中基性火山岩多种叠前反演算法对比、优选及应用——以查干花地区火石岭组为例

doi:10.13809/j.cnki.cn32-1825/te.2024.02.006

Abstract

Abstract:

Currently, the most widely used pre-stack simultaneous inversion algorithm is based on the Zoeppritz equation approximation of isotropic horizontal media. This method, however, encounters challenges in medium-basic volcanic reservoirs characterized by rapid lateral lithological changes and vertical multi-period stacking. In such environments, tuffs and sedimentary tuffs exhibit similar logging and geophysical responses, complicating the distinction of lithology and physical properties of volcanic rocks through pre-stack simultaneous inversion. To address these challenges, qualitative analyses were conducted on the seismic response characteristics of volcanic reservoirs using convolutional model forward modeling. Additionally, well rock physics was employed for physical analyses of lithology and sensitive parameters pertinent to volcanic reservoirs. Comparative analyses of six binomial and trinomial pre-stack inverse algorithms, aimed at approximating the Zoeppritz equation in the specific area, were conducted through model calculations and actual data assessments. Based on these comprehensive analyses, the approximate algorithms developed by SMITH & GIDLOW and FATTI were identified as preferable choices. These algorithms use the reflection coefficients of P-wave impedance, S-wave impedance, and density as inputs for pre-stack inversion. The inversion results for P-wave impedance were utilized to predict the presence of local tuff, while the inversion outcomes for density were employed to ascertain the effective reservoir physical properties. The efficacy of this approach was validated through the deployment of an evaluation well and a horizontal well, which yielded predicted compliance rates of 76.0% and 84.6%, respectively.

Key words: pre-stack inversion, volcanic rock, seismic response, rock petrophysic, petrophysical property prediction

CLC Number:

TE122

LI Ruilei,CAO Lei,FAN Xuepei,FENG Xiaohui,LI Ning. Comparison, optimization and application of multiple prestack inversion algorithm for intermediate basic volcanic reservoirs: A case study of Huoshiling Formation in Chaganhua area[J].Petroleum Reservoir Evaluation and Development, 2024, 14(2): 207-215.

Figures/Tables 10

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Table 1

Corresponding parameters of inversion algorithm"

序号	反演算法	R₁	R₂	R₃	a	b	c
1	AKI-RICHARDS	$R V P$	$R V S$	R_ρ	$s e c 2 θ$	$- 8 K s i n 2 θ$	$1 - 4 K s i n 2 θ$
2	Amendment by WIGGINS	R_AI	R_GI	$R V P$	1	$s i n 2 θ$	$s i n 2 θ t a n 2 θ$
3	SMITH & GIDLOW, FATTI	R_AI	R_SI	R_ρ	$s e c 2 θ$	$- 8 K s i n 2 θ$	$4 K s i n 2 θ - t a n 2 θ$
4	GRAY Lambda	R_λ	R_μ	R_ρ	$(12 - K) s e c 2 θ$	$K (s e c 2 θ - 4 s i n 2 θ)$	$1 - 12 s e c 2 θ$
5	GRAY Bulk	R_κ	R_μ	R_ρ	$(12 - 23 K) s e c 2 θ$	$K (23 s e c 2 θ - 4 s i n 2 θ)$	$1 - 12 s e c 2 θ$
6	Modified SHUEY	R_AI	$R V P / V S$	R_ρ	$s e c 2 θ - 8 K s i n 2 θ$	$8 K s i n 2 θ$	$4 K s i n 2 θ - t a n 2 θ$

