基于岩石物理相的深层页岩气地质—工程甜点参数测井评价方法——以四川盆地LZ区块五峰组—龙马溪组为例

doi:10.13810/j.cnki.issn.1000-7210.2023.01.023

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摘要海相深层页岩气(埋深大于3500 m)储层受沉积、成岩、构造及生物等多种作用影响，甜点成因机制十分复杂，通常的沉积微相、岩相划分难以精细刻画其强非均质性，进一步加大了地质—工程甜点参数测井精细评价的难度。为此，以川南LZ区块页岩气储层为例，在明确海相页岩岩石物理相内涵、影响因素及核心指标的基础上，利用岩石薄片、全岩衍射以及TOC等资料，结合测井响应特征，划分出三类岩石物理相(Ⅰ、Ⅱ、Ⅲ)，进一步分为6个亚类(Ⅰ₁、Ⅰ₂、Ⅰ₃、Ⅱ₁、Ⅱ₂、Ⅲ)，明确了不同类型的特征及优势相，并采用随机森林算法建立了连续测井剖面岩石物理相识别方法和基于岩石物理相分类的地质—工程甜点参数测井精细计算模型。结果表明：①发育在五峰组顶部和龙一₁¹—龙一₁³小层内的Ⅰ₁和龙一₁⁴小层内的Ⅱ₁为有利相，通常具有TOC高、孔隙度大、含气性好以及脆性强的特征；②基于随机森林分类算法的测井岩石物理相识别效果优于常规测井交会图识别法，准确率可达90%以上；③分岩石物理相的随机森林回归模型地质—工程甜点参数计算精度高，各项参数计算结果与实测结果的相关系数均超过0.9。所提方法有效解决了海相深层页岩气储层地质—工程甜点参数求取困难的难题，实现了甜点参数的井剖面精确计算，为评层选区、水平井靶体优选以及资源量估算等奠定了基础。

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	张少龙
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	郭伟
	钟光海
	黄毅
	李志鹏

关键词 ：深层页岩气, 岩石物理相, 甜点参数, 机器学习, 测井识别, 水平井靶体优选

Abstract：Marine deep shale gas (with a burial depth of greater than 3500 m) reservoirs are affected by deposition, diagenesis, tectonism, biological processes, and other factors, and the formation mechanism of their sweet spots is very complicated. As a result, conventional divisions of sedimentary microfacies and lithofacies are difficult to carefully describe their strong heterogeneity,which further increases the difficulty in the fine logging evaluation of geological-engineering sweet spot parameters.Therefore, this paper takes the shale gas reservoir in LZ block in the south of Sichuan Basin as an example and analyzes the connotation, factors,and core indicators of petrophysical facies of marine shales.Then,with rock thin sections, whole-rock diffraction,total organic carbon (TOC) data,and logging response characteristics,the paper obtains three types of petrophysical facies(Ⅰ,Ⅱ,and Ⅲ) and further divides them into six subcategories (Ⅰ₁,Ⅰ₂,Ⅰ₃,Ⅱ₁,Ⅱ₂,and Ⅲ),so as to explore their characteristics and dominant facies. In addition, the random forest algorithm is used to develop a method for identifying petrophysical facies in continuous logging profiles and establish a fine logging calculation model of geological-engineering sweet spot parameters based on different types of petrophysical facies.The results indicate that:1Ⅰ₁ deve-loped at the top of Wufeng Formation and the first and third sub-members of the first member of Longmaxi Formation, namely,l₁¹-l₁³,and Ⅱ₁ developed at the fourth sub-member of the first member of Longmaxi Formation ①₁⁴ are favorable facies, and they usually feature high TOC, large porosity, rich gas content, and strong brittleness.②The logging identification results of petrophysical facies based on the random forest classification algorithm are better than that of conventional logging crossplot identification methods, and the accuracy rate is above 90%.③The calculation accuracy of geological-engineering sweet spot parameters based on a random forest regression model with classified petrophysical facies is high, and the correlation coefficient of calculated and measured parameters all exceeds 0.9. The proposed method effectively solves the difficulty in obtaining geological-engineering sweet spot parameters of marine deep shale gas reservoirs, realizes the accurate calculation of well profiles of sweet spots, and lays a foundation for selecting favorable layers and optimal targets of horizontal wells and estimating resource amounts.

Key words： deep shale gas petrophysical facies sweet spot parameters machine learning logging identification target optimization of horizontal well

收稿日期: 2022-01-18

PACS:

P631

基金资助:本项研究受中国石油—西南石油大学创新联合体科技合作项目“川南深层与昭通中浅层海相页岩气规模效益开发关键技术研究”(2020CX020000)、中石油科技部“十四·五”重大专项“川南海相深层页岩气资源潜力及富集规律研究”(2021DJ1901)、高等学校学科创新引智计划(111计划)“深层海相页岩气高效开发学科创新引智基地”(D18016)、四川省自然科学基金项目“页岩气储层低电阻率成因机制及对含气性的影响研究”(2022NSFSC0287)和南充市市校科技战略合作项目“基于海森堡自旋系统的量子调控研究”(SXHZ017)联合资助。

作者简介: 张少龙博士研究生，1994年生；2016年获西安石油大学勘查技术与工程专业学士学位，2019年获西南石油大学地质工程专业硕士学位；目前在同济大学海洋与地球科学学院攻读海洋科学专业博士学位；主要研究方向为测井地质学、页岩油气地质—工程一体化评价。

引用本文:

张少龙, 闫建平, 郭伟, 钟光海, 黄毅, 李志鹏. 基于岩石物理相的深层页岩气地质—工程甜点参数测井评价方法——以四川盆地LZ区块五峰组—龙马溪组为例[J]. 石油地球物理勘探, 2023, 58(1): 214-227. ZHANG Shaolong, YAN Jianping, GUO Wei, ZHONG Guanghai, HUANG Yi, LI Zhipeng. Logging evaluation method of geological-engineering sweet spot parameters for deep shale gas based on petrophysical facies: A case study of the Wufeng-Longmaxi Formation in LZ block of Sichuan Basin. Oil Geophysical Prospecting, 2023, 58(1): 214-227.

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http://www.ogp-cn.com/CN/10.13810/j.cnki.issn.1000-7210.2023.01.023 或 http://www.ogp-cn.com/CN/Y2023/V58/I1/214

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