南港油田板2油组深层致密砂岩裂缝预测及成因研究

doi:10.3969/j.issn.1672-9803.2022.04.020

摘要/Abstract

摘要： 为了实现南港油田沙一下亚段板2油组孔隙-裂缝双重介质油藏的高效开发,综合岩心、常规测井、三维地震资料开展裂缝预测及成因研究。通过常规测井曲线的裂缝响应特征,建立裂缝识别常数模型,评价裂缝纵向发育情况,同时利用地震多属性的人工神经网络方法,测井与地震数据相结合预测裂缝平面分布范围。预测结果表明,区域主断裂走向为NE向,同时伴生了多条NW、NE、EW向裂缝,主要分布在G 178井断裂和B 228井断裂之间的区域,裂缝的形成与分布明显受隐伏走滑断裂带的控制,双断裂斜交的伴生破碎带所形成的地垒式构造区域裂缝最为发育。综合裂缝发育情况、砂岩厚度以及产能评价结果,将储层划分为4类,其中：裂缝发育且厚度大于10 m的砂岩为Ⅰ类储层,试油无需压裂,自喷高产;裂缝不发育但厚度大于10 m的砂岩为Ⅱ类储层,试油具备一定产能,压裂后可高产。Ⅰ类、Ⅱ类储层发育区域是下步勘探开发的重点目标区。

关键词: 裂缝预测, 致密砂岩, 主控因素, 人工神经网络, 南港油田

Abstract: In order to realize the efficient development of the porous-fractured dual medium reservoir of Ban 2 Formation in Es1x of Nangang oilfield, the fracture prediction and genesis research were carried out based on core, conventional log and three-dimensional seismic data. Based on the fracture response characteristics of conventional logging curves, a fracture identification constant model was established to evaluate the longitudinal development of fractures. At the same time, the multi-attribute artificial neural network method was used to predict the plane distribution of fractures by combining well logging with seismic data. The prediction results show that the trend of the regional main fault is NE, accompanied by multiple NW, NE, and EW fractures, mainly in the area between well G178 fault and well B228 fault. The formation and distribution of fractures are obviously controlled by the burid strike-slip fault zone and the fractures are most developed in the horst-type structural area formed by the accompanying fracture zones of the double fault obliquely crossing. Based on fracture development, sandstone thickness and productivity evaluation results, the reservoirs can be divided into four types. Among them, the sandstone with fracture development and thickness greater than 10m is type I reservoir. No fracturing is required for oil testing, and high production is achieved through flowing. Sandstone with undeveloped fractures but thickness greater than 10 m is type Ⅱ reservoir. Oil testing has a certain production capacity, and high yield after fracturing. The development area of type I and type Ⅱ reservoirs is the key target area for the further exploration and development.

Key words: fracture prediction, tight sandstone, main controlling factors, artificial neural network, Nangang oilfield

中图分类号:

TE132.1

杨佩佩, 卢异, 周杨, 吴刚, 李文亮, 任光文. 南港油田板2油组深层致密砂岩裂缝预测及成因研究[J]. 录井工程, 2022, 33(4): 118-124.

YANG Peipei, LU Yi, ZHOU Yang, WU Gang, LI Wenliang, REN Guangwen. Prediction and genesis study of deep tight sandstone fractures in Ban 2 Formation of Nangang oilfield[J]. Mud Logging Engineering, 2022, 33(4): 118-124.

