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物探与化探  2022, Vol. 46 Issue (1): 87-95    DOI: 10.11720/wtyht.2022.2371
  方法研究·信息处理·仪器研制 本期目录 | 过刊浏览 | 高级检索 |
多属性融合定量储层预测方法研究与应用——以廊固凹陷杨税务潜山为例
王成泉1(), 王孟华1, 周佳宜2, 王盛亮3, 杨洲鹏1, 刘慧1, 张红文1
1.华北油田勘探开发研究院,河北 任丘 062552
2.华北油田苏里格勘探开发分公司,河北 任丘 062552
3.华北油田 巴彦勘探开发分公司,河北 任丘 062552
Application of multi-attribute fusion in quantitative prediction of reservoirs: A case study of Yangshuiwu buried hill in Langgu sag
WANG Cheng-Quan1(), WANG Meng-Hua1, ZHOU Jia-Yi2, WANG Sheng-Liang3, YANG Zhou-Peng1, LIU Hui1, ZHANG Hong-Wen1
1. Exploration and Development Research Institute,Huabei Oilfield Company,PetroChina,Renqiu 062552,China
2. Sulige Exploration and Development Branch of Huabei Oilfield,Renqiu 062552,China
3. Bayan Exploration and Development Branch,Huabei Oilfield Company,PetroChina,Renqiu 062552,China
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摘要 

杨税务潜山位于廊固凹陷北部,裂缝孔隙型储层发育,地质综合研究表明该区处于油气运聚的有利方向,但由于其储层埋藏深,地震资料成像精度低,常规属性预测有效储层难度大,多解性强。本文在充分分析已钻井地球物理响应特征的基础上,优选了对有效储层响应敏感的3个属性——平均振幅、方差属性、弧长属性,并计算有效储层厚度与优选属性之间的相关系数,依据相关系数大小确定属性融合权重,最终得到反映有效储层厚度的融合属性,该融合属性有效降低了单属性预测的多解性,同时实现了有效储层的定量预测。实践证明,多属性融合技术有效实用,并在杨税务潜山地区取得了良好的应用效果。

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王成泉
王孟华
周佳宜
王盛亮
杨洲鹏
刘慧
张红文
关键词 杨税务潜山定量储层预测多属性融合    
Abstract

The Yangshuiwu buried hill is located in the northern part of the Langgu sag,where fractured porous reservoirs are well developed.As indicated by comprehensive geological studies,it is in the direction favorable for hydrocarbon migration and accumulation.However,owing to the deep reservoirs and low imaging accuracy of seismic data,it is difficult to predict effective reservoirs using conventional attributes and the obtained prediction results feature strong multiplicity of solution.Based on full analyses of the geophysical response characteristics of existing drilled wells,this study selects three optimal attributes sensitive to the response of effective reservoirs,namely mean amplitude,variance,and arc length,and calculates the correlation coefficient between the thickness of effective reservoirs and each of the optimal attributes.Then it determines the fusion weight of each attribute according to corresponding correlation coefficient,and finally obtains the fused attribute than can reflect the thickness of effective reservoirs.The fused attribute can be used to effectively reduce the multiplicity of solution compared with single attribute prediction and quantitatively predict effective reservoirs.Practice has proved that the multi-attribute fusion technology is effective and practical and has achieved accurate application results in the Yangshuiwu buried hill.

Key wordsYangshuiwu buried hill    quantitative reservoir prediction    multi-attribute fusion
收稿日期: 2020-10-14      出版日期: 2022-02-25
:  P316.4  
基金资助:中国石油天然气股份有限公司科技重大专项项目“华北油田持续有效稳产勘探开发关键技术研究与应用”(2017E-15)
作者简介: 王成泉(1987-),男,山东济南人,硕士研究生,研究方向为地震储层解释及预测等。Email: wty_wcq@petrochina.com.cn
引用本文:   
王成泉, 王孟华, 周佳宜, 王盛亮, 杨洲鹏, 刘慧, 张红文. 多属性融合定量储层预测方法研究与应用——以廊固凹陷杨税务潜山为例[J]. 物探与化探, 2022, 46(1): 87-95.
