Please wait a minute...
E-mail Alert Rss
 
物探与化探  2025, Vol. 49 Issue (2): 288-298    DOI: 10.11720/wtyht.2025.1356
  方法研究信息处理仪器研制 本期目录 | 过刊浏览 | 高级检索 |
川东南地区凉高山组致密砂岩岩石物理建模
张郑玉成(), 苏建龙
中国石化勘探分公司,四川 成都 610041
Petrophysical modeling of tight sandstones of the Lianggaoshan Formation,Southeast Sichuan
ZHANG Zheng-Yu-Cheng(), SU Jian-Long
Exploration Company,SINOPEC,Chengdu 610041,China
全文: PDF(3907 KB)   HTML
输出: BibTeX | EndNote (RIS)      
摘要 

近年来四川盆地勘探开发实践表明,侏罗系陆相致密砂岩已经取得了重大突破,但是由于致密砂岩低孔低渗的特征,常规的叠后反演分辨率不足,并不能满足实际勘探储层预测精度的需求,因此需要利用叠前反演来进行致密砂岩的精细刻画,而在叠前反演中横波速度信息至关重要。本文以近年来川东南陆相探井为基础,研究了一套针对于川东南致密砂岩的岩石物理建模技术:充分考虑致密砂岩渗透率低以及孔隙中流体不均匀混合的特性,优选Domenico模型计算孔隙流体模量;考虑到实际地下情况,流体模量和密度必然是变化的,前人在岩石物理建模过程中大多用的是定值,本文则是与深度相关的值;川东南地区致密砂岩孔隙度一般都小于10%,利用Nur模型、Krief模型计算出来误差较大,本文优选Lee-Pride模型进行骨架模量的计算,通过引入固结参数α的值来控制岩石基质和骨架之间的关系。将建立好的致密砂岩岩石物理模型应用于实际工区,从应用效果来看与实钻井吻合度高,通过统计测井数据,优选敏感参数泊松比开展叠前高精度反演,可以对河道砂岩内幕进行精细刻画及预测。

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
张郑玉成
苏建龙
关键词 致密砂岩岩石物理建模孔隙流体模量横波预测叠前反演    
Abstract

The exploration and exploitation practices in the Sichuan Basin in recent years indicate that breakthroughs have been achieved in the Jurassic continental tight sandstones.Nevertheless,due to the low porosity and permeability of tight sandstone,conventional post-stack inversion frequently exhibits limited resolution,failing to meet the accuracy requirements for the prediction of actual exploration reservoirs.This necessitates pre-stack inversion for detailed characterization of tight sandstones,while S-wave velocity is crucial to pre-stack inversion.Based on continental exploration wells drilled in the southeastern Sichuan Basin in recent years,this study developed a petrophysical modeling technique for dense sandstones in this region.Specifically,given the low permeability of tight sandstones and the uneven mixing of fluids in the pore space,the Domenico model was preferentially employed to calculate the pore fluid modulus.Although fluid modulus and density are inevitably variable under the actual subsurface conditions,previous studies typically use constant values to conduct petrophysical modeling for tight sandstones.In this study,depth-dependent values were applied.Tight sandstones in the southeastern Sichuan Basin generally exhibit a porosity of less than 10%.Therefore,calculations using the Nur and the Krief models will yield high errors.Given this,this study preferred using the Lee-Pride model to calculate the skeleton modulus and controlled the relationship between the rock matrix and the skeleton by introducing the value of the cementation parameter.The application of the established petrophysical model of tight sandstone to an actual survey area indicates high agreement with data from actual wells.Additionally,based on log statistics,Poisson's ratio,the most sensitive parameter is used for high-precision pre-stack inversion in the proposed technique,enabling detailed characterization and prediction of the internal structure of channel sandstones.

Key wordstight sandstone    petrophysical modeling    pore fluid modulus    S-wave prediction    pre-stack inversion
收稿日期: 2024-09-09      修回日期: 2025-01-13      出版日期: 2025-04-20
ZTFLH:  P631.4  
基金资助:中国石化股份项目“四川盆地及周缘资源评价”(P23221)
作者简介: 张郑玉成(1996-),男,硕士研究生,2022年毕业于中国石油大学(北京),主要从事岩石物理建模和储层预测方面的理论、方法与应用研究工作。Email:1170342450@qq.com
引用本文:   
张郑玉成, 苏建龙. 川东南地区凉高山组致密砂岩岩石物理建模[J]. 物探与化探, 2025, 49(2): 288-298.
