一种多源数据融合的碳酸盐岩高渗带预测方法及其应用

何文渊; 陈鑫; 夏亚良; 王波; 肖灯意; 唐资昌

doi:10.7623/syxb202509007

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一种多源数据融合的碳酸盐岩高渗带预测方法及其应用

地质勘探 | 更新时间：2025-10-18

- 一种多源数据融合的碳酸盐岩高渗带预测方法及其应用
- A new prediction method for high-permeability carbonate reservoir based on multi-source data fusion and its application
- 石油学报 2025年46卷第9期页码：1738-1750
- 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
  中图分类号： TE122.2
- 网络出版：2025-10-11，
  
  纸质出版：2025
- 稿件说明：
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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.

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Keywords

references

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