WANG Cheng-Quan, WANG Meng-Hua, ZHOU Jia-Yi, WANG Sheng-Liang, YANG Zhou-Peng, LIU Hui, ZHANG Hong-Wen. Application of multi-attribute fusion in quantitative prediction of reservoirs: A case study of Yangshuiwu buried hill in Langgu sag. Geophysical and Geochemical Exploration, 2022, 46(1): 87-95.
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https://www.wutanyuhuatan.com/CN/10.11720/wtyht.2022.2371      或      https://www.wutanyuhuatan.com/CN/Y2022/V46/I1/87
Fig.1  白云质含量统计
Fig.2  潜山碳酸盐岩储层类型
储层
级别
类型 常规测井响应 成像测井响应
总孔隙
度/%
电阻率/
(Ω·m)
深浅侧向电阻率
差异/(Ω·m)
裂缝孔
隙度/%
成像特征 孔隙度谱特征
一类
储层
裂缝—孔隙型 ≥3 <700 0.2~0.4 >0.002 裂缝、溶孔共生,较发育 孔隙度谱分布宽,呈中孔径分布
裂缝型 2~3 <1000 >0.4 裂缝规模大、连通性好;成组系发育、网状缝发育 谱型分布窄,呈单峰,呈中小孔径分布
二类
储层
裂缝—孔隙型 ≥3 <1500 0.2~0.4 >0.001 裂缝较发育 谱型展布宽
裂缝型 2~3 <2000 >0.4 裂缝较发育 谱型展布窄,以中小孔径为主
孔隙型 ≥4 <1000 <0.2 裂缝不发育 谱型展布宽,多以大孔径分布
三类
储层
裂缝—孔隙型 ≤3 1500~3000 <0.2 <0.001 裂缝欠发育 谱型展布窄,以中小孔径为主
裂缝型 2~3 2000~4000 0.2~0.3 裂缝欠发育
Table 1  测井储层评价分类
Fig.3  1X、2X、3X、4X、5X、W1井震标定结果
Fig.4  各储层段优选属性预测平面
地层 属性类别 1X井 2X井 3X井 4X井 5X井 W1 实钻厚度与属性拟合公式
峰峰组 平均振幅 属性值 9826 13665 5636 9773 13562 10593 y=45.7ln x-389.3
预测厚度/m 30.8 45.9 5.4 30.6 45.5 34.2
实钻厚度/m 16.2 64.4 12.8 27.8 40.0 31.2
绝对误差/m 14.6 18.5 7.4 2.8 5.5 3.0
方差属性 属性值 2923 2109 2004 3409 3813 3098 y=-5.6ln x+76.2
预测厚度/m 31.8 33.6 33.9 30.9 30.3 31.5
实钻厚度/m 16.2 64.4 12.8 27.8 40.0 31.2
绝对误差/m 15.6 30.8 21.1 3.1 9.7 0.3
弧长属性 属性值 4373 3409 3023 4696 5308 3809 y=63.5ln x-496.7
预测厚度/m 35.6 19.8 12.2 40.2 47.9 26.9
实钻厚度/m 16.2 64.4 12.8 27.8 40.0 31.2
绝对误差/m 19.4 44.6 0.6 12.4 7.9 4.3
上马家沟组 平均振幅 属性值 8992 209 1809 4768 5698 3676 y=14.6ln x-91.9
预测厚度/m 41.0 13.9 17.6 31.8 34.4 28.0
实钻厚度/m 66.6 28.1 15.4 37.2 31.8 22.2
绝对误差/m 25.6 14.2 2.2 5.4 2.6 5.8
方差属性 属性值 4637 2108 2809 3365 4598 2323 y=31.5ln x-227.5
预测厚度/m 38.4 13.6 22.6 28.3 38.2 16.6
实钻厚度/m 66.6 28.1 15.4 37.2 31.8 22.2
绝对误差/m 28.2 14.5 7.2 8.9 6.4 5.6
弧长属性 属性值 5325 3356 2586 4508 5089 2292 y=25ln x-177.1
预测厚度/m 37.4 25.9 19.3 33.2 36.3 16.3
实钻厚度/m 66.6 28.1 15.4 37.2 31.8 22.2
绝对误差/m 29.2 2.2 3.9 4.0 4.5 5.9
下马家沟组 平均振幅 属性值 7870 5567 2096 5765 6908 670 y=29.2ln x-215.7
