Digital rock physics-based studies on effect of pore types on elastic properties of carbonate reservoir Part 2: Pore structure factor characterization and inversion of reservoir
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摘要:
碳酸盐岩复杂的孔隙结构如何影响其弹性性质一直是地球物理研究的难点问题，在此基础上如何半定量甚至是定量地对碳酸盐岩储层预测，特别是如何有效地获取孔隙结构参数相关的地震属性体一直是油气工业界追求的目标，本研究从数字岩心角度入手，联合测井以及地震数据尝试探究这一问题的解决方案.首先针对代表不同孔隙结构类型的有限数目的碳酸盐岩样品获得其对应的高精度数字岩心数据体，为了获得更加可靠的具有地球物理含义的弹性性质随孔隙度变化的统计规律，我们通过子网格的技术，在有限数目的碳酸盐岩数字岩心数据体上获得了大量的数字岩心子网格样本，对于每个子网格样本可以分别获得其对应的数字岩心图像孔隙度、表征孔隙软硬程度的孔隙结构参数（γ）、以及基于有限元法模拟的弹性性质，由此基于数字岩心的研究思路，我们最终获得了基于孔隙结构因子表征与分类下的弹性性质与孔隙度的定量化解释量版.与此同时，在地震尺度上通过叠前地震资料获取的纵横波及密度属性体后，基于如上获得的定量化解释量版，我们最终获得了针对碳酸盐岩储层的新的属性体——孔隙结构参数（γ）属性体，这使得在地震尺度上预测碳酸盐岩储层的孔隙结构类型成为可能，也使利用地震数据在孔隙结构参数表征与分类下的碳酸盐岩储层反演精度的提高成为可能.
Abstract:How the complex pore structure of carbonate rock affects the elastic properties of carbonate rock has always been a difficult problem in the research of geophysics. How to make semi-quantitative or even quantitative predictions of carbonate reservoirs on this basis, and in particular how to effectively obtain pore structure parameters-related seismic property bodies have been pursued by the oil and gas industry. This study attempts to explore the solution to this problem from the perspective of digital cores, combined with well logging and seismic data. To this end, we firstly obtain high-resolution digit core data sets for a limited number of carbonate rock samples with representative pore structures, and then subgridding technique is applied to well-established digital core data sets in order to obtain a large amount of data sets of subgridding digital core, which can meet the need to analyze the relationship of elastic properties vs porosity in a statistical sense. For each subgridding digital core, we can obtain its image porosity, porosity structure factor γ to characterize pore stiffness, and finite element method-based elastic properties respectively. Subsequently, a quantitative interpretation template can be achieved to establish the relationship analysis of elastic properties vs porosity on the basis of pore structure characterization and classification. The quantitative interpretation template is then used to seismic scale VP, VS, and density attributes through pre-stack inversion technique, and finally a newly proposed seismic attribute-pore structure factor γ is obtained for carbonate reservoir prediction and characterization.
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Key words:
- Carbonate /
- Pore structure type /
- Elastic properties /
- Digital core /
- Image processing /
- Binarization
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图 1
7块白云岩样品数字岩心数据体的构建
Figure 1.
Reconstruction on digital core data of seven dolomite samples
图 2
7块白云岩样品的孔隙结构描述与分析
Figure 2.
Pore structure description and analysis of seven dolomite samples
图 3
孔隙结构参数表征与分类下弹性性质与孔隙度关系获取流程图
Figure 3.
Flowchart of analysis on elastic properties vs porosity under pore structure characterization and classification
图 6
基于数字岩心模拟的孔隙结构表征与分类下的纵波速度与孔隙度交会分析
Figure 6.
Digital core based analysis on P-wave velocity vs porosity under pore structure characterization and classification
图 7
基于数字岩心模拟(散点)与差分等效介质理论(虚线)的体积模量随孔隙度变化
Figure 7.
Digital core-simulated (scatter) and DEM theory-based (dashed line) bulk modulus versus porosity
图 8
基于地震数据的孔隙结构属性体预测流程
Figure 8.
Workflow on seismic data-based pore structure attribute prediction
图 10
基于机器学习的预测孔隙度与测井孔隙度的比较
Figure 10.
Comparison between machine learning-predicted and logging-interpreted porosities
图 14
井旁地震道γ因子孔隙结构预测与各孔隙含量预测对比
Figure 14.
Pore structure factor (γ) and different pore types-based volume fraction estimated from a borehole-side seismic trace
表 1
样品基本信息
Table 1.
Sample description
编号 干燥密度
(g·cm-3)矿物 孔隙度
(%)渗透率
(%)白云石
(%)石英
(%)菱铁矿
(%)1# 2.72 84.7 0.8 14.5 3.14 129.73 2# 2.75 92.6 7.4 - 2.89 11.01 3-1# 2.70 92.6 7.4 - 3.71 0.003 3-2# 2.74 92.6 7.4 - 2.67 0.029 4-1# 2.79 96.5 3.5 - 0.97 0.0005 4-2# 2.79 96.5 3.5 - 2.25 0.0002 5# 2.78 70 30 - 0.60 0.0003 
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表 2
7块样品镜下矿物学描述与孔隙类型分析
Table 2.
Microscopic analysis of mineralogy and pore types for seven samples
编号 主要矿物
(镜下)白云石晶粒含量 岩石结构与
地质特征镜下定名 CT及镜下的
孔隙类型细晶
(0.2~0.3 mm)粉晶
(< 0.1 mm)中晶
(>0.5 mm)1# 白云石
(少量硅质)80% 15% 5% 粉晶-细晶结构 粉-细晶白云岩 微裂隙
(面缝率 < 1%)2# 白云石 80% 15% 5% 粉晶-细晶结构 粉-细晶白云岩 晶间孔(面缝率2%)
与微裂隙共存3-1# 白云石97%
硅质物1%
有机质2%3% 95% 2% 粉晶结构 粉晶白云岩 晶间孔(面缝率3%) 3-2# 白云石74%
硅质物25%
有机质1%95% 2% 3% 细晶结构 硅质细晶白云岩 晶间孔(面缝率4%) 4-1# 白云石99%
硅质物1%藻类(藻团块)
70%亮晶白云石
(马牙状)(25%)云泥(5%) 颗粒结构 亮晶藻白云岩 晶间孔(少量)与微裂隙
(面缝率 < 1%)共存4-2# 白云石99%
硅质物1%藻类(藻团块)
60%亮晶白云石
(马牙状)(35%)云泥(5%) 颗粒结构 亮晶藻白云岩 晶间孔(少量)与微裂隙
(面缝率 < 1%)共存5# 白云石95%
硅质物1%
有机质4%- 粉晶：95%
泥晶：5%- 含藻泥-粉晶
结构含藻泥-粉晶
白云岩微裂隙
(面缝率 < 1%)
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