储层沉积微相随机模拟方法及应用
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摘要
我国的油田开发正面临着从易开发区到难开发区、从部分油藏的高含水、高采出程度，到储采基本平衡向严重不平衡过渡的严峻形势。加之考虑到我国陆相储层的复杂性，都向石油开发工业提出了新的难题，需要对储层进行更加深入、精细的研究。
沉积微相是体现储层物理性质及油藏分布的最基本也是最重要的成因单元，储层模拟的目的就是为了建立一个可供研究剩余油饱和度分布的油藏地质模型。这涉及如何利用已知的井位资料估计井间未钻井地区的储层沉积微相变化和各种岩石物理参数值。
地下储层本身是确定的，在每一个位置点都具有确定的性质和特征。但地下储层又是很复杂的，它是许多复杂地质过程(沉积作用、成岩作用和构造作用)综合作用的结果，具有复杂的储层结构(储层相)空间配置及储层参数的空间变化。在进行储层描述过程中，由于用于描述储层的资料总是不完备，因此人们又难于掌握任一尺度下储层的确定的且真实的特征或性质。特别是对于连续性较差且非均质性强的陆相储层来说，更难于精确表征储层的特征。这样，由于认识程度的不足，储层描述便具有不确定性。这些需要通过“猜测”而确定的储层性质，即为储层的随机性质。而随机模拟技术的出现，为更准确的预测储层沉积微相的分布提供了可能。
地质变量的空间变异性理论是随机模拟技术的基础，其最重要的特点在于利用变异函数描述地质变量的空间分布型式。变异函数反映了地质变量空间变异程度随距离而变化的特征，它强调变量在空间上的数据构型，从而可以定量的描述地质规律所造成的储层参数在空间上的相关性。应用变异函数理论对地质变量进行空间结构分析主要包括以下五个主要步骤：1、井位数据的统计分析 2、求取实验变异函数 3、拟合实验变异函数 4、实验变异函数的套合 5、变异函数的检验与修改。
所谓随机模拟，是指以已知的信息为基础，以随机函数为理论，应用随机模拟方法，产生可选的、等概率的储层模型的方法。这种方法承认控制点以外的储层参数具有一定的不确定性，即具有一定的随机性。因此采用随机模拟方法所建立的储层模型不是一个，而是多个，即一定范围内的几种可能实现(即所谓可选的储层模型)，以满足油田开发决策在一定风险范围的正确性的需要。
同传统的地质统计学方法(如克立金方法)相比，随机模拟方法有如下特点：
摘要
l)模拟算法是依赖于模型的，不同模型可能要采用不同的模拟算法。
2)随机模拟结果强调结果的整体相关性。它从整体上对储层属性空间分
布提供了不确定性的度量。而克里金方法不能保证条件化到统计量，所得到
的结果，其直方图和协方差与原始数据计算结果会有很大偏差。
3)克里金方法只给出一个数值结果，而随机模拟能给出多种数值结果。
这些结果的异同正好反映了隐含在概率模型之中的不确定性。
因此，随机模拟方法能够对储层沉积微相的非均质性进行表征，进而
对沉积微相的分布作出模拟和预测。
葡萄花油田位于黑龙江省安达市与肇源县交界附近，是大庆长垣南部
的一个油田。地理坐标位于北纬45’48’一4603‘，东经1240 30‘一124
。45‘范围内。
精细沉积相研究结果表明，葡l组油层自下而上细分为湖退型外前缘、
湖退型内外前缘过渡相、湖退型内前缘、稳定型内前缘、湖进型内前缘、湖
进型内外前缘过渡相和湖进型外前缘七种细分亚相;主体席状砂、非主体席
状砂、席间透镜状砂、水下分流浅河道、水下分流主河道、水下分流浅河道、
水下分流浅滩、分流间透镜状砂、八种沉积微相。对葡I组油层储层沉积微
相的随机模拟研究是建立在以上精细储层沉积学的基础上的。
本次研究完成的主要工作包括:
1、在查阅并研读英文文献38篇，中文文献14篇的基础上，对当今流
行的地质统计学插值方法与随机模拟方法进行了系统、深入的研究。
2、对进行插值与随机模拟方法研究时使用的GSLIB程序库数千行程序
代码进行了分析，在Mierosoft Visual Studio 6.0集成开发环境下对全部代码
进行了重新的编译调试，在此基础上进行应用研究。
3、建立了反映葡I油组十一个储层单元沉积微相详细信息的知识数据
库与进行储层沉积微相随机模拟预测的详细工作流程。
4、应用VC++开发工具开发了进行数据预处理时需要的数据转换与数
据配准工具程序。
5、对大庆葡萄花油田葡I油组十一个含油小层级的储层单元沉积微相
数据进行了百余次实验变异函数的试算与拟合。
6、应用最小邻距插值、普通克立金插值和序贯高斯模拟、截断高斯模
拟、序贯指示模拟方法对葡I油组十一个储层单元沉积微相分布进行了预测
分析。
在上述研究工作的基础上，主要获得以下几点认识:
1、应用随机模拟方法进行储层沉积微相预测，不仅能够预测相分布的
总体特征，还能够体现出局部变化特征。相比传统的插值方法，随机模拟方
法能够更加准确的表征储层岩相的非均质性。
2、储层地质变量数据的空间构型分析是进行储层随机模拟的重要步骤，
数据的空间变异性对模拟结果能够产生重要的影响。通过变异函数的变程反
映储层沉积微相?
