摘要
精准地计算煤岩显微组分是查明煤岩特征的关键。以韩城矿区主力煤层为研究对象,利用煤岩显微组分分析化验和测井资料,采用交会图相关性分析手段,筛选了镜质组和惰质组含量的敏感性测井参数。利用所优选的声波时差、补偿密度、相对自然伽马及电阻率4个敏感性测井参数,构建了镜质组含量、惰质组含量神经网络预测模型,进而利用计算的煤岩显微组分编绘了研究区5~#煤层显微组分平面分布图。研究结果表明,补偿密度与镜质组含量正相关,而声波时差、补偿中子、相对伽马、电阻率测井值与镜质组含量负相关,声波时差和补偿密度相关较好,相对伽马和电阻率次之,补偿中子较弱;惰质组与测井参数的相关性与镜质组相反。5~#煤镜质组含量呈现南北高,中部低;惰质组含量呈现东北高、西南低;矿物成分高值区主要位于东北部地区,西南部则相对较低。
It is the key to identify coal characteristics that coal maceral is accurately calculated. The main coal seam in Hancheng mining area was taken as the research object, and the sensitive log parameters of the content of vitrinite and inertinite were selected by means of correlation analysis of cross plot. The neural network model of predicting the content of vitrinite and inertinite was constructed by using four selected sensitivity logging parameters of AC, DEN, GR and RT, and then the plane distribution maps of 5~#coal maceral in the research area were drawn out. The results show that DEN is positively correlated with the vitrinite content, while AC, CNL, ΔGR, RT are negatively correlated with the vitrinite content. The correlation between AC, DEN and the vitrinite content are positive, followed by GR and RT, and the CNL is relatively weak. The correlation between the inertinite content and log parameters is opposite to vitrinite. The vitrinite content of 5~#coal is high in the North and South, and low in the central region. The inertinite content is high in Northeast and low in Southwest. The high value zone of mineral composition is mainly located in the Northeast and relatively low in the Southwest.
作者
杨珺茹
刘之的
雷琦
王伟
时梦璇
马廷昊
YANG Jun-ru;LIU Zhi-di;LEI Qi;WANG Wei;SHI Meng-xuan;MA Ting-hao(School of Earth Sciences and Engineering,Xi an Shiyou University,Xi an 710065,China;Shaanxi Key Laboratory of Petroleum Accumulation Geology,Xi’an Shiyou University,Xi’an 710065,China;Petro China Coalbed Methane Company,Beijing 100028,China;Hancheng Branch of Petro China Coalbed Methane Company,Xi an 710082,China)
出处
《科学技术与工程》
北大核心
2020年第23期9293-9301,共9页
Science Technology and Engineering
基金
陕西省重点研发计划(2019GY-140)
西安石油大学研究生创新与实践能力培养计划(YCS19112024)。
关键词
煤岩
显微组分
测井预测
韩城矿区
coal rock
maceral
logging prediction
Hancheng mining area