Table 1

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References 20

[1]	KOEFOED O. Reflection and transmission coefficients for plane longitudinal incident waves[J]. Geophysical Prospecting, 1962, 10(3): 304-351. doi: 10.1111/gpr.1962.10.issue-3
[2]	HILTERMAN F J. Amplitudes of seismic waves: A quick look[J]. Geophysics, 1975, 40(5): 745-762. doi: 10.1190/1.1440565
[3]	宗兆云, 印兴耀, 张繁昌. 基于弹性阻抗贝叶斯反演的拉梅参数提取方法研究[J]. 石油地球物理勘探, 2011, 46(4): 598-604.
	ZONG Zhaoyun, YIN Xingyao, ZHANG Fanchang. Elastic impedance Bayesian inversion for lame parameters extracting[J]. Oil Geophysical Prospecting, 2011, 46(4): 598-604.
[4]	AKI K, RICHARDS P G. Quantitative seismology, theory and methods volume Ⅰ and volume Ⅱ[J]. The Journal of the Acoustical Society of America, 1980, 68(5): 1546.
[5]	徐天吉, 程冰洁, 唐建明, 等. PP波与P-SV波叠前联合反演研究与应用[J]. 勘探地球物理进展, 2008, 31(5): 368-372.
	XU Tianji, CHENG Bingjie, TANG Jianming, et al. Prestack PP and P-SV wave joint inversion and its application[J]. Progress in Exploration Geophysics, 2008, 31(5): 368-372.
[6]	SHUEY R T. A simplification of the Zoeppritz equations[J]. Geophysics, 1985, 50(4): 609-614. doi: 10.1190/1.1441936
[7]	GRAY D. Elastic inversion for LAMÉ parameters[C]. 72nd Annual International Meeting, SEG, Expanded Abstracts, 2002, 12(7): 213-216.
[8]	王璞珺, 迟元林, 刘万洙, 等. 松辽盆地火山岩相:类型、特征和储层意义[J]. 吉林大学学报(地球科学版), 2003, 33(4): 449-456.
	WANG Pujun, CHI Yuanlin, LIU Wanzhu, et al. Volcanic facies of the Songliao basin: classification characteristics and reservoir significance[J]. Journal of Jilin University(Earth Science Edition), 2003, 33(4): 449-456.
[9]	曹磊, 蔡峰. 基于地震属性融合技术的火山通道识别研究[J]. 地球物理学进展, 2021, 36(2): 559-564.
	CAO Lei, CAI Feng. Recognition research of volcanic conduit based on seismic attribute fusion technology[J]. Progress in Geophysics, 2021, 36(2): 559-564.
[10]	李勇, 王孟华, 祖志勇, 等. 一种适用于溢流相火山岩有效储层地震预测方法——以乌兰花凹陷溢流相安山岩为例[J]. 工程地球物理学报, 2020, 17(2): 145-153.
	LI Yong, WANG Menghua, ZU Zhiyong, et al. Application of seismic technology in overflow facies volcanic effective reservoir prediction——A case study of overflow facies andesite reservoir in Wulanhua Sag[J]. Chinese Journal of Engineering Geophysics, 2020, 17(2): 145-153.
[11]	刘成川, 王勇飞, 毕有益. 中江气田窄河道致密砂岩气藏高效开发技术[J]. 油气藏评价与开发, 2022, 12(2): 345-355.
	LIU Chengchuan, WANG Yongfei, BI Youyi. Efficient development technique of tight sandstone gas reservoir in narrow channel of Zhongjiang Gas Field[J]. Petroleum Reservoir Evaluation and Development, 2022, 12(2): 345-355.
[12]	周红建. 查干花次凹油气成藏主控因素及有利勘探方向[J]. 中国石油和化工标准与质量, 2020, 40(1): 114-116.
	ZHOU Hongjian. Main controlling factors of hydrocarbon accumulation and favorable exploration direction in Chaganhua subsag[J]. China Petroleum and Chemical Standards and Quality, 2020, 40(1): 114-116.