Oil field exploitation in our country is confronting the critical facts that from easy development areas to hard areas, from the oil pool which high hydrous and high degree of exploitation, from the balance of reserve and exploitation to serious imbalance. Considering the complexity of continental reservoir, all that above-described bring forward a lot of new difficult problems to oil industry, so there is a long way to go.
Sedimentary microfacies is the most basic and important genesis unit which shows the reservoir physical properties and oil pool distribution. Reservoir simulation aims at establishing an oil pool geological model which can be used to study the distribution of residual oil saturation. This paper mainly concerns how to utilize the known drill site data to estimate the variaty of reservoir sedimentary microfacies and a variety of rock physical parameter in the districts with no dill site data between wells.
Underground reservoir is determinate on itself. That is it has determinate properties and characteristics at every position. But it is complex, too. Many complex geology process affect it, so it has the complex spatial arrangement of reservoir structure and spatial variation of reservoir parameter. In the course of reservoir description, we have so much difficulty in mastering its determinate and substantial characteristics or properties at any scale because of lack of record. Especially to the land reservoir with uncontinuity and heterospheric, it is more difficult in characterizationing. Because of the deficient recognition, reservoir description typifies indeterminacy. These reservoir properties that need "guess" are random distribution. With the appearance of stochastic simulation, it is possible for us to forecast the distribution of reservoir Sedimentary microfacies accurately.
The theory of geologic variable spatial variability is the basis of stochastic simulation. Using variogram to describe the spatial distribution of geologic variable. Variogram shows that the variant degree of geologic variable varies with the distance, and emphasizes on the data configuration in space so that we can
describe the relativity of reservoir parameters in space by geological regulation quantitatively. Five main steps are included during using the theory of variogram to analysis geologic spatial variable: 1?Analyze drill sit data with statistical method. 2?Get experimental variogram 3?Fit experimental variogram.4?Match the experimental variogram. 5, Detect and modify variogram.
Stochastic simulation is a method that is on the basis of known information, using stochastic simulation to produce alternative, equal probability reservoir model. The method manifests that reservoir parameter out of control point is indeterminate, that is so called stochastic. Therefore, the reservoir model established with stochastic simulation is not only one, but several. That is there are several possibilities at a given range, so that satisfy the truth requirements of exploitative decision at a range.
Comparing with the traditional geostatistics (such as Kriging), stochastic simulation typify the fowling :
(1) The simulation algorithm is on the basis of model, different model may use different simulation algorithm.
(2) The result take the emphasis on global relativity. It offers the metric of indeterminateness to attributive spatial distribution of reservoir in general. But Kriging can not assure conditionalization to statistic, and the bar chart and covariance in the result may have much deviation comparing with genesis data.
(3) Kriging has only one numerical solution, but stochastic simulation has multi-valued solution. Just these identity and difference implicate the indeterminateness hided in the probabilistic model.
Hence, stochastic simulation can characterize the heterospheric of reservoir sedimentary microfacies and simulate and forecast its distribution.
