摘要
目前与储层相关的勘探技术大多基于均匀单相各向同性介质模型的假设,忽略了储层的孔隙结构和充填流体,在实际应用中可能会对精度产生一定的影响。双相介质理论认为,地下介质由固体和流体(包括气体和液体)组成,能更准确地描述含油气地层中地球物理响应特征,提高油气检测精度。利用测井、录井及井中地层的地质解释结果,基于质量守恒理论和方程,进行井中目标储层流体(气体)和骨架密度参数的反演数值计算,根据计算的气体密度参数实现目标储层的流体识别。基于概率神经网络进行的流体密度反演,将井上得到的流体密度与地震属性进行训练,将训练得到的非线性映射关系推广到整个三维数据体,最终反演得到储层流体密度数据体,定量对储层进行描述和评价。利用实际资料进行流体密度预测,反演结果与实际测试结果吻合。
At present,the exploration technologies related to reservoir and mostly based on the assumption of single-phase isotropic homogeneous medium model ignore the reservoir pore structure and fluid filling.The precision may be affected in the actual production application.The theory of two-phase medium holds that the underground medium is composed of solid and fluid(including gas and liquid),which could more accurately describe the geophysical responding characteristics of oil and gas bearing strata and improve the accuracy of oil and gas detection.Based on the results of geological interpretation of log information,log data and in-well formation,the inversion numerical calculation of fluid(gas)and skeleton density parameters of the target reservoir in the well is carried out based on the mass conservation theory and equation.The fluid identification of the target reservoir is realized according to the calculated gas density parameters.The fluid density and seismic attributes from well are trained by the fluid density inversion based on the probabilistic neural network,the nonlinear mapping relations obtained from the training are extended to the whole 3D data volume,and the reservoir fluid density data volume are inverted finally.The reservoir is described and evaluated quantitatively.The fluid density are predicted by the actual data,and the inversion results are in good agreement with the actual test results.
作者
屈佳欣
佟恺林
QU Jia-xin;TONG Kai-lin(State Key Laboratory of Reservoir Geology and Development Engineering,Chengdu University of Technology,Chengdu Sichuan 610059,China;Sichuan Energy Mining Investment & Development Co. Ltd.,Chengdu Sichuan 610023,China)
出处
《油气地球物理》
2019年第2期50-55,共6页
Petroleum Geophysics
关键词
双相介质
流体密度反演
储层预测
概率神经网络
two-phase medium
fluid density inversion
reservoir prediction and probabilistic neural network