[13]	刘哲, 单玄龙, 衣健, 等. 松辽盆地长岭断陷火山岩储层对天然气成藏的控制作用[J]. 世界地质, 2017, 36(3): 880-888.
	LIU Zhe, SHAN Xuanlong, YI Jian, et al. Controlling effects on gas accumulation of volcanic reservoirs of Changling fault depression in Songliao Basin[J]. World Geology, 2017, 36(3): 880-888.
[14]	高帅, 巩磊, 刘小波, 等. 松辽盆地北部深层致密火山岩气藏天然裂缝分布特征及控制因素[J]. 石油与天然气地质, 2020, 41(3): 503-512.
	GAO Shuai, GONG Lei, LIU Xiaobo, et al. Distribution and controlling factors of natural fractures in deep tight volcanic gas reservoirs in Xujiaweizi area, Northern Songliao Basin[J]. Oil & Gas Geology, 2020, 41(3): 503-512.
[15]	钟高明, 赵向原, 石磊, 等. 松南长岭断陷查干花次凹火石岭组火山碎屑岩储层特征及主控因素[J]. 天然气地球科学, 2024, 35(1):84-95.
	ZHONG Gaoming, ZHAO Xiangyuan, SHI Lei, et al. Characteristics and main controlling factors of volcanic clastic rock reservoir of Huoshiling Formation in Chaganhua sub-sag, Changling Fault Depression of southern Songliao Basin[J]. Natural Gas Geoscience, 2024, 35(1): 84-95.
[16]	李瑞磊, 杨立英, 朱建峰, 等. 松辽盆地南部断陷层火山岩储层特征及油气成藏主控因素[J]. 地学前缘, 2023, 30(4): 100-111. doi: 10.13745/j.esf.sf.2022.10.24
	LI Ruilei, YANG Liying, ZHU Jianfeng, et al. Volcanic reservoir characteristics and hydrocarbon accumulation control factors of rift depressions in southern Songliao Basin[J]. Earth Science Frontiers, 2023, 30(4): 100-111. doi: 10.13745/j.esf.sf.2022.10.24
[17]	谷文龙, 牛花朋, 张关龙, 等. 乌伦古地区石炭系火山岩成岩作用及其对储层发育的影响[J]. 地学前缘, 2023, 30(2):296-305. doi: 10.13745/j.esf.sf.2022.2.65
	GU Wenlong, NIU Huapeng, ZHANG Guanlong, et al. Diagenesis of Carboniferous volcanic rocks in Wulungu area and its implications for the volcanic reservoir development[J]. Earth Science Frontiers, 2023, 30(2): 296-305. doi: 10.13745/j.esf.sf.2022.2.65
[18]	王乃军, 罗静兰, 刘华清, 等. 岐口凹陷沙河街组火山岩成岩作用及对储集性能的控制[J]. 地球学报, 2012, 33(3): 360-370.
	WANG Naijun, LUO Jinglan, LIU Huaqing, et al. Diagenesis of volcanic rocks in Shahejie Formation of Qikou Depression and its control over reservoir performance[J]. Acta Geoscientica Sinica, 2012, 33(3): 360-370.
[19]	赵容容, 张宇, 周星合, 等. 基于褶积地震正演技术在碳酸盐岩储层预测中的应用——以川中高磨地区灯影组为例[J]. 物探化探计算技术, 2022, 44(4): 442-451.
	ZHAO Rongrong, ZHANG Yu, ZHOU Xinghe, et al. The seismic modeling method based convolution using in the reservoir predicting of carbonate rock: Using the Gao-Mo block Dengying formation as example[J]. Computing Techniques for Geophysical and Geochemical Exploration, 2022, 44(4): 442-451.
[20]	赵虎, 尹成, 鲍祥生, 等. 不同正演方法对地震属性的影响[J]. 石油地球物理勘探, 2013, 48(5): 729-733.
	ZHAO Hu, YIN Cheng, BAO Xiangsheng, et al. Influence of different forward modeling methods on seismic attributes[J]. Oil Geophysical Prospecting, 2013, 48(5): 729-733.

Comparison, optimization and application of multiple prestack inversion algorithm for intermediate basic volcanic reservoirs: A case study of Huoshiling Formation in Chaganhua area

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