A new prediction method for high-permeability carbonate reservoir based on multi-source data fusion and its application

He Wenyuan; Chen Xin; Xia Yaliang; Wang Bo; Xiao Dengyi; Tang Zichang

doi:10.7623/syxb202509007

English Version Reader LoginJournal Login

您当前的位置：

首页 >

文章列表页 >

A new prediction method for high-permeability carbonate reservoir based on multi-source data fusion and its application

更新时间：2025-10-18

- A new prediction method for high-permeability carbonate reservoir based on multi-source data fusion and its application
- ACTA PETROLEI SINICA Vol. 46, Issue 9, Pages: 1738-1750(2025)
- 作者机构：
  
  1. 中国石油国际勘探开发有限公司北京 100034
  2. 中国石油集团东方地球物理勘探有限责任公司河北涿州 072750
- 作者简介：
- 基金信息：
- DOI：10.7623/syxb202509007
  CLC： TE122.2
- Published Online：11 October 2025，
  
  Published：2025
- 稿件说明：
移动端阅览
何文渊, 陈鑫, 夏亚良, 王波, 肖灯意, 唐资昌. 一种多源数据融合的碳酸盐岩高渗带预测方法及其应用[J]. 石油学报, 2025, 46(9): 1738-1750,1791.

He Wenyuan, Chen Xin, Xia Yaliang, et al. A new prediction method for high-permeability carbonate reservoir based on multi-source data fusion and its application[J]. Acta petrolei sinica, 2025, 46(9): 1738-1750.
何文渊, 陈鑫, 夏亚良, 王波, 肖灯意, 唐资昌. 一种多源数据融合的碳酸盐岩高渗带预测方法及其应用[J]. 石油学报, 2025, 46(9): 1738-1750,1791. DOI： 10.7623/syxb202509007.

He Wenyuan, Chen Xin, Xia Yaliang, et al. A new prediction method for high-permeability carbonate reservoir based on multi-source data fusion and its application[J]. Acta petrolei sinica, 2025, 46(9): 1738-1750. DOI： 10.7623/syxb202509007.

摘要

高渗透碳酸盐岩储层的精准预测是提升油气田开发效率与采收率的关键。针对碳酸盐岩储层强非均质性导致的高渗带预测难题

特别是中东地区注水开发油田对高渗透储层表征的迫切需求

提出了一种多源数据融合的高渗带预测方法。该方法首先通过岩心、岩石薄片观察与物性测试的有机结合

准确识别储层高渗带;进而基于智能测井相分析技术

实现高渗带岩石类型的精细划分

并建立可靠的孔隙度—渗透率关系模型。为突破传统方法的精度限制

采用高精度全频地震反演技术精细刻画储层物性分布

结合深度学习方法实现渗透率三维空间展布的智能化预测。通过整合地震小断层预测与生产动态数据

揭示断裂系统对渗透率分布的控制规律

最终实现预测结果的动态校正与优化。在中东地区W油田的实际应用中

该方法将渗透率预测误差从传统方法的15%降低至7%以下

为井网优化与注采方案调整提供了可靠依据

有效提升了开发效果。所建立的预测方法体系为复杂碳酸盐岩储层高效开发提供了创新性解决方案

具有良好的应用前景与推广价值。

Abstract

Accurate prediction of high-permeability carbonate reservoirs is crucial for enhancing efficiency and recovery rates in oil and gas field development. The paper proposes a method integrating multi-source data to address the challenge of predicting high-permeability zones in carbonate reservoirs

especially the urgent need for characterizing high-permeability reservoirs in waterflooded oilfields in the Middle East. This method can accurately identify high-permeability zones based on the combination of core and thin section observations with petrophysical tests. Subsequently

intelligent logging lithofacies analysis techniques are employed to achieve the refined classification of rock types

and establish a reliable porosity-permeability relationship model. To overcome the precision limitations of conventional methods

a high-resolution full-frequency seismic inversion technology is used to delineate reservoir properties. Combined with deep learning-based approaches

an achievement has been made in the intelligent prediction of three-dimensional spatial permeability distribution. By integrating seismic-based minor fault prediction with dynamic production data

the study reveals the controlling regularity of fracture system on permeability distribution

thus achieving dynamic calibration and optimization of predictions. In practical applications in W field of the Middle East

this method reduces permeability prediction errors from traditional levels of 15 % to below 7 %

providing a reliable basis for well network optimization and injection-production scheme adjustment

thereby enhancing the development effect. This predictive framework offers an innovative solution for efficiently exploiting complex carbonate reservoirs

with promising application prospects and significant potential for widespread adoption.

