GAS PRODUCTIVITY PREDICTION FOR TIGHT SANDY GRAVEL FORMATION IN THE DAQING OILFIELD
WANG Zhuwen, LIU Jinghua, XU Yanqing (College of Geo-Exploration Science and Technology, Jilin University, Changchun130026,China)
The gas productivity prediction is one of the main tasks in the oil/gas development. This paper discussed the potential relationship between the productive capacity and the logging response of a deeply buried, gas-bearing and tight sandy gravel formation in the Daqing oilfield,and probed the method to predict the productivity of the gas-bearing formation employing the neural network technique based on the logging data. The test results of the gas-bearing formation and the corresponding logging data were taken as the neural network training samples. Based on the training result, the static parameters of the logging data such as neutron porosity, density etc. were taken as the input parameters of the network to predict the productive capacity of the gas-bearing formation. Hence, the present paper provides a new parameter or tool for the development design of the deeply buried, gas-bearing and tight sandy gravel formation in the Daqing oilfield,thus has widen the application range of well-logging in oil/gas exploration and exploitation.