摘要
随钻电阻率在随钻测井中占有重要的地位。随钻电阻率受井眼(井径,泥浆)、围岩(高阻或低阻邻层)等环境因素的影响,其测井值常不同于实际地层的电阻率值。对随钻测井曲线进行环境影响校正主要使用图版法。校正图版是根据理论计算或实验结果做出的,不适合于逐点对所有井段的地层进行较全面的环境影响校正。以Schlumberg-er、Haliburton和Bakerhuges三大公司随钻电阻率测井的围岩影响校正图版,研究了围岩电阻率、地层厚度和倾角与随钻电阻率测井值的关系,对图版采样读值,并采用神经网络法进行最优拟合得到校正公式,编制程序来实现随钻电阻率测井的围岩影响自动校正。应用表明,用这种自动校正法处理得到的结果合理,校正效果明显。
Resistivity logging while drilling plays an important role in logging while drilling. Resistivity logging while drilling is affected by such environmental factors as borehole(hole diameter,mud) and shoulder beds (high resistivity or low resistivity shoulder beds), its logging value is always different from actual formation resistivity. The environmental factor correction of logging curves while drilling mainly uses chart correction method. The correction chart is made based on theory calculation or experiment result , it is not suitable for successive comprehensive environmental correction on all well segment formation. Schlumberger、Haliburton and Bakerhuges shoulder bed effect correction charts on resistivity logging while drilling made by the three companies are used to study on the relationship between resistivity logging values while drilling and shoulder bed resistivity, bed thickness and sampling and reading value on charts, and adopting neural network to match optimally, to gain correction formula, developing programs to realize automatic correction on shoulder bed effect in resistivity logging while drilling, all these show that ,the result gained with this method is rational, and the correction effect is evident.
出处
《西南石油学院学报》
CAS
CSCD
北大核心
2006年第6期20-23,共4页
Journal of Southwest Petroleum Institute
基金
四川省重点学科项目(SZD0414)。
关键词
随钻电阻率
校正图版
围岩
层厚
神经网络
resistivity logging while drilling
correction chart
shoulder bed
thickness
neural networks