关键词

Keywords

references

王小敏,樊太亮.碳酸盐岩储层渗透率研究现状与前瞻[J].地学前缘,2013,20(5):166-174.

BABAK O,RESNICK J.On the use of particle-size-distribution data for permeability prediction[J].SPE Reservoir Evaluation & Engineering,2016,19(1):163-180.

赵建国,潘建国,胡洋铭,等.基于数字岩心的碳酸盐岩孔隙结构对弹性性质的影响研究(下篇):储层孔隙结构因子表征与反演[J].地球物理学报,2021,64(2):670-683.

PIRRONE M,BONA N,GALLI M T.Physics-based probabilistic permeability prediction in thin-layered reservoirs:transport theory,dielectric dispersion logging and core-to-log Bayesian statistics[J].Petrophysics,2024,65(6):971-982.

DI Jianwei,JENSEN J L.A new approach for permeability prediction with NMR measurements in tight formations[J].SPE Reservoir Evaluation & Engineering,2016,19(3):481-493.

YANG Liuqing,FOMEL Sergey,WANG Shoudong,et al.Porosity and permeability prediction using a transformer and periodic long short-term network[J].Geophysics,2023,88(1):WA293-WA308.

PARRA J O.Deep learning for predicting permeability logs in offset wells using an artificial neural network at a Waggoner Ranch reservoir, Texas[J].The Leading Edge,2022,41(3):184-191.

DING Qian,GAN Lideng,WEI Lele,et al.Permeability prediction based on dual seismic pore structure factors[J].Geophysics,2023,88(3):M173-M184.

丁骞,甘利灯,魏乐乐,等.双孔隙结构因子及其在储层渗透性地震预测中的应用[J].石油学报,2023,44(2):339-347.

MIELE R,GRANA D,VARELLA L E S,et al.Iterative geostatistical seismic inversion with rock-physics constraints for permeability prediction[J].Geophysics,2023,88(2):M105-M117.

甘利灯,王峣钧,罗贤哲,等.基于孔隙结构参数的相控渗透率地震预测方法[J].石油勘探与开发,2019,46(5):883-890.

ABOUBACAR M S I,HENG Z,LI Jie,et al.Prediction of petrophysical classes and reservoir beds through microfacies and pore types characterization,Tahe Ordovician naturally fractured vuggy carbonates[J].Interpretation,2022,10(4):T837-T854.

WEI Guohua,CAO Danping,XIE Yu.Porosity,shale content,and permeability prediction based on prestack multiparameter inversion[C]// SEG Technical Program Expanded Abstracts.SEG,2021:292-296.

AL-ANAZI,A,GATES I D D.Support-vector regression for permeability prediction in a heterogeneous reservoir:a comparative study[J]. SPE Reservoir Evaluation & Engineering,2010,13(3):485-495.

梁尚斌,赵海洋,宋宏伟,等.利用生产数据计算油藏相对渗透率曲线方法[J].大庆石油地质与开发,2005,24(2):24-25

RAHEEM O,PAN Wen,MORALES M M,et al.Best practices in automatic permeability estimation:machine-learning methods vs.conventional petrophysical models[J].Petrophysics,2024,65(5):789-812.

LUCIA F J.Rock-fabric/petrophysical classification of carbonate pore space for reservoir characterization[J].AAPG Bulletin,1995,79(9):1275-1300.

何文渊,毕建军,陈彦虎.地震波形指示反演方法、原理及应用[M].北京:科学出版社,2021:43-52.

HE Wenyuan,CHEN Xin,SONG Jiawen,et al.Integrated hydrocarbon detection based on full frequency pre-stack seismic inversion[J].SPE,2025(4):1-11.

熊繁升,甘利灯,孙卫涛,等.裂缝-孔隙介质储层渗透率表征及其影响因素分析[J].地球物理学报,2021,64(1):279-288.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Distribution of hydrocarbon fluid and division of flow units in fracture-cave reservoir

Heterogeneity of clastic carrier bed and hydrocarbon migration and accumulation

Vertical heterogeneity characteristics of pore structure for huge thick coal reservoirs: a case study of Juhugeng mining area in Q inghai province

Related Author

Chen Zhihai Huang Guangtao Liu Changhong Chang Tielong

黄广涛

刘常红

常铁龙

Luo Xiaorong

Zhang Liqiang

Zhang Likuan

Lei Yuhong

Related Institution

中国石化石油勘探开发研究院北京100083

Petroleum Exploration and Production Research Institute,Sinopec

Institute of Geomechanics, Chinese Academy of Geological Sciences

School of Geosciences, China University of Petroleum

Institute of Geology and Geophysics, Chinese Academy of Sciences

AI问答

⁰