It's very important to evaluate layered producing performances at different stages in the course of oilfield development, but due to limitations of monitoring well conditions and logging expenses, it is difficult to comprehensively and systematicly monitor all the producers, and this will bring troubles to adjustments and evaluations during oil field development. To solve this problem, the Chromatographic Fingerprint technique is introduced to monitor layered producing performances in test area F which has four target zones in Daqing Oilfied. After response of alkaline-surfactant-polymer (ASP) flooding, screw pumps are adopted to some producers to minimize scaling in F area where producing profile surveys for annulus wells aren't adaptable to mornitor the layed producing status. Pre-test in lab shows that the Chromatographic Fingerprint technique is feasible for the four individual test zones for there are distinct chromatographic fingerprint differences, and ASP injected solution has less influences on fingerprints. A fuzzy interpretation template has been successfully set up for commingled 4-6 layers production numerical simulation by using EPB neural network model, and relative errors can be controlled below 10%. The technique has been applied to the producers for 180 times. In-situ test results are stable and correspond to the static information of lays. It indicates that the Chromatographic Fingerprint technique is suitable for monitoring individual zone producing performances of ASP flooding. The results from the fuzzy interpretation template have provided powerful data to re-recognize the connectivity of heterogeneous reservoir between injectors and producers. It can be applied to producers whose producing intervals belong to the fuzzy interpretation template. The oil production proportion of per lay's can be easily achieved after the sample from the well fluid is analysed with chromatography and calculated with the fuzzy interpretation template. The technique has proved to be economical and it is not astricted by well conditions, so it can play an important role to guide development programs' adjustments and evaluations in oilfields.

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