Pu-taohua oil field, situated in the border land of An-da city and Zhao-yuan country, is one of the oil fields in the south of Da-qing C
沉积微相是体现储层物理性质及油藏分布的最基本也是最重要的成因单元，储层模拟的目的就是为了建立一个可供研究剩余油饱和度分布的油藏地质模型。这涉及如何利用已知的井位资料估计井间未钻井地区的储层沉积微相变化和各种岩石物理参数值。
地下储层本身是确定的，在每一个位置点都具有确定的性质和特征。但地下储层又是很复杂的，它是许多复杂地质过程(沉积作用、成岩作用和构造作用)综合作用的结果，具有复杂的储层结构(储层相)空间配置及储层参数的空间变化。在进行储层描述过程中，由于用于描述储层的资料总是不完备，因此人们又难于掌握任一尺度下储层的确定的且真实的特征或性质。特别是对于连续性较差且非均质性强的陆相储层来说，更难于精确表征储层的特征。这样，由于认识程度的不足，储层描述便具有不确定性。这些需要通过“猜测”而确定的储层性质，即为储层的随机性质。而随机模拟技术的出现，为更准确的预测储层沉积微相的分布提供了可能。
地质变量的空间变异性理论是随机模拟技术的基础，其最重要的特点在于利用变异函数描述地质变量的空间分布型式。变异函数反映了地质变量空间变异程度随距离而变化的特征，它强调变量在空间上的数据构型，从而可以定量的描述地质规律所造成的储层参数在空间上的相关性。应用变异函数理论对地质变量进行空间结构分析主要包括以下五个主要步骤：1、井位数据的统计分析 2、求取实验变异函数 3、拟合实验变异函数 4、实验变异函数的套合 5、变异函数的检验与修改。
所谓随机模拟，是指以已知的信息为基础，以随机函数为理论，应用随机模拟方法，产生可选的、等概率的储层模型的方法。这种方法承认控制点以外的储层参数具有一定的不确定性，即具有一定的随机性。因此采用随机模拟方法所建立的储层模型不是一个，而是多个，即一定范围内的几种可能实现(即所谓可选的储层模型)，以满足油田开发决策在一定风险范围的正确性的需要。
同传统的地质统计学方法(如克立金方法)相比，随机模拟方法有如下特点：
摘要
l)模拟算法是依赖于模型的，不同模型可能要采用不同的模拟算法。
2)随机模拟结果强调结果的整体相关性。它从整体上对储层属性空间分
布提供了不确定性的度量。而克里金方法不能保证条件化到统计量，所得到
的结果，其直方图和协方差与原始数据计算结果会有很大偏差。
3)克里金方法只给出一个数值结果，而随机模拟能给出多种数值结果。
这些结果的异同正好反映了隐含在概率模型之中的不确定性。
因此，随机模拟方法能够对储层沉积微相的非均质性进行表征，进而
对沉积微相的分布作出模拟和预测。
葡萄花油田位于黑龙江省安达市与肇源县交界附近，是大庆长垣南部
的一个油田。地理坐标位于北纬45’48’一4603‘，东经1240 30‘一124
。45‘范围内。
精细沉积相研究结果表明，葡l组油层自下而上细分为湖退型外前缘、
湖退型内外前缘过渡相、湖退型内前缘、稳定型内前缘、湖进型内前缘、湖
进型内外前缘过渡相和湖进型外前缘七种细分亚相;主体席状砂、非主体席
状砂、席间透镜状砂、水下分流浅河道、水下分流主河道、水下分流浅河道、
水下分流浅滩、分流间透镜状砂、八种沉积微相。对葡I组油层储层沉积微
相的随机模拟研究是建立在以上精细储层沉积学的基础上的。
本次研究完成的主要工作包括:
1、在查阅并研读英文文献38篇，中文文献14篇的基础上，对当今流
行的地质统计学插值方法与随机模拟方法进行了系统、深入的研究。
2、对进行插值与随机模拟方法研究时使用的GSLIB程序库数千行程序
代码进行了分析，在Mierosoft Visual Studio 6.0集成开发环境下对全部代码
进行了重新的编译调试，在此基础上进行应用研究。
3、建立了反映葡I油组十一个储层单元沉积微相详细信息的知识数据
库与进行储层沉积微相随机模拟预测的详细工作流程。
4、应用VC++开发工具开发了进行数据预处理时需要的数据转换与数
据配准工具程序。
5、对大庆葡萄花油田葡I油组十一个含油小层级的储层单元沉积微相
数据进行了百余次实验变异函数的试算与拟合。
6、应用最小邻距插值、普通克立金插值和序贯高斯模拟、截断高斯模
拟、序贯指示模拟方法对葡I油组十一个储层单元沉积微相分布进行了预测
分析。
在上述研究工作的基础上，主要获得以下几点认识:
1、应用随机模拟方法进行储层沉积微相预测，不仅能够预测相分布的
总体特征，还能够体现出局部变化特征。相比传统的插值方法，随机模拟方
法能够更加准确的表征储层岩相的非均质性。
2、储层地质变量数据的空间构型分析是进行储层随机模拟的重要步骤，
数据的空间变异性对模拟结果能够产生重要的影响。通过变异函数的变程反
映储层沉积微相?
