基于测井曲线重构的致密砂岩储层裂缝识别

Identification of Fractures in Tight Sandstone Reservoirs Based on Reconstruction of Well Logging Curves

常规裂缝识别方法受致密砂岩储层物性差、测井响应不明显等因素的影响,在川西坳陷M气田N段应用效果差,为了提高裂缝识别的准确率,提出基于测井曲线重构的致密砂岩储层裂缝识别方法。首先,在对声波时差、深浅电阻率测井曲线进行平滑滤波处理后,对其进行构建测井曲线“基线”的处理;其次,在构建声波时差与深电阻率测井曲线“反向基线”的基础上,提出消除钙质夹层影响的方法;再次,提取3个裂缝敏感因子:声波时差增大因子、电阻率减小因子以及电阻率幅度差因子;最后,使用改进的斜率关联度法计算3个裂缝敏感因子与裂缝发育程度之间的关联度,将其作为各个敏感因子的权重,加权计算得到裂缝综合指示参数。经过岩心描述、电成像资料的标定,确定0.05为裂缝综合指示参数的下限,从而确定了裂缝识别的标准。将该方法应用于研究区的5口井,裂缝识别准确率达到85%以上。

The conventional fracture identification method has poor application effect in the tight sandstone reservoirs in N section of M gas field in the Western Sichuan Depression due to the complicated physical properties and unobvious logging response.Therefore,a fracture identification method for tight sandstone reservoirs based on logging curve reconstruction is proposed.Firstly,after the smooth filtering of the acoustic time difference logging curve and deep and shallow resistivity logging curves,the"baseline"of each logging curve is constructed.Secondly,the method to eliminate the influence of calcareous interlayer is proposed on the basis of constructing the"reverse baseline"of acoustic time difference logging curve and deep resistivity logging curve.Then,three fracture sensitivity factors,the acoustic time difference increase factor,the resistivity reduction factor and the resistivity amplitude difference factor are extracted.Finally,the correlation degree between each fracture sensitivity factor and the fracture development degree is calculated by using the improved slope correlation method,and is used as the weight of each fracture sensitivity factor,and the comprehensive indication parameter of the fracture is obtained by weighted calculation.According to the core description and the micro-resistivity imaging logging data,0.05 is determined as the lower limit of the fracture comprehensive indication parameter,thus the standard of fracture identification is determined.The method is applied to five wells in the study area,and the accuracy of fracture recognition is more than 85%.