Abstract：The fracture parameters are obtained through the well test data from a small number of wells. With the existing parameters as samples, the artificial neural network system is set up. The input parameters that influence the outcome of fracturing, such as the formation thickness, porosity, clay content, working stress and sand displacement, are selected. The ability to fracture conductivity and fracture half-length are the output parameters. With training by BP neural networks, the fracturing parameters of the whole wells are inferred, resulting in the fracturing distribution of the reservoir.
李新明. 用人工神经网络方法分析油藏压裂效果——以靖安油田塞A井区长2油藏为例[J]. 地质与资源, 2009, 18(3): 217-221.
LI Xin-ming. ANALYSIS ON THE FRACTURING RESULT OF RESERVOIR BY ARTIFICIAL NEURAL NETWORK: A case study of the C2 reservior in SA wellblock, Jingan oilfeld. GEOLOGY AND RESOURCES, 2009, 18(3): 217-221.
2009, Vol. 18 
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