摘要
粘土矿物成份是造成气层电阻率低的主要原因。用灰色静态模型计算孔隙度 ,用BP神经网络计算束缚水饱和度等参数。与岩心分析结果比较 ,储层参数计算结果精度有所提高。用储层参数建立气层和水层的判别式 ,判别结果与试油有较好的一致性。综合数字处理结果建立了气层产能评价模型 ,处理结果与试油结果比较 ,符合率 82 %。
The composition of clay minerals is the main cause generating low-resistivity gas zone. Grey static system is used to calculate porosity, and the BP neural network is used to count irreducible water saturation, etc. Compared with core analytic results, the accuracy of reservoir parameters obtained from the system and the network has been enhanced. The math discriminant for gas and water beds, set up with the reservoir parameters, agrees well with the tested results. The newly developed model of productivity evaluation of gas bed has an 82 percentage of agreement with the test results.
出处
《测井技术》
CAS
CSCD
2003年第5期394-398,共5页
Well Logging Technology
关键词
低电阻率气层
测井
地层评价方法
含气砂岩
储层参数
数学模型
粘土矿物
low resistivity gas sand reservoir parameter math model clay mineral logging data processing formation evaluation
