摘要
为了解决塔里木盆地博孜井区巴什基奇克组深层气藏低对比度成因机理不明确、流体识别效果差等问题,基于铸体薄片、高压压汞以及核磁共振等分析化验资料,深入剖析低对比度成因机理;结合测井资料及生产动态资料,选取孔隙度、电阻率、体积模量、流体压缩系数、流体指数、等效流体体积模量与流体体积模量,应用随机森林算法进行流体识别。研究结果表明,塔里木盆地博孜井区巴什基奇克组深层气藏低对比度成因机理具有区域性差异,南井区低对比度是地层水矿化度、孔隙度及孔隙结构共同作用的结果,碳酸盐胶结物发育程度是北井区气藏低对比度成因的主控因素;基于随机森林算法的低对比度气藏流体识别模型的预测集精确率平均值为89.25%,该预测模型削弱了单一流体识别因子所引起的多解性,为气田的高效开发提供可靠依据。
In order to solve the problems such as unclear formation mechanism and poor fluid identification effect in deep gas reservoirs with low contrast of Bashijiqike formation in Bozi well area,Tarim basin,the mechanism of formation with low contrastis deeply analyzed based on the analysis data of cast thin section,high pressure mercury injection and nuclear magnetic resonance experiment.Combined with logging and production dynamic data,fluid sensitive factors such as porosity,resistivity,volume modulus,fluid compression coefficient,fluid index,equivalent fluid volume modulus and fluid volume modulus are selected to identify fluids by random forest algorithm.The results show that the low contrast formation mechanism is different in the region.The reservoirs with low contrast in the southern well area is the result of the combination of formation water salinity,reservoir physical property and pore structure.However,the degree of carbonate cement development is the main factor of the reservoirs with low contrast in the northern well area.The accuracy of the fluid identification model of low contrast gas reservoir based on random forest algorithm is 89.25%,which weakens the multiple solutions caused by a single fluid identification factor and provides a reliable basis for the efficient development of gas fields.
作者
曹原
赵元良
袁雪花
袁龙
荣俊卿
赵盼
别康
CAO Yuan;ZHAO Yuanliang;YUAN Xuehua;YUAN Long;RONG Junqing;ZHAO Pan;BIE Kang(Geological Research Institute,China National Logging Corporation,Xi’an,Shaanxi 710077,China;Exploration Department,PetroChina Tarim Oilfield Company,Korla,Xinjiang 841000,China;Exploration and Development Research Institute,PetroChina Dagang Oilfield Company,Tianjin 300457,China;Changqing Branch,China National Logging Corporation,Xi’an,Shaanxi 710200,China;Exploration and Development Research Institute,PetroChina Tarim Oilfield Company,Korla,Xinjiang 841000,China;Well Logging Key Laboratory,China National Petroleum Corporation,Xi’an,Shaanxi 710077,China)
出处
《测井技术》
CAS
2023年第6期671-678,共8页
Well Logging Technology
基金
国家科技重大专项(2011ZX05046-003)
中国石油集团测井有限公司十大科技项目“重点勘探领域测井解释评价核心技术攻关”(CNLC2022-08B02)。
关键词
流体识别
深层
随机森林
低对比度
博孜井区
fluid identification
deep reservoir
random forest
low contrast
Bozi well area