Oil field exploitation in our country is confronting the critical facts that from easy development areas to hard areas, from the oil pool which high hydrous and high degree of exploitation, from the balance of reserve and exploitation to serious imbalance. Considering the complexity of continental reservoir, all that above-described bring forward a lot of new difficult problems to oil industry, so there is a long way to go.
Sedimentary microfacies is the most basic and important genesis unit which shows the reservoir physical properties and oil pool distribution. Reservoir simulation aims at establishing an oil pool geological model which can be used to study the distribution of residual oil saturation. This paper mainly concerns how to utilize the known drill site data to estimate the variaty of reservoir sedimentary microfacies and a variety of rock physical parameter in the districts with no dill site data between wells.
Underground reservoir is determinate on itself. That is it has determinate properties and characteristics at every position. But it is complex, too. Many complex geology process affect it, so it has the complex spatial arrangement of reservoir structure and spatial variation of reservoir parameter. In the course of reservoir description, we have so much difficulty in mastering its determinate and substantial characteristics or properties at any scale because of lack of record. Especially to the land reservoir with uncontinuity and heterospheric, it is more difficult in characterizationing. Because of the deficient recognition, reservoir description typifies indeterminacy. These reservoir properties that need "guess" are random distribution. With the appearance of stochastic simulation, it is possible for us to forecast the distribution of reservoir Sedimentary microfacies accurately.
The theory of geologic variable spatial variability is the basis of stochastic simulation. Using variogram to describe the spatial distribution of geologic variable. Variogram shows that the variant degree of geologic variable varies with the distance, and emphasizes on the data configuration in space so that we can
describe the relativity of reservoir parameters in space by geological regulation quantitatively. Five main steps are included during using the theory of variogram to analysis geologic spatial variable: 1?Analyze drill sit data with statistical method. 2?Get experimental variogram 3?Fit experimental variogram.4?Match the experimental variogram. 5, Detect and modify variogram.
Stochastic simulation is a method that is on the basis of known information, using stochastic simulation to produce alternative, equal probability reservoir model. The method manifests that reservoir parameter out of control point is indeterminate, that is so called stochastic. Therefore, the reservoir model established with stochastic simulation is not only one, but several. That is there are several possibilities at a given range, so that satisfy the truth requirements of exploitative decision at a range.
Comparing with the traditional geostatistics (such as Kriging), stochastic simulation typify the fowling :
(1) The simulation algorithm is on the basis of model, different model may use different simulation algorithm.
(2) The result take the emphasis on global relativity. It offers the metric of indeterminateness to attributive spatial distribution of reservoir in general. But Kriging can not assure conditionalization to statistic, and the bar chart and covariance in the result may have much deviation comparing with genesis data.
(3) Kriging has only one numerical solution, but stochastic simulation has multi-valued solution. Just these identity and difference implicate the indeterminateness hided in the probabilistic model.
Hence, stochastic simulation can characterize the heterospheric of reservoir sedimentary microfacies and simulate and forecast its distribution.
Pu-taohua oil field, situated in the border land of An-da city and Zhao-yuan country, is one of the oil fields in the south of Da-qing C
引文
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[40] 纪发华，熊琦华，张一伟．地质统计学在油藏描述中的应用．石油大学出版社，1992，1．
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[44] Tom Swan．汤姆·斯旺C++编程秘诀．电子工业出版社，1994，1．
[45] 陆明德，田时芸．石油天然气数学地质．中国地质大学出版社，1991，1．
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[49] 梅启太．葡北油田储层精细地质描述及剩余油分布研究．吉林大学硕士学位论文，2000．
[50] 薛林福，王剑，王东坡，梅启太，孙权．储层沉积微相带自动跟踪方法．石油与天然气地质，2004，1，88～92．
[51] 边肇祺，张学工．模式识别．清华大学出版社，2000，2．
[52] 曾允孚，夏文杰．沉积岩石学．地质出版社，1989，2．
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