新场构造沙溪庙组碎屑岩储层弹性敏感性参数研究
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摘要
本论文主要基于国家科技重大专项的二级专题<<四川盆地碎屑岩储层测井弹性参数预测技术研究>>,针对新场构造沙溪庙组碎屑岩不同储层类型,以及现阶段测井和地震联合进行储层预测方面的一些局限性,通过开展岩石物理弹性参数的精细分析,总结出新场构造沙溪庙组不同岩性、含流体性等弹性参数,重点突出目的层段优质储层弹性敏感性参数,并建立岩性、含流体性弹性参数图版及敏感性参数表。将测井和地震以岩石物理弹性参数为桥梁紧密结合,为形成有效井震约束反演方法技术体系,提高有效储层分布预测研究服务。
首先,在充分利用研究区内多种测井方法(放射性、电阻率、声学、地球化学等)的基础上,采用BP神经网络算法、正态概率分布法、电阻率与孔隙度交会法、最优化算法等方法,针对研究区从定性和定量两方面进行了岩性和含油气性评价研究,为岩石物理弹性参数的研究提供了准确的研究对象。
其次,在研究区沙溪庙组缺少横波测井资料的背景上,依据前人的研究成果,在分析本区实际情况后优选Castagna方法结合VRH模型作为横波预测的最优方法,并推广到本区无横波井,取得了较好的效果,为弹性参数研究提供了数据保证。
然后,在求准纵波、横波后,结合地层密度、孔隙度、岩石体积等参数来计算杨氏模量、剪切模量、体积模量、泊松比等岩石物理弹性参数;对弹性参数之间两两交会,描述各种参数组合方式对岩性及流体的区分能力;分岩性、流体建立弹性敏感性参数识别图版及敏感性分析表。为今后新场构造沙溪庙组储层勘探开发工作提供了有力的技术支撑。
This article is based on State Plan for Development of Basic Research in Key Areas——The forcasting technology for elastic parameters of clastic reservoir by log welling in Sichuan basin. Considering the various reservoir characters of the Shaximiao Formation and the limitations of cooperative reservoir forcasting by log welling and seismic methods, we concluded the elastic parameter of different lithology and fluid parameter. Through our research, we have high lightened the sensitive elastic parameters of excellent reservoirs in amid intervals and achieved the plate and the table of lithology and fluid elastic sensitive parameter. Through the tight combination between seimic and log welling interpretation, we have arrived at a effective system for seismic and well cooperative inversion and well improved the reservoir forcasting technology.
First, we have both quantificationally and qualitatively studied the lithology and fluid nature of the reservoir based on the BP neural network simulation、normal probability function、optimization method、resistivity porosity cross plot,etc. All this work has provided us with a definitive subject for elastic parameter research.
In addition, our research area has no Shear Wave data, so we chose Castagna formula and VRH model as the preferable method for Shear Wave forcasting based on the results of previous researches. The model has been spread to all the wells in the area which has no shear wave data, and resulted in an excellent situation providing an appreciable criterion for the further study.
Finally, we achieved the Young's modulus、shear modulus、bulk modulus、Poisson ratio,etc using porosity, rock volume and density. Then, we mapped the crossplot of each two elastic parameters and described their ability to discriminate the fluid and the lithology achieving the plate and the table of lithology and fluid elastic sensitive parameter. All the work in this article has provided a technological support for the further exploration of Shaximiao formation in Xinchang Gas Field.
首先,在充分利用研究区内多种测井方法(放射性、电阻率、声学、地球化学等)的基础上,采用BP神经网络算法、正态概率分布法、电阻率与孔隙度交会法、最优化算法等方法,针对研究区从定性和定量两方面进行了岩性和含油气性评价研究,为岩石物理弹性参数的研究提供了准确的研究对象。
其次,在研究区沙溪庙组缺少横波测井资料的背景上,依据前人的研究成果,在分析本区实际情况后优选Castagna方法结合VRH模型作为横波预测的最优方法,并推广到本区无横波井,取得了较好的效果,为弹性参数研究提供了数据保证。
然后,在求准纵波、横波后,结合地层密度、孔隙度、岩石体积等参数来计算杨氏模量、剪切模量、体积模量、泊松比等岩石物理弹性参数;对弹性参数之间两两交会,描述各种参数组合方式对岩性及流体的区分能力;分岩性、流体建立弹性敏感性参数识别图版及敏感性分析表。为今后新场构造沙溪庙组储层勘探开发工作提供了有力的技术支撑。
This article is based on State Plan for Development of Basic Research in Key Areas——The forcasting technology for elastic parameters of clastic reservoir by log welling in Sichuan basin. Considering the various reservoir characters of the Shaximiao Formation and the limitations of cooperative reservoir forcasting by log welling and seismic methods, we concluded the elastic parameter of different lithology and fluid parameter. Through our research, we have high lightened the sensitive elastic parameters of excellent reservoirs in amid intervals and achieved the plate and the table of lithology and fluid elastic sensitive parameter. Through the tight combination between seimic and log welling interpretation, we have arrived at a effective system for seismic and well cooperative inversion and well improved the reservoir forcasting technology.
