1 Research Institute of Yanchang Petroleum (Group) Co., Ltd., Xi’an 710065, China
2 State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University, Chengdu 610500, China
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文章历史+
Received
Published
Revised
2025-11-03
2026-10-18
2026-05-09
Online Date
2026-10-23
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摘要
可压性评价是页岩油储层优化压裂设计的核心前提。针对延长油田小庄科区块长7段储层非均质性强、改造效果受地质、工程多因素制约的难题,提出基于多准则优化的双甜点可压性综合评价方法。通过降维处理测井数据并结合室内实验,进行地质参数与工程参数差异化优选,建立夹层型、纹层型、天然裂缝型三类储层甜点指标体系,并基于层次分析(analytic hierarchy process, AHP)-多准则妥协解排序法(vlsekriterijumska optimizacija i kompromisno resenje, VIKOR)构建权重模型,最后利用核密度估计法对可压性结果分级分类。实际应用表明,该模型可精准量化储层可压性等级,指导压裂靶体优选,为复杂页岩油储层高效开发提供理论支撑。
Abstract
Fracability evaluation is regarded as the core prerequisite for optimizing fracturing design in shale oil reservoirs. The Chang 7 Member reservoir in the Xiaozhuangke block of Yanchang Oilfield exhibits strong heterogeneity. Its stimulation effectiveness is constrained by multiple geological and engineering factors. A comprehensive evaluation method for dual-sweet-spot fracability based on multi-criteria optimization was proposed to address these challenges. Dimensionality reduction of logging data was performed. Laboratory experiments were conducted. Geological and engineering parameters were differentially optimized. Three types of reservoir sweet spot indicator systems were established, namely interbedded, laminated, and natural fracture types. A weight model was constructed based on the analytic hierarchy process(AHP)-vlsekriterijumska optimizacija i kompromisno resenje(VIKOR). Finally, the fracability results were classified and categorized using kernel density estimation. Practical application shows that this model can accurately quantify the fracability grade of reservoirs. It guides the optimization of fracturing targets. It provides theoretical support for the efficient development of complex shale oil reservoirs.
MU Jing-fu, SUN Xiao, GUO Xing, ZHANG Jun-tao, LU Cong, SUN Ji-hong.
Multi-criteria Optimization-based Comprehensive Evaluation Method for Shale Oil Sweet Spots in the Chang 7 Member of Yanchang Oilfield[J].
Science Technology and Engineering, 2026, 26(28): 12157-12167 DOI:10.12404/j.issn.1671-1815.2507617
ShiCan, LinBotao, XieBobo, et al. Evaluation method of shale reservoir fracability based on double sweet spots[J]. Journal of Shenzhen University (Science and Engineering), 2024, 41(2): 183-191.
LiaoQing. Effective source rocks in the Chang 7 Member of the Ordos Basin and their oil control effects[D]. Beijing: China University of Petroleum (Beijing), 2019.
JiangHongfeng, LiuBing, GaoYongde, et al. Geological characteristics and fracability evaluation of shale reservoir in Liushagang Formation, Weixinan Depression[J]. Science Technology and Engineering, 2024, 24(15): 6241-6253.
WangAnlong, SunXiaoqin, LiXiaolong, et al. Logging evaluation method for shale gas reservoir in Wufeng-Longmaxi Formation, Nanchuan block[J]. Science Technology and Engineering, 2020, 20(34): 14046-14052.
WanYouyu, WangXiaoqiong, LinHai, et al. Experimental study on sweet spot evaluation of shale oil reservoir in Yingxiongling area[J]. Science Technology and Engineering, 2025, 25(11): 4496-4504.
SuiLili, YangYongming, YangWenguang, et al. Comprehensive evaluation of shale fracability in Dongying subsidence zone of Shengli Oilfield[J]. Journal of China Coal Society, 2015(7): 1588-1594.
LiQuanhui, XiaoHui, LiangChaoyang, et al. Study on compressibility evaluation of shale oil reservoir in Fu2 section[J]. Unconventional Oil & Gas, 2023, 10(5): 134-144.
CaoJiangjun, WangXi, WangLiuwei, et al. Characteristics and main controlling factors ofinterbedded shale oil reservoirs of Triassic Chang 7 Member in Heshui Area, Ordos Basin[J]. Lithologic Reservoirs, 2024, 36(3): 158-171.
NiuXiaobing, LüChengfu, FengShengbin, et al. Lamina combination characteristics and differential shale oil enrichment mechanisms of continental organic-rich shale: a case study of Triassic Yanchang Formation Chang 73 sub-member, Ordos Basin, NW China[J]. Petroleum Exploration and Development, 2025, 52(2): 279-291.
LiJiacheng, ZouJiawei, TianGang, et al. Shale oil fracturing scheme optimization based on geology-engineering integrated fracturing model: taking Lucaogou Formation, Jimsar Sag as an example[J]. Fault-Block Oil & Gas Field, 2025, 32(1): 35-46.
DengXue, LiJiaming, ZengHaojian, et al. Analysis and application research on weight calculation methods in the analytic hierarchy process[J]. Mathematics in Practice and Theory, 2012(7): 93-100.
LiRunsen, HouBing, ZhouChangjing, et al. Mechanical mechanism of fractures controlling fracturing in sandstone-mudstone thin interbedded reservoirs and criteria for layer penetration[J]. China Offshore Oil and Gas, 2025, 37(1): 156-166.
[29]
马通. 永川地区龙马溪组页岩可压性综合评价研究[D]. 成都: 成都理工大学, 2023.
[30]
MaTong. Comprehensive evaluation of shale reservoir fracability inthe Longmaxi Formation of Yongchuan Area[D]. Chengdu: Chengdu University of Technology, 2023.
LiWei, WangMin, XiaoDianshi, et al. Development characte-ristics and intelligent identification method of natural fractures: a case study of the Upper Triassic Xujiahe Formation in the western Sichuan Depression, Sichuan Basin[J]. Petroleum Reservoir Evaluation and Development, 2025(3): 443-454.
LiuDongdong, YangDongxu, ZhangZiya, et al. Fracture identification for tight reservoirs by conventional and imaging logging: a case study of Permian Lucaogou Formation in Jimsar Sag, Junggar Basin[J]. Lithologic Reservoirs, 2019, 31(3): 76-85.
[35]
ShouW, GuptaS, KhalilS. Kernel density estimation of a sensitive variable in the presence of auxiliary information[J]. Communications in Statistics-Theory and Methods, 2024, 54(14): 4509-4516.
[36]
WanZ, WangS, WuZ, et al. Dimension-independent single-loop Monte Carlo simulation method for estimate of Sobol’ indices in variance-based sensitivity analysis[J]. Reliability Engineering & System Safety, 2025, 263: 111236.
[37]
ThomasB, NicholasT, AnnieF, et al. KEVS: enhancing segmentation of visceral adipose tissue in pre-cystectomy CT with Gaussian kernel density estimation[J]. International Journal of Computer Assisted Radiology and Surgery, 2025, 20(7): 1335-1344.