双密度重叠找气新方法

本文根据岩石孔隙中的天然气引起介电测井孔隙度减小和密度测井孔隙度增大的原理,建立了双密度重叠找气新方法,消除了岩性的影响,提高了测井找气的分辨率,增加了评价气层的直观性。该方法用于寻找陕甘宁盆地低孔隙白云岩气藏,获得了明显的地质效果。这种方法还可以在其它储层岩性气藏中应用。     

地球物理测井数据格式及面向对象的数据读取

在进行地球物理测井数据处理或解释软件开发时,只有知道所处理的测井数据的格式,才能正确地进行读取、格式转换、数据处理以及绘图等工作。在对ECLIPS 5700数控测井系统XTF文件格式分析的基础上,基于内存映射文件技术,采用面向对象的方法创建了XTF文件数据读取类,实现了大数据量XTF文件数据的快速读取,也可为其它格式测井数据的格式转换和数据处理提供技术参考。     

Sea-level Eustatic Cycles and Potash Formation Event:A Case Study of Triassic Sichuan Basin

正Identification of the favorable salt-and potash-forming layer,we still mainly rely on logging interpretation and coring verification.However,we both know that the continuously carbonate platform is cyclical growing.It has recorded valuable information about the phase transition of carbonate rocks,which were synchronized with the relative sea-level cycles.This paper presents a fast and     

侵入带对视电阻率的影响

利用混合法研究了侵入带的三种模式——台阶型、斜坡型、具有低阻环带的侵入带对电位电极系视电阻率的影响。其目的, 一是确定常用的台阶型侵入带引入的误差, 二是研究细致地探测侵入带的方法。研究结果一方面表明了考虑过渡带和低阻环带可以提高测井解释的准确度,另一方面也为用多电极系电阻率测井探测侵入带提供了理论依据。     

斯通利波和渗透率的关系分析

理论和实际资料证明,在渗透性地层,低频斯通利波出现的频散和能量衰减与地层渗透率有关,因此可利用斯通利波的传播速度和能量衰减等特性来估算地层渗透率。斯通利波的能量和渗透率负相关。因为斯通利波能量代表了斯通利波的有关信息,因此可以认定斯通利波和渗透率负相关。     

声波扫描成像在灾害防治及环境保护中的应用

利用超声波扫描成像测井技术，在地质灾害的预防与环境保护中可以检测由于采矿而形成的冒落带、裂隙带、离层带、洞穴及软弱层的存在状况及部位，测定基岩风化蚀变程度及深度，在地质灾害与环境保护的治理时可以监测治理的质量，提供了直观精确可靠的基础资料。     

对综合测井曲线解释的几点体会

本文阐述了在地质找矿中从地质角度对综合测井曲线解释的重要性；指出了测井曲线的一般特点和特殊优点；尤其对致密钙质岩石在测井曲线上的表现形式及其厚度确定做了深入研究；分析了测井曲线解释存在的问题；最后提出了几点建议。     

利用测井曲线绘制煤岩层真厚对比图

采用三次样条函数拟合测井数据，并使用新的采样间隔对其进行采样，将采样数据映射到真厚的钻孔柱状图上，从而绘制出真厚对比图。实际编程中，为保证其拟合精度及计算效率，实际采用的计算步长一般为本层厚度加上下扩展10m。选择采样间隔时，可对测井曲线进行抽稀，以减小数据占用的空间。该方法的使用解决了长期以来地质与测井对比图工作分离、结论出现差异的问题。     

油气层录井的精细解释方法 ——以吉林探区为例

采用“四大参数法” (峰值、峰基比、甲烷和重烃) 、全烃曲线形态法和全烃储层灌满系数法, 解决了吉林探区油气层气测低异常和组分发育不全等问题。进一步以地化录井为核心, 结合核磁孔隙度和色谱油性, 建立了地化含油饱和度储层评价体系, 解决了油气层荧光、气测显示异常和电测低电阻的油层评价难题。     

Application of PSO-LIBSVM in Recognition of Potassium Salt Deposits

The shortage of potassium salt seriously restricts the development of China's agriculture. Increasing the exploration and development of potash will help improve the self-sufficiency of potassium in China. With rich potassium salt resources, Sichuan basin is one of the most important research areas for potash exploration and development in China. Polyhalite is an important solid potassium salt mineral in Sichuan basin, often intercalated in rock minerals such as anhydrite, rock salt and dolomite. Aiming at the problem that conventional log interpretation methods are difficult to accurately identify polyhalites, this paper proposed a new Support Vector Machine (SVM) recognition method based on Particle Swarm Optimization (PSO) to classify polyhalites in Sichuan basin. Based on particle swarm optimization and support vector machine theory, combined with logging interpretation theory, the effective data sensitive to polyhalite logging response were selected as input samples to generate training sets and test sets randomly. The Radial Basis Function (RBF) parameters were optimized by particle swarm optimization, and the classification and prediction model of polyhalite was established. Compared with mud logging results, the recognition accuracy of SVM model based on particle swarm optimization reached 97.5758%, which is obviously better than that of SVM model optimized by cross validation method in recognition accuracy and speed. The results show that the model has broad application prospects in potash exploration in Sichuan basin.