预测厚度/m 46.2 36.1 7.6 37.2 42.4 25.7
实钻厚度/m 42.6 40.8 21.8 82.4 24.2 6.4
绝对误差/m 3.6 4.7 14.2 45.2 18.2 19.3
方差属性 属性值 3809 2809 1679 3565 3609 1345 y=19.6ln x-126.7
预测厚度/m 34.9 28.9 18.8 33.6 33.8 14.5
实钻厚度/m 42.6 40.8 21.8 82.4 24.2 6.4
绝对误差/m 7.7 11.9 3.0 48.8 9.6 8.1
弧长属性 属性值 4871 3868 2508 5282 5071 1813 y=20.1ln x-135.6
预测厚度/m 35.1 30.4 21.7 36.7 35.9 15.2
实钻厚度/m 42.6 40.8 21.8 82.4 24.2 6.4
绝对误差/m 7.5 10.4 0.1 45.7 11.7 8.8
亮甲山组 平均振幅 属性值 8034 7785 13926 2567 11735 9817 y=17.4ln x-122.1
预测厚度/m 34.4 33.8 43.9 14.5 40.9 37.8
实钻厚度/m 12.8 35.3 8.4 36.6 19.0
绝对误差/m 21.0 8.6 6.1 4.3 18.8
方差属性 属性值 3546 2071 4673 1893 5634 3865 y=21.8ln x-151.8
预测厚度/m 26.4 14.7 32.4 12.7 36.5 28.3
实钻厚度/m 12.8 35.3 8.4 36.6 19.0
绝对误差/m 1.9 2.9 4.3 0.1 9.3
弧长属性 属性值 3523 3630 3960 3675 4930 2031 y=25.5ln x-181.9
预测厚度/m 26.4 27.1 29.3 27.4 34.9 12.3
实钻厚度/m 12.8 35.3 8.4 36.6 19.0
绝对误差/m 14.3 6.0 19.0 1.7 6.7
Table 2  优选属性与有效储层厚度统计关系
Fig.5  属性与有效厚度关系
峰峰组 上马家沟组 下马家沟组 亮甲山组
振幅属性 0.731 0.714 0.727 0.725
方差属性 0.632 0.647 0.638 0.643
弧长属性 0.585 0.592 0.588 0.591
Table 3  属性与有效储层厚度相关系数
方法 峰峰组 上马家沟组 下马家沟组 亮甲山组
平均振幅/% 37.5 36.5 37.2 37
方差统计/% 32.4 33 32.6 32.8
弧长属性/% 30.1 29.5 30.2 30.2
Table 4  优选属性融合权重计算
地层 井名 1X井 2X井 3X井 4X井 5X井 W1 实钻厚度与融合
属性拟合公式
峰峰组 融合属性值 4710.5 11802.0 4123.0 5882.0 7123.5 6022.6 y=50.5ln x-409.6
预测厚度/m 17.9 64.3 11.1 29.1 38.8 30.3
实钻厚度/m 16.2 64.4 12.8 27.8 40.0 31.2
绝对误差/m 1.7 0.1 1.7 1.3 1.2 0.9
上马家沟组 融合属性值 9583.2 3352.6 2254.2 4586.5 3998.2 2783.5 y=35.28ln x-258.4
预测厚度/m 65.0 28.0 14.0 39.0 34.2 21.4
实钻厚度/m 66.6 28.1 15.4 37.2 31.8 22.2
绝对误差/m 1.6 0.1 1.4 1.8 2.4 0.8
下马家沟组 融合属性值 3040.5 2654.0 1683.5 9601.0 1779.5 995.3 y=32.51ln x-217.2
预测厚度/m 43.5 39.1 24.3 80.9 26.1 7.2
实钻厚度/m 42.6 40.8 21.8 82.4 24.2 6.4
绝对误差/m 0.9 1.7 2.5 1.5 1.9 0.8
亮甲山组 融合属性值 5199.6 4056.3 7085.3 3656.3 7678.8 4863.5 y=39.2ln x-312.8
预测厚度/m 22.6 12.9 34.7 8.8 37.9 20.0
实钻厚度/m 12.8 35.3 8.4 36.6 19.0
绝对误差/m 0.1 0.6 0.4 1.3 1.0
Table 5  融合属性公式及误差分析
Fig.6  融合属性预测厚度
Fig.7  多属性融合预测分析
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