摘要
岩屑荧光检测是判断储层含油性与油气显示级别的重要手段,但传统人工判读存在主观性强、难以量化等问题。为此,提出一种基于HSV色彩空间的荧光图像自动识别方法。通过将RGB图像转换至HSV色彩空间,解耦色调(H)、饱和度(S)和明度(V),有效抑制光照不均干扰,并结合阈值分割提取荧光区域,统计不同颜色(如黄白、亮黄、浅蓝)的像素占比,可实现荧光特征的定量化表征。进一步融合地质深度信息,构建“深度-图像”剖面图,实现了图像与录井曲线的深度对齐。该方法已用于鄂尔多斯盆地F 25等多口预探井1286个储层单元的含油性解释与评价,经试油、测压及生产动态资料验证,其中1103个储层的解释结果与实际流体性质相符,整体符合率达85.8%,较传统人工判读方式提高18%,显著提升了油气判识的客观性、一致性与可视化水平,为智能录井中油气显示的自动识别与分级提供了技术支撑。
Cuttings fluorescent detection is an important means to judge the oil content and SG&O grade of reservoirs,but the traditional artificial interpretation has the problems of subjectivity and difficulty in quantification.To this end,an automatic identification method of fluorescent images based on HSV color space is proposed.By converting RGB images to the HSV color space and decoupling hue(H),saturation(S),and value(V),the interference of uneven illumination is effectively suppressed.By combining threshold segmentation to extract fluorescent regions and statistically analyzing the proportion of pixels in different colors(e.g.,yellow-white,bright yellow,light blue),fluorescent features can be quantitatively characterized.By further integrating deep information of geology and constructing a"depth–image"profile,the depth alignment between images and mud logging curves has been achieved.This method has been applied to the interpretation and evaluation of oil content in 1286 reservoir units of multiple preliminary prospecting wells,including well F 25 in the Ordos Basin.After verification through oil test,pressure measurement,and production dynamic data,the interpretation results of 1103 reservoirs were consistent with the actual fluid properties.The overall coincidence rate reached 85.8%,an 18%increase over traditional manual interpretation.This significantly enhances the objectivity,consistency,and visualization level of the oil and gas discrimination,providing technical support for the automatic identification and classification of SG&O in intelligent mud logging.
作者
卢履盛
方铁园
黄子舰
焦艳爽
梁晓双
张春朋
LU Lyusheng;FANG Tieyuan;HUANG Zijian;JIAO Yanshuang;LIANG Xiaoshuang;ZHANG Chunpeng(CCDC Well Intervention Company,CNPC,Chengdu,Sichuan 610057,China;Panjin Zhonglu Oil&Gas Technology Service Ltd.,Panjin,Liaoning 124000,China)
出处
《录井工程》
2025年第4期58-62,共5页
Mud Logging Engineering
