大数据驱动下的智能气藏数字孪生系统关键技术及应用研究

孙亚隆; 牛浩; 张永利; 陈宇轩; 古毅; 方军龄; 张仕帆

doi:10.13809/j.cnki.cn32-1825/te.2024614

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大数据驱动下的智能气藏数字孪生系统关键技术及应用研究
Research on key technologies and applications of intelligent gas reservoir digital twin system driven by big data
2026年16卷第2期页码：479-488
收稿：2025-07-04，

纸质出版：2026-03-26
DOI： 10.13809/j.cnki.cn32-1825/te.2024614
稿件说明：

移动端阅览

孙亚隆, 牛浩, 张永利, 等. 大数据驱动下的智能气藏数字孪生系统关键技术及应用研究[J]. 油气藏评价与开发, 2026, 16(2): 479-488. DOI： 10.13809/j.cnki.cn32-1825/te.2024614.

SUN Yalong, NIU Hao, ZHANG Yongli, et al. Research on key technologies and applications of intelligent gas reservoir digital twin system driven by big data[J]. Petroleum Reservoir Evaluation and Development, 2026, 16(2): 479-488. DOI： 10.13809/j.cnki.cn32-1825/te.2024614.

摘要

传统油气藏研究方法通常依赖各专业领域的分工合作，通过接力推进的方式进行研究与开发。然而，这种模式难以实现各环节之间的高效协同，且在整体效益优化方面存在局限。为解决这一问题，研究提出了一种智能气藏数字孪生系统，融合人工智能算法适配、数据预处理、数据分析应用及模型自动更新预测四大核心技术，旨在构建一个集多学科协同、高效数据利用与动态优化预测于一体的综合平台。研究表明：针对勘探与开发过程中数据的多维度、异构性与高噪声特性，系统通过异常值处理、缺失值填补、数据变换、统计分析与质量评估于一体的数据预处理方案，保障了数据的准确性与可靠性；通过封装随机森林、梯度提升等人工智能算法，系统能够构建多参数与气藏产能之间的最佳回归模型，重点解决产量分析、压裂分析和“甜点”预测三大关键问题，从而提高产量预测精度、优化压裂施工参数，并有效识别“甜点”区域。此外，系统采用自动建模引擎，动态更新气藏的构造模型、相模型和属性模型，并结合模拟器引擎进行实时模拟、跟踪与预测，保证了模型在气藏开发过程中的适应性与准确性。系统借助数字孪生技术，在虚拟空间中构建了与气藏实体一致的虚拟模型，实现了对气藏全生命周期的分析、预测与优化管理。研究成果为推动气藏管理向智能化、精细化与高效化方向发展提供了有力的理论支撑和技术保障。

Abstract

Traditional oil and gas reservoir research methods usually rely on the division of labor and collaboration among different specialized disciplines

with research and development conducted in a relay-style approach. However

this model faces challenges in achieving efficient collaboration across different stages and has limitations in optimizing overall benefits. To address this issue

an intelligent gas reservoir digital twin system was proposed

integrating four core technologies: artificial intelligence algorithm adaptation

data preprocessing

data analysis applications

and automatic model updating and prediction

aiming to construct a comprehensive platform that integrates multidisciplinary collaboration

efficient data utilization

and dynamic optimization and prediction. The results showed that

in response to the multidimensional

heterogeneous

and high-noise characteristics of data during the exploration and development process

the system ensured data accuracy and reliability through an integrated data preprocessing scheme incorporating outlier handling

missing value imputation

data transformation

statistical analysis

and quality assessment. By encapsulating artificial intelligence algorithms such as random forest and gradient boosting

the system was able to construct the optimal regression model between multiple parameters and gas reservoir productivity

focusing on three key problems—production analysis

fracturing analysis

and “sweet spot” prediction—thereby improving production prediction accuracy

optimizing fracturing operation parameters

and effectively identifying “sweet spot” areas. In addition

the system adopted an automatic modeling engine to dynamically update the structural

phase

and attribute models of the gas reservoir

and combined it with a simulator engine for real-time simulation

tracking

and prediction

thereby ensuring model adaptability and accuracy throughout the gas reservoir development process. By leveraging digital twin technology

the system constructed a virtual model consistent with the physical gas reservoir in virtual space

enabling analysis

prediction

and optimized management throughout the entire lifecycle of the gas reservoir. The research results provide strong theoretical support and technical assurance for promoting the development of gas reservoir management toward intelligent

refined

and efficient practices.

关键词

Keywords

references

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