考虑孔隙形状的碳酸盐岩储层流体综合识别方法研究及应用——以鄂尔多斯盆地奥陶系马家沟组四段为例

doi:10.11720/wtyht.2025.1183

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摘要

鄂尔多斯盆地马四段白云岩储层具有厚度较薄、致密、孔隙形状复杂、非均质性显著、地震响应相对较弱的特征。储层地震预测机理尚存疑,流体识别具有一定困难。传统的单一属性地球物理方法未能准确预测流体,因此我们以对孔隙形状与孔隙连通性的综合考虑为基础,借助岩石物理建模和分析,结合波动理论的叠前AVO反演、流体因子的频变AVO反演与基于PNN的孔隙结构参数预测,构建了一种新的综合流体识别方法。该方法全面考虑了弹性参数、物性参数与频散属性的影响,取得了显著的效果。相对于传统单一属性流体识别方法,综合流体识别方法展现出更高的精度,尤其在含气层区域表现出显著的指示效果,充分验证了其在流体识别领域的有效性,具备广泛推广和应用的潜力。

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	王永刚
	姚宗惠
	杨骐羽
	李景叶
	宋炜

关键词 ：马家沟组四段, 岩石物理模型, 孔隙形状, 频散属性, 流体识别

Abstract：

The dolomite reservoirs of the fourth member of the Majiagou Formation in the Ordos Basin are characterized by thin layers,tightness,complex pore shapes,strong heterogeneity,and relatively weak seismic responses.The mechanisms behind the seismic prediction of the reservoirs remains uncertain, making fluid identification challenging.Traditional geophysical methods based on a single attribute fail to accurately predict fluids.Therefore,by comprehensively accounting for the pore shapes and connectivity,this study proposed a new fluid identification method using petrophysical modeling and analysis,as well as wave theory-based prestack AVO inversion,frequency-dependent AVO inversion of fluid factors,and probabilistic neural network(PNN)-based prediction of pore structure parameters.This method,comprehensively considering the impacts of elastic parameters,physical parameters,and dispersion properties,achieve remarkable results.Compared to traditional single-attribute fluid identification method,the proposed method demonstrates higher accuracy,particularly for gas-bearing areas,fully verifying its effectiveness in fluid identification and highlighting its potential for widespread application.

Key words： the fourth member of the Majiagou Formation rock physical model shape of pore dispersion property fluid recognition

收稿日期: 2024-05-07 修回日期: 2024-11-04 出版日期: 2025-02-20

ZTFLH:

P631.4

基金资助:中国石油集团公司攻关性应用性科技专项“海相碳酸盐岩油气规模增储上产与勘探开发技术研究”课题4“复杂碳酸盐岩地球物理评价关键技术研究”(2023ZZ16YJ04)

作者简介: 王永刚(1980-),男,汉族,陕西扶风人,硕士研究生学历,高级工程师,主要研究方向为地震资料解释、地震反演及储层预测等。Email:wangyg_cq@petrochina.com.cn

引用本文:

王永刚, 姚宗惠, 杨骐羽, 李景叶, 宋炜. 考虑孔隙形状的碳酸盐岩储层流体综合识别方法研究及应用——以鄂尔多斯盆地奥陶系马家沟组四段为例[J]. 物探与化探, 2025, 49(1): 177-188.
WANG Yong-Gang, YAO Zong-Hui, YANG Qi-Yu, LI Jing-Ye, SONG Wei. A comprehensive fluid identification method for carbonate reservoirs considering pore shapes: A case study of the fourth member of the Ordovician Majiagou Formation,Ordos Basin. Geophysical and Geochemical Exploration, 2025, 49(1): 177-188.

链接本文:

https://www.wutanyuhuatan.com/CN/10.11720/wtyht.2025.1183 或 https://www.wutanyuhuatan.com/CN/Y2025/V49/I1/177

Fig.1 井1合成记录

Fig.2 综合流体因子预测的流程

Fig.3 考虑连通孔隙与孔隙形状的碳酸盐岩岩石物理模型

Fig.4 基于岩石物理模型的孔隙参数对弹性模量影响分析
a—连通孔隙变化情况下孔隙参数与体积模量关系;b—连通孔隙变化情况下孔隙参数与剪切模量关系;c—连通孔隙变化情况下孔隙参数与密度关系;d—软孔含量变化情况下孔隙参数与体积模量关系;e—软孔含量变化情况下孔隙参数与剪切模量关系;f—软孔含量变化情况下孔隙参数与密度关系

Fig.5 横波速度预测结果
a—质控井I1横波预测结果;b—质控井I2横波预测结果;c—预测井1横波预测结果;d—预测井2横波预测结果

Fig.6 孔隙形状参数(软孔含量)敏感性分析
a—纵横波速度比与体积模量散点分析;b—剪切模量与泊松比量散点分析;c—体积模量与泊松比散点分析;d—纵波速度与横波速度散点分析;e—体积模量、纵波速度和泊松比散点分析;f—纵波速度、横波速度和纵波模量散点分析;g—体积模量、密度和纵横波速度比散点分析

Fig.7 PCNN模型流程

Fig.8 LP-CNN多属性融合方法流程(以两种属性为例)

Fig.9 模型储层(a)及含气饱和度(b)示意

Fig.10 地震正演结果

Fig.11 单属性结果示意
a—纵波速度差(归一化结果);b—频散因子(归一化结果);c—孔隙结构参数

Fig.12 多属性融合预测结果(归一化结果)

Fig.13 马四段预测结果示意

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