Please wait a minute...
E-mail Alert Rss
 
物探与化探  2019, Vol. 43 Issue (2): 234-243    DOI: 10.11720/wtyht.2019.1354
  地质调查·资源勘查 本期目录 | 过刊浏览 | 高级检索 |
改进的贝叶斯迭代反演方法及其在白云岩致密储层识别的应用
马琦琦, 孙赞东, 杨柳鑫
中国石油大学(北京) 地球物理与信息工程学院, 北京 102246
Modified Bayesian iterative inversion method and its application to dolomite tight oil reservoirs prediction
Qi-Qi MA, Zan-Dong SUN, Liu-Xin YANG
College of Geophysics and Information Engineering,China University of Petroleum(Beijing),Beijing 102246,China
全文: PDF(5003 KB)   HTML
输出: BibTeX | EndNote (RIS)      
摘要 

由于低孔隙度和低渗透率的白云岩致密油储集层的纵波阻抗与其围岩差异非常小,利用叠后反演技术难以有效预测储层,而可以提取丰富弹性信息的叠前反演是解决此问题的有效手段,但是由于噪声等问题,叠前反演方程有较强的不适定性,笔者在贝叶斯框架下引入了改进的多变量柯西分布和改进的低频约束因子,重新推导了反演方程,获得了新的目标函数,有效地减少了反演的不适定性,从而提高了反演的稳定性,并结合迭代的思想来不断更新反演求解过程中的背景纵横波速度比值,从而增加了反演结果的精度。模型数据测试和实际资料应用都证明了该方法的稳定性和适用性。统计表明,利用提出的反演方法,目的层段内优质储层厚度预测吻合率高达 89.75%。因此,此方法对类似硅质致密储层的勘探有重要的借鉴意义。

服务
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章
马琦琦
孙赞东
杨柳鑫
关键词 贝叶斯反演致密油储集层改进的约束横波数据优化储层识别    
Abstract

It is difficult to accurately predict the dolomite tight oil reservoir which has the characteristics of low porosity and low permeability by using the post-stack inversion,due to the small difference in acoustic impedance between the reservoir and its surrounding rock.Therefore,more abundant elastic information is needed.AVO inversion is an effective means to extract elastic information from pre-stack data.However,due to the noise and other factors,the pre-stack inversion equation has a strong ill-posed problem.Bayesian theory allows the construction of a regularization term by introducing a priori information about the model parameters,thereby effectively reducing the ill-posed problem of the inversion.Therefore,the modified Trivariate Cauchy constraint and the modified low-frequency constraint factor is introduced into the objective function,which can improve the ill-posed problem of the inversion,thus upgrading the accuracy of the inversion results.The iterative idea is used to address the non-linear nature of the proposed inverse operator.The P and S-wave velocity is updated in the iterations,which leads to more reliable results when applied to real data.Both the model data tests and the field data applications prove the validity and stability of the proposed method.Statistics show that,by using the proposed inversion method,the prediction accuracy rate of the reservoir thickness is as high as 89.5%.Therefore,this method has important reference significance for the exploration of similar siliceous reservoirs.

