应用遗传神经网络研究低渗透储层成岩储集相—以胜利渤南油田三区沙河街组为例

doi:10.11743/ogg20060118

Abstract

Abstract:

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.

Key words: neural network, low permeability, pattern recognition, diagenetic reservoir facies, Bonan oilfield

CLC Number:

TE112.2

Li Haiyan, Peng Shimi. Study diagenetic reservoir facies of low permeability reservoir with genetic neural network; take Shahejie Fm in block 3 of Bonan oilfield as an example[J]. Oil & Gas Geology, 2006, 27(1): 111-117.

References

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Study diagenetic reservoir facies of low permeability reservoir with genetic neural network; take Shahejie Fm in block 3 of Bonan oilfield as an example

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