Taking Shahejie Fm in block 3 of Bonan oilfield as an example

the pattern recognition method based on genetic artificial neural network is used to study the diagenetic reservoir facies of low permeability reservoirs. Based on study of the sedimentary facies and diagenesis of reservoirs

7 parameters including flow zone index

porosity

permeability

median grain diameter

shale content

mean radius of pore throat and variation coefficient etc. are selected and neural network pattern recognition method is used to identify the diagenetic reservoir facies of Shahejie Fm in block 3 of Bonan oilfield through building learning and predicting models of genetic neural network. Four diagenetic reservoir facies are recognized

namely secondary pore diagenetic reservoir facies resulted from strong dissolution of unstable components (type Ⅰ)

secondary pore diagenetic reservoir facies resulted from dissolution of carbonate cements (type Ⅱ)

relic intergranular pore diagenetic reservoir facies after strong compaction and cementation (type Ⅲ)

and tight diagenetic reservoir facies after extremely strong compaction and strong cementation (type Ⅳ). The reservoir properties of the type I reservoir facies are the best

while that of type Ⅳ are the worst and they are nonreservoirs or poor reservoirs.