First, we have both quantificationally and qualitatively studied the lithology and fluid nature of the reservoir based on the BP neural network simulation、normal probability function、optimization method、resistivity porosity cross plot,etc. All this work has provided us with a definitive subject for elastic parameter research.
In addition, our research area has no Shear Wave data, so we chose Castagna formula and VRH model as the preferable method for Shear Wave forcasting based on the results of previous researches. The model has been spread to all the wells in the area which has no shear wave data, and resulted in an excellent situation providing an appreciable criterion for the further study.
Finally, we achieved the Young's modulus、shear modulus、bulk modulus、Poisson ratio,etc using porosity, rock volume and density. Then, we mapped the crossplot of each two elastic parameters and described their ability to discriminate the fluid and the lithology achieving the plate and the table of lithology and fluid elastic sensitive parameter. All the work in this article has provided a technological support for the further exploration of Shaximiao formation in Xinchang Gas Field.
引文
[1]《测井学》编写组.测井学.北京:石油工业出版社,2004:384～523.
[2]曾文冲.油气藏储集层测井评价技术[M].北京:石油工业出版社,1991.248～341.
[3]李瑞.裂缝性碳酸盐岩测井储层参数研究及应用[M].成都:四川科学技术出版社,2003.
[4]符晓等.四川盆地西部天然气资源与勘探开发.中国地质大学出版社2001.
[5]杨克明等.川西坳陷低孔渗碎屑岩含气领域勘探理论与实践.地质出版社2004.
[6]朱彤,叶军,王胜.川西坳陷新场气田成藏环境的划分及识别标志[J].石油实验地质,2001,23(2):174~177.
[7]朱彤,叶军.川西坳陷致密碎屑岩气藏类型划分及特征[J].石油实验地质,2004,26(6):537~541.
[8]金燕.人工神经网络在测井地质领域中的应用[J].天然气勘探与开发,1999,22(1):1~13
[9]陶厚鑫,陈立.神经网络控制及BP算法[J].技术与市场,2008,(1):43~44
[10]范训礼,戴航等.神经网络在岩性识别中的应用[J].测井技术,1999,23(1):50~52
[11]王辉,周承刚等.测井解释最优化方法中的误差和约束条件[J].中国煤田地质,2002,14(1):73~76
[12]周改英,赵喜亮.多矿物模型分析的最优化测井解释[J].河南石油,2005,19(5):21~22
[13]燕继成,文政等.基于多矿物模型分析的最优化测井解释在X油田中的应用研究[J].国外测井技术,2008,23(6):18~20
[14]马中高.碎屑岩地震岩石物理学特征研究[D],成都理工大学博士学位论文,2007
[15]王炳章.地震岩石物理学及其应用研究[D],成都理工大学博士学位论文,2008
[16]李维新,史謌等.岩石物理弹性参数规律研究[J].地球物理学进展,2007,22(5):1380~1385
[17]马中高,解吉高.岩石的纵、横波速度与密度的规律研究[J].地球物理学进展,2005,20(4):905~910.
[18]史謌,沈文略,杨东全.岩石物理弹性波速度和饱和度、孔隙流体分布的关系[J].地球物理学报,2003,46 (1):138~142.
[19]史謌,杨东全.岩石波速和孔隙度、泥质含量之间的关系研究[J].北京大学学报(自然科学版),2001,37 (3):379~384.
[20]陶果,岳文正,谢然红,等.岩石物理的理论模拟和数值实验新方法[J].地球物理学进展,2005,20(1):4~11.
[21]李瑞,肖崇礼,等.混合矿物体积模型优化计算复杂岩性储层参数[J].物探化探计算技术,1993,15(1),44~48.
[22]李瑞,邹玮等.川西坳陷须家河组测井气水解释模型及气层识别技术研究,2006.
[23]吴铭,刘成川等.新场气田蓬莱镇组和沙溪庙组气藏新增天然气探明储量报告,2002
[24]刘庆海,贺欣蔚等.利用岩石物性参数反演裂缝天然气富集区研究,2008
[25]葛瑞·马沃可,塔潘·木克基等,岩石物理手册-孔隙介质中地震分析工具[M].合肥:中国科技大学出版社,2008
[26]孙建国,岩石物理学基础[M].北京:地质出版社,2006
[27]印兴耀,韩文功等,地震技术新进展(上、下) [M].东营:中国石油大学出版社,2006
[28]邹玮,川西坳陷须家河组储层综合评价[D],成都理工大学硕士学位论文,2007
[29]王思权,川西XC构造须家河组测井储层综合评价研究[D],成都理工大学硕士学位论文,2009
[30]蒋涔,余瀚熠.岩石物理分析技术的应用[J].内蒙古石油化工, 2010(5):97～99.
[31] Schlumberger. Subsurface Structural and Stratigraphic Interpretation Methodology.1991.
[32] Serra O, Fundamentals of Well-Log Interpretation, Elsevier Publishers V.B,1984.