Key wordsBayesian inversion    tight oil reservoir    modified constraints    S-wave data optimization    reservoirs identification
收稿日期: 2018-09-27      出版日期: 2019-04-10
:  P631.4  
基金资助:国家科技重大专项“大型油气田及煤层气开发”课题“致密气有效储层预测技术”之“致密储层地震响应模式研究”子课题(2016ZX05047-002-001)
作者简介: 马琦琦(1990-),女,中国石油大学(北京)在读博士,主要从事叠前反演、储层预测研究工作。Email: ma_qi_qi@163.com
引用本文:   
马琦琦, 孙赞东, 杨柳鑫. 改进的贝叶斯迭代反演方法及其在白云岩致密储层识别的应用[J]. 物探与化探, 2019, 43(2): 234-243.
Qi-Qi MA, Zan-Dong SUN, Liu-Xin YANG. Modified Bayesian iterative inversion method and its application to dolomite tight oil reservoirs prediction. Geophysical and Geochemical Exploration, 2019, 43(2): 234-243.
链接本文:  
https://www.wutanyuhuatan.com/CN/10.11720/wtyht.2019.1354      或      https://www.wutanyuhuatan.com/CN/Y2019/V43/I2/234
Fig.1  柯西分布和改进的柯西分布约束对比
Fig.2  不同迭代过程中反演得到的横波阻抗反射系数(Rs)与理论值的对比
a—理论Rs;b—第一次迭代反演得到的Rs;c—第一次迭代反演得到的Rs与理论值之差;d—第二次迭代反演得到的Rs;e—第二次迭代反演得到的Rs与理论值之差;f—第三次迭代反演得到的Rs;g—第三次迭代反演得到的Rs与理论值之差
Fig.3  合成记录
a—无噪声;b—信噪比为2
Fig.4  实测井曲线与反演结果对比
a—无噪声反演纵波阻抗;b—无噪声反演横波阻抗;c—无噪声反演密度;d—信噪比为2时反演纵波阻抗;e—信噪比为2时反演横波阻抗;f—信噪比为2时反演密度
方法 纵波阻抗相关系数 横波阻抗相关系数 密度相关系数
无噪声 新方法 0.9993 0.9987 0.9937
常规方法 0.9988 0.9970 0.9851
有噪声 新方法 0.9903 0.9742 0.8684
常规方法 0.9766 0.9433 0.7873
Table 1  反演结果的相关系数统计
Fig.5  白云岩致密油层段纵波阻抗与横波阻抗交会
Fig.6  研究层段反演结果对比
a—常规方法反演纵波阻抗;b—常规方法反演横波阻抗;c—新方法反演纵波阻抗; d—新方法反演横波阻抗
Fig.7  井旁道反演结果与实测井曲线的对比
a—纵波阻抗;b—横波阻抗
Fig.8  储层厚度预测流程
Fig.9  目的层段预测优质白云岩储层厚度
井名 实际厚度/m 预测厚度/m 吻合率/% 井名 实际厚度/m 预测厚度/m 吻合率/%
Well 1 59.56 50 84 Well 9 87 92 94
Well 2 9.79 11 88 Well 10 53.54 54 99
Well 3 72.5 71 98 Well 11 42.56 36 85
Well 4 47.3 48.5 95 Well 12 92.5 79.19 86
Well 5 62.87 54 86 Well 15 71.31 57 80
Well 6 49 49.8 98 Well 16 24.97 27 92
Well 7 65.1 80.9 76 Well 18 55.5 57.2 97
Well 8 54.02 51.05 95 Well 22 80 94 83
总吻合率/% 89.75
Table 2  研究区内实际钻遇优质白云岩厚度与预测厚度吻合率统计
[1] 贾承造, 郑民, 张永峰 , 等. 中国非常规油气资源与勘探开发前景[J]. 石油勘探与开发, 2012,39(2):129-136.
[1] Jia C Z, Zheng M, Zhang Y F , et al. Unconventional hydrocarbon resources in China and the prospect of exploration and development[J]. Petroleum Exploration and Development, 2012,39(2):129-136.
[2] 邹才能, 杨智, 朱如凯 , 等. 中国非常规油气勘探开发与理论技术发展[J]. 地质学报, 2015,89(6):979-1007.
doi: 10.3969/j.issn.0001-5717.2015.06.001
[2] Zou C N, Yang Z, Zhu R K , et al. Progress in China’s unconventional oil and gas exploration and development and theoretical technologies[J]. Acta Geologica sinica, 2015,89(6):979-1007.
[3] 田忠斌, 申有义, 王建青 , 等. 非常规气地震勘探采集技术——以沁水煤田中东部煤系为例[J]. 物探与化探, 2016,40(1):167-173.
doi: 10.11720/wtyht.2016.1.30
[3] Tian Z B, Shen Y Y, Wang J Q , et al. Unconventional gas seismic exploration acquisition technology:A case study of the middle east Qinshui coalfield[J]. Geophysical and Geochemical Exploration, 2016,40(1):167-173.
[4] 杜江民, 张小莉, 钟高润 , 等. 致密油烃源岩有机碳含量测井评价方法优选及应用——以鄂尔多斯盆地延长组长7段烃源岩为例[J]. 地球物理学进展, 2017,31(6):2526-2533.
[4] Du J M, Zhang X L, Zhong G R , et al. Analysis on the optimization and application of well logs indentification methods for organic carbon content in source rocks of the tight oil-illustrated by the example of the source rocks of Chang 7 member of Yanchang Formation in Ordos Basin[J]. Progress in Geophysics, 2017,31(6):2526-2533.
[5] 王社教, 蔚远江, 郭秋麟 , 等. 致密油资源评价新进展[J]. 石油学报, 2014,35(6):1095-1105.
doi: 10.7623/syxb201406007
[5] Wang S J, Wei Y J, Guo Q L , et al. New advance in resources evaluation of tight oil[J]. Acta Petrolei sinica, 2014,35(6):1095-1105.
[6] 章雄, 张本健, 梁虹 , 等. 波形指示叠前地震反演方法在致密含油薄砂层预测中的应用[J]. 物探与化探, 2018,42(3):545-554.
doi: 10.11720/wtyht.2018.1050
[6] Xiong Z, Zhang B J, Hong L , et al. The application of pre-stack inversion based on seismic waveform indicator to the prediction of compact and thin oil-bearing sand layer[J]. Geophysical and Geochemical Exploration, 2018,42(3):545-554.