ZHANG Zheng-Yu-Cheng, SU Jian-Long. Petrophysical modeling of tight sandstones of the Lianggaoshan Formation,Southeast Sichuan. Geophysical and Geochemical Exploration, 2025, 49(2): 288-298.
链接本文:  
https://www.wutanyuhuatan.com/CN/10.11720/wtyht.2025.1356      或      https://www.wutanyuhuatan.com/CN/Y2025/V49/I2/288
Fig.1  岩石物理建模流程
Fig.2  岩石物理建模示意
类型 体积模量/GPa 剪切模量/GPa 密度/(g·cm-3)
石英 52 31 2.72
黏土 23 7 2.54
0.001 5 0 0.002
2.2 0 1.1
Table 1  石英、黏土和流体的体积模量、剪切模量和密度
参数 最小值 最大值 平均值
孔隙度/% 2.00 12.57 4.24
含水饱和度/% 3.21 100 52.10
泥质含量/% 1.06 62.19 21.90
Table 2  测井数据中孔隙度、含水饱和度和泥质含量的最大值、最小值和平均值
Fig.3  纵波速度(a)和横波速度(b)随泥质含量和固结参数α的变化
Fig.4  纵波速度(a)和横波速度(b)随孔隙度和固结参数α的变化
Fig.5  纵波速度(a)和横波速度(b)随含水饱和度和固结参数α的变化
Fig.6  岩石物理建模过程中需要A井中的测井数据
Fig.7  岩石物理建模过程中孔隙中水和气的体积模量和密度
Fig.8  A井纵横波实测结果和预测结果以及横波实测结果和预测结果的相对误差
Fig.9  岩石物理建模过程中需要B井中的测井数据
Fig.10  B井纵波速度的实测结果和预测结果以及横波速度的预测结果
Fig.11  A井泊松比和纵波阻抗交会
Fig.12  使用A井(a)和同时使用A、B井(b)得到的泊松比反演对比
Fig.13  原始泊松比测井曲线和两次泊松比反演结果曲线对比
[1] Ruiz F, Cheng A. A rock physics model for tight gas sand[J]. Leading Edge, 2010, 29(12):1484-1489.
[2] Avseth P, Johansen T A, Bakhorji A, et al. Rock-physics modeling guided by depositional and burial history in low-to-intermediate-porosity sandstones[J]. Geophysics, 2014, 79(2):D115-D121.
[3] 未晛, 杨志芳, 晏信飞, 等. 改进型随机斑块饱和模型及其在致密气层检测中的应用[J]. 石油地球物理勘探, 2018, 53(6):1227-1234.
[3] Wei S, Yang Z F, Yan X F, et al. Modified continuous random patchy-saturation model in tight gas detection[J]. Oil Geophysical Prospecting, 2018, 53 (6):1227-1234.
[4] 乔汉青, 方慧, 杜炳锐, 等. 基于改进Xu-White模型的富有机质页岩横波预测方法研究[J]. 物探化探计算技术, 2023, 45(4):411-419.
[4] Qiao H Q, Fang H, Du B R, et al. Research on transverse wave prediction method of organic-rich shale based on improved Xu-White model[J]. Computing Techniques for Geophysical and Geochemical Exploration, 2023, 45(4):411-419.
[5] Castagna J P, Batzle M L, Eastwood R L. Relationships between compressional-wave and shear-wave velocities in clastic silicate rocks[J]. Geophysics, 1985, 50(4):571-581.
[6] Han D H, Nur A, Morgan D. Effects of porosity and clay content on wave velocities in sandstones[J]. Geophysics, 1986, 51(11):2093-2107.
[7] Greenberg M L, Castagna J P. Shear-wave velocity estimation in porous rocks:Theoretical formulation,preliminary verification and applications[J]. Geophysical Prospecting, 1992, 40(2):195-209.