参考文献

[1] 李阳,薛兆杰,程喆,等. 中国深层油气勘探开发进展与发展方向[J].中国石油勘探,2020,25(1):46-57.
LI Yang, XUE Zhaoyang, CHENG Zhe, et al.Progress and developement directions of deep oil and gas exploration and development in China[J]. China Petroleum Exploration,2020,25(1):46-57.
[2] 黄捍东,朱筱敏,庞雄奇.复杂油气藏地震预测方法[M].北京:科学出版社,2017:208-278.
HUANG Handong, ZHU Xiaomin, PANG Xiongqi.Seismic prediction methodology for complex hydrocarbon reservoirs[M]. Beijing:Science Press,2017:208-278.
[3] 冯翠菊, 闫伟林.利用常规测井资料识别变质岩储层裂缝的方法探讨[J]. 国外测井技术,2008,23(2):14-16.
FENG Cuiju, YAN Weilin.Discussion on the method of identifying fractures in metamorphic rock reservoir with conventional logging data[J]. World Well Logging Technology,2008,23(2):14-16.
[4] 汤小燕,王兴元,朱永红.综合概率法评价火山岩裂缝发育程度[J]. 天然气勘探与开发,2009,32(1):26-27,38.
TANG Xiaoyan, WANG Xingyuan, ZHU Yonghong.Synthetic Probability Approach to evaluate the development degree of volcanic rock fractures[J]. Natural Gas Exploration and Development,2009,32(1):26-27,38.
[5] 高松洋. 测井资料在裂缝识别中的应用:以 H 地区砂岩储层为例[J]. 石油天然气学报(江汉石油学院学报),2009,31(2):272-274.
GAO Songyang.Application of logging data in fracture identification: A case study of sandstone reservoir in H area[J]. Journal of Oil and Gas Technology(J.JPI),2009,31(2):272-274.
[6] 景永奇,秦瑞宝. 利用裂缝指示曲线判别花岗岩潜山纵向裂缝发育带[J]. 测井技术,1999,23(1):38-42.
JING Yongqi, QIN Ruibao.Identifying the fracture distribution in granite buried hill with synthetic curve[J]. Well Logging Technology,1999,23(1):38-42.
[7] 杨凤丽,周祖翼,张善文,等. 利用地震方法预测潜山裂缝性油气储层:以渤海湾南部为例[J]. 高校地质学报,1999,5(3):322-327.
YANG Fengli, ZHOU Zuyi, ZHANG Shanwen, et al.Predicting fractured reservoirs in burial hill pool with seismic methods: A case study in southern Bohai Basin[J]. Geological Journal of China Universities,1999,5(3):322-327.
[8] 王永刚,李振春,刘礼农,等. 利用地震信息预测储层裂缝发育带[J]. 石油物探,2000,39(4):57-63.
WANG Yonggang, LI Zhenchun, LIU Linong, et al.Using seismic data to predict fractured zones of reservoir[J]. Geophysical Prospecting for Petroleum,2000,39(4):57-63.
[9] 陈崇河,马晓芬. 利用地震资料研究裂缝性储层的新进展[J]. 石油物探,1997,36(增刊1):20-26.
CHEN Chonghe, MA Xiaofen.New progress in the study of fractured reservoirs using seismic data[J]. Geophysical Prospecting for Petroleum,1997,36(S1):20-26.
[10] GRECHKA V,TSVANKIN I. 3-D description of normal moveout in anisotropic inhomogeneous media[J]. Geophysics,1998,63(3):1079-1092.
[11] 尹志军,黄述旺,陈崇河. 用三维地震资料预测裂缝[J]. 石油勘探与开发,1999,26(1):78-80.
YIN Zhijun, HUANG Shuwang, CHEN Chonghe.Fracture determination by means of three-dimensional seismic data[J]. Petroleum Exploration and Development,1999,26(1):78-80.
[12] TOKSÖZ M N, CHENG C H, TIMUR A. Velocities of seismic waves in porous rocks[J]. Geophysics,1976,41(4):621-645.
[13] FATTI J L, SMITH G C, VAIL P J,et al. Detection of gas in sandstone reservoirs using the AVO analysis:A 3-D seismic case history using Geostack technique[J]. Geophysics,1994,59(9):1362-1376.
[14] MCQUILLAN H. Small-scale fracture density in Asmari Formation of southwest Iran and its relation to bed thickness and structural settings[J]. AAPG Bulletin, 1973,47(12):2367-2385.
[15] 胡光岷,贺振华,黄德济,等.利用纵波资料反演裂缝发育密度和方向[J]. 成都理工学院学报,2000,27(2):145-150.
HU Guangmin, HE Zhenhua, HUANG Deji, et al.Inversion of fracture density and azimuth using P-wave seismic data[J]. Journal of Chengdu University of Technology,2000,27(2):145-150.
[16] 李军,郝天珧,赵百民. 地震与测井数据综合预测裂缝发育带[J]. 地球物理学进展,2006,21(1):179-183.
LI Jun, HAO Tianyao,ZHAO Baimin.Synthetic predication of favorable fracture zone from seismic and log data[J]. Progress in Geophysics,2006,21(1):179-183.
[17] 艾能平,任建业,祁鹏,等.歧口凹陷伸展滑脱构造系统的厘定及其地质意义[J]. 大地构造与成矿学,2009,33(3):343-351.
AI Nengping, REN Jianye, QI Peng, et al.The redefinition and geological significance of extensional decollement structure systems in the Qikou depression[J]. Geotectonica et Metallogenia,2009,33(3):343-351.
[18] 肖敦清,王桂芝,韦阿娟,等.黄骅坳陷火成岩成藏特征研究[J]. 特种油气藏,2003,10(1):59-61.
XIAO Dunqing, WANG Guizhi, WEI Ajuan, et al.Study on igneous reservoir forming conditions and distribution in Huanghua depression[J].Special Oil & Gas Reservoirs,2003,10(1):59-61.
[19] 王文革,张志攀,卢异,等.黄骅坳陷歧口凹陷新生代走滑断裂特征及控盆作用[J]. 天然气地质学,2012,23(4): 713-719.
WANG Wenge, ZHANG Zhipan, LU Yi, et al.Characteristics and its role in basin controlling of strike-slip faults in Cenozoic Qikou sag, Huanghua depression[J]. Natural Gas Geoscience,2012,23(4):713-719.
[20] 石双虎,何樵登,滕吉文,等.利用多元地震属性预测测井特性[J]. 西北地震学报,2006,28(4):309-313.
SHI Shuanghu, HE Qiaodeng, TENG Jiwen, et al.Using multi-attribute to predict log properties from seismic data[J].Northwestern Seismological Journal,2006,28(4): 309-313.
[21] MOON S, LEE G H, KIM H,et al. Collocated cokriging and neural-network multi-attribute transform in the prediction of effective porosity: A comparative case study for the Second Wall Creek Sand of the Teapot Dome field, Wyoming, USA[J]. Journal of Applied Geophysics,2016,131:69-83.
[22] ITURRARÁN-VIVEROS U, PARRA J O. Artificial Neural Networks applied to estimate permeability, porosity and intrinsic attenuation using seismic attributes and well-log data[J]. Journal of Applied Geophysics, 2014,107:45-54.
[23] YASIN Q,DING Y, BAKLOUTI S, et al. An integrated fracture parameter prediction and characterization method in deeply-buried carbonate reservoirs based on deep neural network[J]. Journal of Petroleum Science and Engineering, 2021,208(9):1-21.

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