯片

由图5可知，随钻电阻率各向异性系数与微电阻率成像各向异性系数具有很好的一致性，微电阻率成像提取的各向异性系数分辨率更高。

从一致性评价结果可知：不同测量工具间受井斜角、仪器探测特性及测量环境等因素的影响，各向异性分辨率存在一定差异；随钻电阻率、微电阻率成像均为电性采集数据，其各向异性系数具有相关性，通过各向异性一致性评价，两者有效互补，能有效提高地层各向异性系数评价的准确性和适用性。

4. 结　论

1）各向异性地层随钻电磁波电阻率正演模拟显示，当井眼与地层的夹角小于30°时，不同探测模式测量的视电阻率重合且大于地层的水平电阻率，各向异性对视电阻率的放大效应依然存在，但是基于电阻率曲线差异信息来计算地层各向异性系数的方法不再适用。

2）基于微电阻率成像测井的高分辨率和不同方位电阻率的差异性特征，采用数理统计方法，通过划分图像灰度等级并建立等效体积模型，根据体积模型中不同灰度区域代表的电阻率，结合泥质含量和加权系数计算地层等效的水平电阻率、垂直电阻率和各向异性系数，解决了井斜角较小井中无法利用曲线差异反演获得各向异性系数的难题。

3）微电阻率成像计算的各向异性系数与随钻电阻率计算的各向异性系数对比结果表明，二者一致性好，且微电阻率成像计算的各向异性系数分辨率更高，能够更好地反映裂缝、孔洞、非均质井段的各向异性特征。

图 1 智能钻井系统组成

Figure 1. Composition of intelligent drilling system

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图 2 智能录井平台架构

Figure 2. Architecture of intelligent logging

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图 3 录井采集技术体系

Figure 3. Logging acquisition technology system

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表 1 录井与钻井智慧平台架构的对比

Table 1 Comparison of intelligent platform architecture between drilling and logging

层次	智能录井^[64]	智能钻完井^[1]	钻井^[28]
用户层	移动端、Pad端、PC端的实时监控、技术支持、协同研究、远程决策		钻井工程师、完井工程师、地质工程师、管理人员、管理员
网络层	局域网、广域网
应用层	数据管理平台（分类存储与查询等）、数据挖掘平台（统计分析方法）、成果输出平台（岩性自动识别、物性评价、油气层解释、工程智能预警等）	机械钻速智能预测与参数优化、井眼轨迹智能优化与闭环调控、钻井风险智能预警与动态调控、固井质量智能评价与优化控制、压裂方案智能设计和优化调控、完井方案智能设计与生产优化及钻完井多过程动态耦合与多目标协同优化	钻头选型、井壁稳定、钻速预测、卡钻预警、钻井参数优化等
装备层		智能钻头、井下测量短节、智能导向工具、智能钻杆、智能滑套、智能钻机
算法层/数据操作层		机理数据融化、数据增强、小样本学习、迁移学习、强化学习、卷积神经网络、小波分析、在线学习、图算法、遗传算法	数据清洗、资源调度、计算工程
数据层	各类录井仪器、传感器采集的数据、图像、音频/视频等信息	物探数据、综合录井数据、测井数据、岩心数据、地质资料数据、随钻数据、文档资料、其他数据	井信息、录井、测井、地质、地震实时级历时数据

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表 2 录井信息处理与解释评价技术体系

Table 2 Technical system of logging information processing, interpretation, and evaluation

录井信息处理	解释	评价及应用
深度校正与数据源深度匹配影响因素校正或消除散失量恢复或原位重构解谱解耦与信息挖掘曲线、图谱、影像特征提取多元、多维、多尺度数据融合平滑、抽稀、插值等处理标准化、归一化处理	岩性、岩相识别古生物鉴定及沉积环境识别成分及结构、构造识别地质层位及地质小层识别流体类型及赋存状态识别物源、油（气）源识别油（气）成因识别含水性及水型识别有效储层识别油气水层解释 VOCs类型识别钻井工况与安全风险识别高压层及其成因识别溢流预警井漏及其原因识别	物性及孔隙结构评价烃源岩特性评价脆性、岩石力学、可压性评价润湿性、水淹层评价含油气丰度及油气性质评价含水性或含水率评价甜点评价或产能预测单井评价或选区评价地层压力随钻评价可钻性、井壁稳定性评价井筒封闭性或盖层评价热储及锂、钾、铀丰度评价钻井地质设计水平井综合地质导向压裂选层

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1. 各向异性地层随钻电阻率测井响应特征
2. 微电阻率成像各向异性表征方法
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