Optimizing fracture design based on a novel RBF neural network

Hong Liu (), Zhen Huang, Hong-yi Gao and Qing-heng Zeng
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Hong Liu: Chongqing University of Science and Technology
Zhen Huang: Northwest-Sichuan Gas Field
Hong-yi Gao: CNOOC-ETS-Oilfield Technology Services Co.
Qing-heng Zeng: Chongqing University of Science and Technology

Fuzzy Information and Engineering, 2011, vol. 3, issue 1, 23-33

Abstract: Abstract The factors affecting performance of fractured wells are analyzed in this work. The static and dynamic geologic data of fractured well and fracturing treatment parameters obtained from 51 fractured wells in sand reservoirs of Zhongyuan oilfield are analyzed by applying the grey correlation method. Ten parameters are screened, including penetrability, porosity, net thickness, oil saturation, water cut, average daily production, and injection rate, amount cementing front spacer, amount sand-carrying agent and amount sand. With the novel Radial Basis Function neural network model based on immune principles, 13 parameters of 42 wells out of 51 are used as the input samples and the stimulation ratios as the output samples. The nonlinear interrelationship between the input samples and output samples are investigated, and a productivity prediction model of optimizing fracture design is established. The data of the rest 7 wells are used to test the model. The results show that the relative errors are all less than 7%, which proves that the novel Radial Basis Function neural network model based on immune principles has less calculation, high precision and good generalization ability.

Keywords: Radial basis function neural network; Grey correlation method; Artificial immune system; Hydraulic fracture; Optimum design (search for similar items in EconPapers)
Date: 2011
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DOI: 10.1007/s12543-011-0063-z

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