基于中子活化瞬发伽马能谱确定地层元素含量的方法

Method for the Determination Formation Element Content Based on Neutron Activation Prompt Gamma Spectrometry

中子技术能够利用中子与物质的相互作用确定其组成等性质,广泛应用于医学、环境科学、核反应堆建设、金属矿产勘查及石油工业中。在石油天然气领域,中子探测技术主要被用于确定井孔中的孔隙度、密度和流体饱和度等参数。随着测井技术的不断发展,基于瞬发伽马射线的中子活化分析技术也已应用于井下确定地层元素含量,并成为复杂岩性和非常规油气储层获得矿物含量的唯一手段。通过归纳地层元素伽马能谱测井的方法,利用蒙特卡罗方法模拟不同地层条件下的伽马能谱,构建地层元素的标准谱库,研究奇异值分解和极大似然估计能谱数据处理方法,并利用得到的元素产额及氧闭合模型实现地层元素含量的确定。利用蒙特卡罗方法构建已知地层的伽马能谱,采用上述数据处理方法得到元素含量与地层真实元素含量误差均在4%之内,Si误差最大为2.8%,Ca最大误差为3.3%,研究结果为井下确定地层元素成分及含量提供了有效的技术支持。

Neutron technology utilizes the interaction between neutrons and matter to determine its composition and other properties,and is widely used in medicine,environmental science,nuclear reactor construction,metal mineral exploration,and petroleum industry.In the oil and gas industry,neutron detection technology is primarily employed to determine crucial parameters such as porosity,density,and fluid saturation in well bore formations.As logging technology progresses,neutron activation analysis based on prompt gamma rays has emerged to determine the content of formation elements in the well,becoming the sole method for obtaining mineral content in complex lithology and unconventional oil and gas reservoirs.This paper presented a detailed account of the gamma-ray spectrum logging method for assessing formation elements.The Monte Carlo method was used to simulate the gamma-ray spectrum under varying formation conditions,and a standard spectrum library for formation elements was constructed.Furthermore,the study explored data processing methods,including singular value decomposition and maximum likelihood estimation,to extract specific parameters from the spectrum data.The obtained element yield and oxygen closure model accurately determined the formation element content.Monte Carlo simulations were conducted using known formation data,and the aforementioned data processing methods yielded errors within 4% compared to the actual formation element content,with the maximum error for Si being 2.8% and Ca element being 3.3%.These research findings establish effective technical support for the determination of composition and content of formation elements in subsurface environments.