Gas productivity prediction for deep-seated volcanic reservoir in Songliao Basin

The deep-seated volcanic reservoir of Songliao Basin is one of the main research areas of the geophysics, geology and geochemistry in Daqing at present. And now it had obtained plentiful and substantial achievements. As a parameter representing the dynamic characteristic, gas reservoir performance is one of main indices for reservoir evaluation. This paper discussed the potential relationship between the productive capacity and the logging response of the deep-seated volcanic gas-bearing formation in Daqing oilfield,and probed the method to predict the productivity of the gas-bearing formation making use of the neural network technique based on the logging data. The gas-bearing formation test results and the logging data were taken as the neural network training samples. Based on the training result the static parameters logging data such as neutron porosity, density etc. was taken as the input parameters of the network, the productive capacity of the gas-bearing formation could be predicted. Hence, it provided a new parameter or tool for the development design of the deep-seated volcanic gas-bearing formation in Daqing Oilfield,and had widen the application range of well-logging in oil/gas exploration and exploitation.