摘要
川中地区金秋气田—天府气区沙溪庙组沙一段致密砂岩储层成岩相复杂,给储层评价与天然气勘探开发造成了较大困扰,但传统成岩相识别方法准确率低、对专业人员依赖性强、耗时长,急需准确率高、成本低、速度快的成岩相识别方法。首先,基于铸体薄片鉴定数据,通过组分三端元图确定了致密砂岩岩性,结合图像处理技术确定了孔隙、胶结物的类型与比例,并划分了致密砂岩成岩相。然后,对岩心划分成岩相数据对应的1019个深度测井数据进行了分布范围、中位数、均匀性、偏斜性等特征分析,通过标准化将6条测井数据转换到了0~1范围,通过合成少数类过采样技术(synthetic minority over-sampling technique,SMOTE)处理数据不均衡问题。最后,选取传统机器学习算法和集成学习算法中的10种方法模型训练与性能对比。研究发现,集成学习算法(特别是极端随机树算法)在成岩相识别中表现最佳,其准确率和F_(1)分数均高于传统机器学习算法,显著提高了识别精度与稳定性。利用构建的极端随机树算法模型对JQ8井的成岩相进行预测验证,验证了该方法的可行性,为致密砂岩成岩相的研究提供了有效的技术手段和参考。
The complex diagenetic facies of the tight sandstone reservoir in the Shaximiao Formation,located in the Jinqiu gas field to Tianfu gas area in the central Sichuan region,pose significant challenges to reservoir evaluation and natural gas exploration and development.Traditional diagenetic facies identification methods are often low in accuracy,heavily reliant on specialized personnel,and time-consuming.There is an urgent need for a diagenetic facies identification method that is highly accurate,cost-effective,and fast.Firstly,based on cast thin section identification data,the lithology of the tight sandstone was determined using a ternary plot of components.Image processing techniques were then used to identify the types and proportions of pores and cements,and the diagenetic facies of the tight sandstone were classified.Secondly,the corresponding 1019 depth-based well log data for core-divided diagenetic facies were analyzed in terms of distribution range,median,uniformity,and skewness.These 6 types of well log data were standardized to a 0-1 range,and data imbalance was addressed using synthetic minority over-sampling technique(SMOTE).Finally,10 traditional machine learning algorithms and ensemble learning algorithms were selected for model training and performance comparison.The study found that ensemble learning algorithms,especially the extreme randomized trees(ET)algorithm,performs best in diagenetic facies identification,achieving higher accuracy and F_(1) scores than traditional machine learning algorithms.This significantly improved identification accuracy and stability.The ET model was then used to predict the diagenetic facies of the JQ8 well,validating the feasibility of the method.This study provides effective technical methods and references for diagenetic facies research in tight sandstones.
作者
曹脊翔
陈思源
肖柏夷
杨曦冉
罗莹莹
陈宏
吴丰
CAO Ji-xiang;CHEN Si-yuan;XIAO Bai-yi;YANG Xi-ran;LUO Ying-ying;CHEN Hong;WU Feng(Tight Oil and Gas Project Department,PetroChina Southwest Oil and Gas Field Company,Chengdu 610056,China;School of Computer Science and Software Engineering,Southwest Petroleum University,Chengdu 610500,China;School of Geoscience and Technology,Southwest Petroleum University,Chengdu 610500,China;Sichuan Rainbow Oil and Gas Field Technology Company,Chengdu 610500,China)
出处
《科学技术与工程》
北大核心
2025年第21期8858-8870,共13页
Science Technology and Engineering
基金
四川省重点研发计划(重大科技专项)(2020YFSY0039)
国家自然科学基金区域创新发展联合基金(U20A20266)
中国石油-西南石油大学创新联合体科技合作项目(2020CX030103)。
关键词
成岩相
沙溪庙组
特征分析
集成学习
机器学习
diagenetic facies
Shaximiao Formation
feature analysis
ensemble learning
machine learning