[8] Rajabi M, Bohloli B, Ahangar E G. Intelligent approaches for prediction of compressional,shear and Stoneley wave velocities from conventional well log data:A case study from the Sarvak carbonate reservoir in the Abadan Plain(Southwestern Iran)[J]. Computers & Geosciences, 2010, 36(5):647-664.
[9] 王晓光. 自适应BP神经网络在横波速度预测中的应用[J]. 岩性油气藏, 2013, 25(5):86-88.
[9] Wang X G. Application of self-adaptive BP neural network to the prediction of shear wave velocity[J]. Lithologic Reservoirs, 2013, 25(5):86-88.
doi: 10.3969/j.issn.1673-8926.2013.05.015
[10] Zhang Y, Zhong H R, Wu Z Y, et al. Improvement of petrophysical workflow for shear wave velocity prediction based on machine learning methods for complex carbonate reservoirs[J]. Journal of Petroleum Science and Engineering, 2020, 192:107234.
[11] Xu S, White R E. A new velocity model for clay-sand mixtu res[J]. Geophysical Prospecting, 1995, 43(1):91-118.
[12] Xu S, Payne M A. Modeling elastic properties in carbonate rocks[J]. Leading Edge, 2009, 28(1):66-74.
[13] Lee, Myung W. A simple method of predicting S-wave velocity[J]. Geophysics, 2006, 71(6):F161-F164.
[14] 张广智, 李呈呈, 印兴耀, 等. 基于修正Xu-White模型的碳酸盐岩横波速度估算方法[J]. 石油地球物理勘探, 2012, 47(5):717-722.
[14] Zhang G Z, Li C C, Yin X Y, et al. A shear velocity estimation method for carbonate rocks based on the improved Xu-White model[J]. Oil Geophysical Prospecting, 2012, 47(5):717-722.
[15] 张秉铭, 刘致水, 刘俊州, 等. 富有机质泥页岩岩石物理横波速度预测方法研究[J]. 石油物探, 2018, 57(5):658-667.
doi: 10.3969/j.issn.1000-1441.2018.05.004
[15] Zhang B M, Liu Z S, Liu J Z, et al. A new S-wave velocity estimation method for organic-enriched shale[J]. Geophysical Prospecting, 2018, 57(5):658-667.
[16] 王斌, 陈祥忠, 陈娟, 等. 四川盆地侏罗系致密砂岩弹性特征及岩石物理建模[J]. 地球物理学报, 2020, 63 (12):4528-4539.
doi: 10.6038/cjg2020O0346
[16] Wang B, Chen X Z, Chen J, et al. Elastic characteristics and petrophysical modeling of Jurassic tight sandstone in Sichuan Basin[J]. Chinese Journal of Geophysics, 2020, 63 (12):4528-4539.
[17] 张佳佳, 李宏兵, 张广智, 等. 基于多孔可变临界孔隙度模型的储层孔隙结构表征[C]// SPG/SEG北京2016国际地球物理会议, 2016.
[17] Zhang J J, Li H B, Zhang G Z, et al. Characterization of reservoir pore structure based on porous variable critical porosity model[C]// SPG/SEG Beijing 2016 International Geophysical Conference, 2016.
[18] Hashin Z, Shtrikman S. A variational approach to the theory of the elastic behaviour of multiphase materials[J]. Journal of the Mechanics and Physics of Solids, 1963, 11(2):127-140.
[19] Hill R. The elastic behavior of crystalline aggregate[J]. Proceedings of the Physical Society:Section A, 1952, 65(5):349.
[20] Domenico S N. Elastic properties of unconsolidated porous sand reservoirs[J]. Geophysics, 1977, 42(7):1339-1368.
[21] 贾凌云, 李琳, 王千遥, 等. 流体体积模量计算方法研究[J]. 地球物理学进展, 2018, 33 (1):223-227.
[21] Jia L Y, Li L, Wang Q Y, et al. Research on calculation methods of fluid bulk modulus[J]. Progress in Geophysics, 2018, 33 (1):223-227.
[22] Wood A B, Lindsay R B. A textbook of sound[J]. Physics Today, 1956, 9(11):37-37.
[23] 刘雯林. 油气田开发地震技术[M]. 北京: 石油工业出版社, 1996.
[23] Liu W L. Seismic technology for oil and gas field development[M]. Beijing: Petroleum Industry Press, 1996.
