基于深度学习数据融合的测井数据精细表征  

Fine characterization of logging data based on the deep learning data fusion

作　　者：孙正心 金衍[1,2,3] 孟翰 郭旭洋 SUN Zhengxin;JIN Yan;MENG Han;GUO Xuyang(College of Artificial Intelligence,China University of Petroleum-Beijing,Beijing 102249,China;State Key Laboratory of Petroleum Resources and Engineering,China University of Petroleum-Beijing,Beijing 102249,China;College of Petroleum Engineering,China University of Petroleum-Beijing,Beijing 102249,China)

机构地区：[1]中国石油大学(北京)人工智能学院,北京102249 [2]中国石油大学(北京)油气资源与工程全国重点实验室,北京102249 [3]中国石油大学(北京)石油工程学院,北京102249

出　　处：《石油科学通报》2025年第1期75-86,共12页Petroleum Science Bulletin

基　　金：国家自然科学基金面上项目“深层脆性页岩井壁失稳的化学断裂机理与控制研究”(52074314)资助。

摘　　要：测井曲线记录钻井过程中地层的物理参数,在研究岩石特性、评估油气藏资源及揭示储层分布等方面具有重要意义。随着油气勘探的深入,隐蔽油气藏的复杂性不断增加,而传统测井数据分辨率较低的局限性,难以满足薄互层储层改造选点的需求,亟待开发高分辨率的测井数据精细解释方法。本研究提出了一种基于ResNet50回归算法的储层预测模型。该模型将能够捕捉复杂垂向地质细节的纵向连续光学薄片数据,与5种常规测井参数相结合,提升储层分析的精度。通过对某井区二叠系地层的5个井段数据进行验证,使用连续的570张地层图片样本与测井数据进行训练与预测,模型将测井数据分辨率从12.5 cm提升至6.25 cm,显著提高了测井数据的精度和分辨率。本研究使用3种公认的定量评估指标决定系数(R^(2))、均方根误差(RMSE)和平均绝对误差(MAE)对模型性能进行了全面评估。结果表明,该模型在声波时差(AC)、补偿中子(CNL)、电阻率(RT)和伽马(GR)参数的预测中表现较为准确,平均误差低于0.094,展示出模型在预测精度上的可靠性与优异性。然而,在密度(DEN)参数的预测中,模型在岩性变化较大或地质条件复杂的井段中受到了一定影响。Well logging curves are essential for recording the physical parameters of formations during drilling,providing vital information for analyzing rock properties,evaluating hydrocarbon reservoirs,and understanding reservoir distribution.As oil and gas exploration continues to progress,the complexity of subtle and hidden reservoirs has increased,posing challenges for traditional exploration techniques.Despite their importance,conventional well logging data suffer from low resolution,which significantly limits their ability to address the requirements of detailed reservoir characterization.In particular,the inability to precisely identify modification points in thin interbedded reservoirs remains a critical bottleneck in reservoir analysis.To overcome these limitations,developing high-resolution interpretation methods for well logging data has become an urgent priority in the field of reservoir analysis and geological exploration.This study proposes a novel reservoir prediction model based on the ResNet50 regression algorithm.By integrating vertically continuous optical thin-section data,which can capture fine-scale and complex vertical geological features,with five conventional well logging parameters,the proposed model aims to improve the resolution and accuracy of reservoir analysis.This combination leverages the strengths of image-based geological analysis and traditional well logging to deliver a more precise interpretation of subsurface formations.The model was validated using data collected from five intervals of the Permian formation in a specific well area.A total of 570 continuous geological image samples,combined with their corresponding well logging data,were utilized for model training and prediction.The results demonstrate that the model effectively enhances the resolution of well logging data,improving it from the traditional 12.5 cm to 6.25 cm.This significant improvement not only increases the precision of well logging interpretation but also provides a more detailed understanding of reservoir charac

关 键 词：数据融合 测井参数精细表征 ResNet50 深度学习模型 储层精细化建模 

分 类 号：P618.13[天文地球—矿床学] P631.81[天文地球—地质学]