2024 Vol. 40, No. 4
Article Contents

QIN Dewen, ZHANG Yan, YU Jie. “Sweet spot” prediction technique for mid-deep low permeability gas reservoirs in M Structure of East China Sea[J]. Marine Geology Frontiers, 2024, 40(4): 83-91. doi: 10.16028/j.1009-2722.2023.138
Citation: QIN Dewen, ZHANG Yan, YU Jie. “Sweet spot” prediction technique for mid-deep low permeability gas reservoirs in M Structure of East China Sea[J]. Marine Geology Frontiers, 2024, 40(4): 83-91. doi: 10.16028/j.1009-2722.2023.138

“Sweet spot” prediction technique for mid-deep low permeability gas reservoirs in M Structure of East China Sea

  • In recent years, structural-lithological complex reservoirs have gradually become the key in reservoir expansion and production in the East China Sea. The low permeability gas reservoir in the middle and deep low-permeability gas reservoir in the M Structure of the East China Sea was studied. The study area has large burial depth, strong internal heterogeneity, and complex porosity-permeability relationship, but little difference in geophysical response characteristics. It is urgent to study the fine characterization of sweet spot reservoir in the reservoir. Seismic rock physics were analyzed, in which the Young’s impedance was used to distinguish clastic rock reservoir from non-reservoir. By classifying and optimizing parameters in seismological profiling, shear modulus was found and used as a comprehensive sensitive elastic factor, and combined with a high sensitivity hydrocarbon detection factor, clear clean, coarse-grained, and high-permeability high quality reservoirs could be detected. In addition, to reduce the influence of rock skeleton porosity, a highly sensitive fluid factor was used to detect hydrocarbons. Finally, combined with lithology and attributes of hydrocarbon detection, good sweet spot reservoir areas were finely characterized. Results show that the sweet spot prediction using this method reached a high successful prediction rate of 86.07%, which provided an important basis for well deployment and trajectory optimization and a reference for working on similar blocks.

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  • [1] 谢玉洪. 中国海油“十三五”油气勘探重大成果与“十四五”前景展望[J]. 中国石油勘探,2021,26(1):43-54.

    Google Scholar

    [2] 杨勤勇,杨江峰,王咸彬,等. 中国石化物探技术新进展及发展方向思考[J]. 中国石油勘探,2021,26(1):121-130.

    Google Scholar

    [3] OSTRANDER W J. Plane-wave reflection coefficients for gas sands at nonnormal angles of incidence[J]. Geophysics,1984,49:1637-1648. doi: 10.1190/1.1441571

    CrossRef Google Scholar

    [4] CHIBURIS E F. Analysis of amplitude versus offset to detected gas/oil contacts in the Arabian Gulf[C]//54th Ann. Internat. Mtg., Soc. Expl. Geophys., Expanded Abstracts, 1984: 669-670.

    Google Scholar

    [5] SMITH G C,GIDLOW P M. Weighted stacking for rock property estimation and detection of gas[J]. Geophysical Prospecting,1987,35(9):993-1014. doi: 10.1111/j.1365-2478.1987.tb00856.x

    CrossRef Google Scholar

    [6] GOODWAY B, CHEN T, DOWNTON J. Improved AVO fluid detection and lithology discrimination using Lame petrophysical parameters[C]. SEG Technical Program Expanded Abstracts, 1997, 16: 183-186.

    Google Scholar

    [7] HEDLIN K, Pore space modulus and extraction using AVO[C]. SEG Technical Program Expanded Abstracts, 2000, 19: 170-173.

    Google Scholar

    [8] BATZLE M L. Optimal hydrocarbon indicators[J]. SEG Technical Program Expanded Abstracts,2001,20:1697-1700.

    Google Scholar

    [9] RUSSELL B H,Hedlin K,Hilterman F J,et al. Fluid-property discrimination with AVO:a Biot-Gassmann perspective[J]. Geophysics,2003,68(1):29-39. doi: 10.1190/1.1543192

    CrossRef Google Scholar

    [10] MARK Q,BRUCE S,CHRIS T. Poisson impedance[J]. The Leading Edge,2006,45(3):239-242.

    Google Scholar

    [11] 王栋,何振华,黄德济. 新流体识别因子的构建与应用分析[J]. 石油物探,2009,48(2):141-148.

    Google Scholar

    [12] 印兴耀,张世鑫,张锋. 针对深层流体识别的两项弹性阻抗反演与Russell流体因子直接估算方法研究[J]. 地球物理学报,2013,56(7):2378-2390.

    Google Scholar

    [13] 刘力辉,李建海,刘玉霞. 地震物相分析方法与“甜点”预测[J]. 石油物探,2013,52(4):432-437.

    Google Scholar

    [14] 许翠霞,马鹏善,赖令彬,等. 致密砂岩含气性敏感参数:以松辽盆地英台气田营城组为例[J]. 石油勘探与开发,2014,41(6):712-716.

    Google Scholar

    [15] 李春宁. 多波联合AVA属性提取与油气预测[D]. 青岛: 中国石油大学(华东), 2014.

    Google Scholar

    [16] 陈祖庆,杨鸿飞,王静波. 基于叠前反映的致密砂岩含气储层识别技术研究[J]. 天然气技术与经济,2015,9(4):18-22.

    Google Scholar

    [17] 徐玥,张林清. 利用Russell流体因子进行致密砂岩气“甜点”预测[J]. 西部探矿工程,2016,12(2):20-23.

    Google Scholar

    [18] 张林清,张会星,姜效典,等. 弹性参数反演与属性融合技术在“甜点”预测中的应用[J]. 天然气地球科学,2017,28(4):582-589.

    Google Scholar

    [19] 曹冰,秦德文,陈践发. 西湖凹陷低渗储层“甜点”预测关键技术研究与应用:以黄岩A气田为例[J]. 石油学报,2018,36(1):188-197.

    Google Scholar

    [20] 李久娣. 东海西湖N区块致密砂岩气藏甜点预测研究[J]. 石油物探,2019,58(3):444-452. doi: 10.3969/j.issn.1000-1441.2019.03.014

    CrossRef Google Scholar

    [21] 王迪,张益明,刘志斌,等. AVO定量解释模板在LX地区致密气“甜点”预测中的应用[J]. 石油物探,2020,59(6):936-948.

    Google Scholar

    [22] 韩刚,高红艳,龙凡,等. 叠前反演在西湖凹陷致密砂岩储层“甜点”预测中的应用[J]. 石油物探,2021,60(3):471-478.

    Google Scholar

    [23] 张岩,秦德文. 东海古近系致密碎屑岩“甜点”地震预测方法及应用[J]. 海洋地质前沿,2023,39(5):51-58.

    Google Scholar

    [24] 尤丽,张迎朝,李才,等. 基于沉积成岩-储集相分析确定文昌9区低渗储层“甜点”分布[J]. 吉林大学学报,2014,44(5):1432-1440.

    Google Scholar

    [25] RUSSELL B H,GRAY D,HAMPSON D P,et al. Linearized AVO and poroelasticity[J]. Geophysics,2011,76(1):19-29.

    Google Scholar

    [26] 张岩,李键,侯志强. 基于叠前弹性信息直接提取的高灵敏烃类检测方法[J]. 地球物理学进展,2021,36(3):1187-1195.

    Google Scholar

    [27] 李春宁, 杜启振, 陈刚. 一种新的流体指示因子[C]//中国地球物理学会第二十九届年会, 2013.

    Google Scholar

    [28] 刘力辉,杨晓,丁燕. 基于岩性预测的CRP道集优化处理[J]. 石油物探,2013,52(5):482-488.

    Google Scholar

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