[24] Krief M, Garat J, Stellingwerff J, et al. A petrophysical interpretation using the velocities of P and S waves(full-waveform sonic)[J]. Log Analyst, 1990, 31:355-369.
[25] Nur A. Critical porosity and the seismic velocities in rocks[J]. EOS, 1992, 73(1):43-66.
[26] Pride S R. Relationships between seismic and hydrological properties[J]. Hydrogeophysics, 2005:217-255.
[27] 张佳佳, 李宏兵, 刘怀山, 等. 几种岩石骨架模型的适用性研究[J]. 地球物理学进展, 2010, 25(5):1697-1702.
[27] Zhang J J, Li H B, Liu H S, et al. Accuracy of dry frame models in the study of rock physics[J]. Progress in Geophysics, 2010, 25(5):1697-1702.
[1] 徐风, 司兆伟, 梁忠奎, 田超国, 罗兰, 郭宇航. 基于孔隙结构和多相渗流能力的鄂尔多斯盆地致密砂岩储层品质分类方法研究[J]. 物探与化探, 2025, 49(1): 138-147.
[2] 单博, 邢宇鑫, 张繁昌, 李志伟, 陈默. 基于二次编解码网络的适应性叠前反演方法[J]. 物探与化探, 2025, 49(1): 158-165.
[3] 何小龙, 张兵, 杨凯, 何一帆, 李琢. 基于沉积微相特征挖掘的随机森林岩石相测井识别方法——以新场地区须家河组二段致密砂岩为例[J]. 物探与化探, 2024, 48(5): 1337-1347.
[4] 曹绍贺, 任凤茹, 王霄霄. 东胜气田致密砂岩储层甜点预测关键技术与应用效果[J]. 物探与化探, 2024, 48(4): 954-961.
[5] 钟厚财, 刘振宇, 朱哲, 屈琳, 张珊, 姚燕飞, 范蓉蓉. 玛湖凹陷风城组复杂岩性组合横波预测方法探索[J]. 物探与化探, 2024, 48(3): 736-746.
[6] 牛丽萍, 胡华锋, 周单, 郑晓东, 耿建华. 基于精确Zoeppritz方程的贝叶斯叠前地震随机反演[J]. 物探与化探, 2024, 48(1): 77-87.
[7] 黄彦庆. 川东北元坝地区致密砂岩多产状裂缝刻画[J]. 物探与化探, 2023, 47(5): 1189-1197.
[8] 谢锐, 阎建国, 陈琪. 叠前各向异性系数反演及在裂缝预测中的应用[J]. 物探与化探, 2022, 46(4): 968-976.
[9] 商伟, 张云银, 孔省吾, 刘峰. 基于叠前多参数敏感因子融合的浊积岩储层识别技术[J]. 物探与化探, 2022, 46(4): 904-913.
[10] 张德明, 刘志刚, 臧殿光, 廖显锋, 刘志毅, 刘国宝. 基于叠前同时反演的致密砂岩储层预测及含气性识别——以苏里格S区块为例[J]. 物探与化探, 2022, 46(3): 645-652.
[11] 刘兰锋, 尹龙, 黄捍东, 周振亚, 董金超. 一种基于岩石物理建模的横波预测方法[J]. 物探与化探, 2021, 45(6): 1482-1487.
[12] 朱颜, 韩向义, 岳欣欣, 杨春峰, 常文鑫, 邢丽娟, 廖晶. 致密砂岩储层脆性测井评价方法研究及应用——以鄂尔多斯盆地渭北油田为例[J]. 物探与化探, 2021, 45(5): 1239-1247.
[13] 刘浩杰, 陈雨茂, 王延光, 宗兆云, 吴国忱, 侯庆杰. 粘弹介质叠前四参数同步反演及应用[J]. 物探与化探, 2021, 45(1): 140-148.
[14] 邓炜, 梁金强, 钟桐, 何玉林, 孟苗苗. 基于水合物指示因子的地震识别方法[J]. 物探与化探, 2021, 45(1): 57-67.
[15] 王超, 宋维琪, 林彧涵, 张云银, 高秋菊, 魏欣伟. 基于叠前反演的地应力预测方法应用[J]. 物探与化探, 2020, 44(1): 141-148.
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 , whtbjb@163.com