[33] Schlumberger. Sonic Waveform Analysis Shear Waves Velociy.Copyright, 1981.
[34] Fred J. Hilterman. Seismic Amplitude Interpretation[M],2001:Distinguished Instructor Short Course.
[35] Murphy W, Reischer A, Hsu K. Modulus decomposition of compressional and shear velocities in sand bodies[J]. Geo-physics,1993,58(2):227~239.
[36] SHEN F, TOKSOZ M N. Scattering characteristics inheterogeneous fractured reservoirs from waveform estimation[J].Geophys J Internat,2000,140:251～266.
[2]曾文冲.油气藏储集层测井评价技术[M].北京:石油工业出版社,1991.248～341.
[3]李瑞.裂缝性碳酸盐岩测井储层参数研究及应用[M].成都:四川科学技术出版社,2003.
[4]符晓等.四川盆地西部天然气资源与勘探开发.中国地质大学出版社2001.
[5]杨克明等.川西坳陷低孔渗碎屑岩含气领域勘探理论与实践.地质出版社2004.
[6]朱彤,叶军,王胜.川西坳陷新场气田成藏环境的划分及识别标志[J].石油实验地质,2001,23(2):174~177.
[7]朱彤,叶军.川西坳陷致密碎屑岩气藏类型划分及特征[J].石油实验地质,2004,26(6):537~541.
[8]金燕.人工神经网络在测井地质领域中的应用[J].天然气勘探与开发,1999,22(1):1~13
[9]陶厚鑫,陈立.神经网络控制及BP算法[J].技术与市场,2008,(1):43~44
[10]范训礼,戴航等.神经网络在岩性识别中的应用[J].测井技术,1999,23(1):50~52
[11]王辉,周承刚等.测井解释最优化方法中的误差和约束条件[J].中国煤田地质,2002,14(1):73~76
[12]周改英,赵喜亮.多矿物模型分析的最优化测井解释[J].河南石油,2005,19(5):21~22
[13]燕继成,文政等.基于多矿物模型分析的最优化测井解释在X油田中的应用研究[J].国外测井技术,2008,23(6):18~20
[14]马中高.碎屑岩地震岩石物理学特征研究[D],成都理工大学博士学位论文,2007
[15]王炳章.地震岩石物理学及其应用研究[D],成都理工大学博士学位论文,2008
[16]李维新,史謌等.岩石物理弹性参数规律研究[J].地球物理学进展,2007,22(5):1380~1385
[17]马中高,解吉高.岩石的纵、横波速度与密度的规律研究[J].地球物理学进展,2005,20(4):905~910.
[18]史謌,沈文略,杨东全.岩石物理弹性波速度和饱和度、孔隙流体分布的关系[J].地球物理学报,2003,46 (1):138~142.
[19]史謌,杨东全.岩石波速和孔隙度、泥质含量之间的关系研究[J].北京大学学报(自然科学版),2001,37 (3):379~384.
[20]陶果,岳文正,谢然红,等.岩石物理的理论模拟和数值实验新方法[J].地球物理学进展,2005,20(1):4~11.
[21]李瑞,肖崇礼,等.混合矿物体积模型优化计算复杂岩性储层参数[J].物探化探计算技术,1993,15(1),44~48.
[22]李瑞,邹玮等.川西坳陷须家河组测井气水解释模型及气层识别技术研究,2006.
[23]吴铭,刘成川等.新场气田蓬莱镇组和沙溪庙组气藏新增天然气探明储量报告,2002
[24]刘庆海,贺欣蔚等.利用岩石物性参数反演裂缝天然气富集区研究,2008
[25]葛瑞·马沃可,塔潘·木克基等,岩石物理手册-孔隙介质中地震分析工具[M].合肥:中国科技大学出版社,2008
[26]孙建国,岩石物理学基础[M].北京:地质出版社,2006
[27]印兴耀,韩文功等,地震技术新进展(上、下) [M].东营:中国石油大学出版社,2006
[28]邹玮,川西坳陷须家河组储层综合评价[D],成都理工大学硕士学位论文,2007
[29]王思权,川西XC构造须家河组测井储层综合评价研究[D],成都理工大学硕士学位论文,2009
[30]蒋涔,余瀚熠.岩石物理分析技术的应用[J].内蒙古石油化工, 2010(5):97～99.
[31] Schlumberger. Subsurface Structural and Stratigraphic Interpretation Methodology.1991.
[32] Serra O, Fundamentals of Well-Log Interpretation, Elsevier Publishers V.B,1984.
[33] Schlumberger. Sonic Waveform Analysis Shear Waves Velociy.Copyright, 1981.
[34] Fred J. Hilterman. Seismic Amplitude Interpretation[M],2001:Distinguished Instructor Short Course.
[35] Murphy W, Reischer A, Hsu K. Modulus decomposition of compressional and shear velocities in sand bodies[J]. Geo-physics,1993,58(2):227~239.
[36] SHEN F, TOKSOZ M N. Scattering characteristics inheterogeneous fractured reservoirs from waveform estimation[J].Geophys J Internat,2000,140:251～266.