Volcanic rock reservoirs are affected by many factors such as lithofacies

lithology

and reservoir space types

and fluid identification is difficult

which is one of the difficulties in well logging interpretation. It is urgent to establish a convenient and quick identification method. For this reason

the SVM（Support Vector Machine） algorithm of machine learning is used to predict the fluids of unknown reservoirs for the volcanic rock reservoirs in the Nanpu Sag of the Bohai Bay Basin. The research shows that: ① Comprehensive application of core

well logging

mud logging and other data to optimize fluid sensitive characteristic parameters

single information sensitive parameters are acoustic time difference

compensation density

resistivity

multi-information fusion parameters are natural gamma relative value

total hydrocarbon Ratio

hydrocarbon gas density index

hydrocarbon gas humidity index

the above seven parameters participate in the model establishment; ②Using the SVM algorithm for volcanic fluid prediction

the reservoir fluid is divided into three types: oil layer

oil-water layer and water layer. Sensitive parameters of well logging and mud logging are selected

and a reliable sample library is trained. The correct judgment rate of the prediction library reaches 90 %. The prediction application of SVM algorithm shows that it has low calculation complexity and strong generalization ability

which can quickly identify the fluid properties of volcanic rocks and provide a reliable basis for the analysis of oil and gas accumulation rules and the production and development of geological reserves.