[7] Zhou L, Li J, Chen X , et al. Pre-stack AVA inversion of exact Zoeppritz equations based on modified trivariate Cauchy distribution[J]. Journal of Applied Geophysics, 2017,138:80-90.
doi: 10.1016/j.jappgeo.2017.01.009
[8] Aki K, Richards P . Quantitative Seismology[M]. New York:W. H. Freeman & Co, 1980.
[9] Smith G C, Gidlow P M . Weighted stacking for rock property estimation and detection of gas[J]. Geophysical Prospecting, 1987,35:993-1014.
doi: 10.1111/j.1365-2478.1987.tb00856.x
[10] Goodway B, Chen T W, Downton J. Improved AVO fluid detection and lithology discrimination using Lame petrophysical parameters:”λρ”,”μρ”, &”λ/μ fluid stack,from P and S inversions [C]//Expanded Abstracts of the 67 th Annual SEG Meeting,Society of Exploration Geophysicists , 1997,16:183-186.
[11] Russell B H, Gray D, Hampson D P . Linearized AVO inversion and poroelasticity[J]. Geophysics, 2011,76(3):19-29.
[12] 宗兆云, 印兴耀, 张峰 , 等. 杨氏模量和泊松比反射系数近似方程及叠前地震反演[J]. 地球物理学报, 2012,55(11):3786-3794.
doi: 10.6038/j.issn.0001-5733.2012.11.025
[12] Zong Z Y, Yin X Y, Zhang F , et al. Reflection coefficient equotion and pre-stack seismic inversion with Young’s modulus and Possion ratio[J]. Chinese Journal of Geophysics, 2012,55(11):3786-3794.
[13] 王彦飞, 唐静, 耿伟峰 , 等. 带粒子滤波约束的PP-PS联合反演的稀疏解算法[J]. 地球物理学报, 2018,61(3):1169-1177.
doi: 10.6038/cjg2018L0331
[13] Wang Y F, Tang J, Geng W F , et al. Sparse solution of PP-PS joint inversion with constraint of particle filtering[J]. Chinese Journal of Geophysics, 2018,61(3):1169-1177.
[14] Buland A, Omre H . Bayesian linearised AVO inversion[J]. Geophysics, 2003,68:185-198.
doi: 10.1190/1.1543206
[15] Alemie W, Sacchi M D . High-resolution three-term AVO inversion by means of a trivariate Cauchy probability distribution[J]. Geophysics, 2011,76:43-55.
[16] Zhang J H, Zhang B B, Zhang Z J , et al. Low-frequency data analysis and expansion[J]. Applied Geophysics, 2015,12(2):212-220.
doi: 10.1007/s11770-015-0484-2
[17] Fang Y, Zhang F Q, Wnag Y C . Generalized linear joint PP-PS inversion based on two constraints[J]. Applied Geophysics, 2016,13(1):103-115.
doi: 10.1007/s11770-016-0527-3
[18] 贾凌霄, 王彦春, 菅笑飞 , 等. 叠后地震反演面临的问题与进展[J]. 地球物理学进展, 2016,31(5):2108-2115.
[18] Jia L X, Wang Y C, Jian X F , et al. Problems and progress in post-stack seismic inversion[J]. Progress in Geophysics, 2016,31(5):2108-2115.
[19] Ma Q Q, SUN Z D. Fluid identification of secondary carbonate reservoir based on iterative AVO inversion [C]//Expanded Abstracts of the 86 th Annual SEG Meeting,Society of Exploration Geophysicists , 2016: 602-606.
[20] Bortfeld R . Approximation to the reflection and transmission coefficients of plane longitudinal and transverse waves[J]. Geophysical Journal of the Royal Astronomical Society, 1978,53:467-496.
doi: 10.1111/j.1365-246X.1978.tb03754.x
[21] 张丰麒, 金之钧, 盛秀杰 , 等. 贝叶斯三参数低频软约束同步反演[J]. 石油地球物理勘探, 2016,51(5):965-975.
doi: 10.13810/j.cnki.issn.1000-7210.2016.05.017
[21] Zhang F Q, Jin Z J, Sheng X J , et al. Bayesian pre-stack three-term inversion with soft low-frequency constraint[J]. Oil Geophysical Prospecting, 2016,51(5):965-975.
[22] Backs G E, Gibert F . The resolving power of gross earth data[J]. Geophysical Journal International, 1968,16:169-205.
doi: 10.1111/j.1365-246X.1968.tb00216.x
[23] 黄捍东, 汪佳蓓, 郭飞 . 敏感参数分析在叠前反演流体识别中的应用[J]. 物探与化探, 2012,36(6):941-946.
doi: 10.11720/wtyht.2012.6.10
[23] Huang H D, Wang J B, Guo F . Thev application of sensitive parameters analysis to fluid identification based on pre-stack inversion[J]. Geophysical and Geochemical Exploration, 2012,36(6):941-946.
[1] 秦臻, 林振洲, 潘和平, 方思南, 邓呈祥, 覃瑞东, 纪扬, 徐伟. 木里水合物测井评价系统[J]. 物探与化探, 2017, 41(6): 1275-1280.
[2] 田瀚, 杨敏. 碳酸盐岩缝洞型储层测井评价方法[J]. 物探与化探, 2015, 39(3): 545-552.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
京ICP备05055290号-3
版权所有 © 2021《物探与化探》编辑部
通讯地址:北京市学院路29号航遥中心 邮编:100083
电话:010-62060192;62060193 E-mail:whtbjb@sina.com