Oilfield Measure Effect Prediction Based on Radial Basis Process Neural Network
JIANG Xinzhu
XU Shaohua
Abstract：Because of complicated factors which influence the oil wells’ increment ,and the non-linear relationship be-tween them ,it’s hard for traditional methods of oilfield measure effect prediction to reflect the time accumulation’s influence . So the oilfield measure effect prediction method based on the radial basis process neural network is proposed ,the model with the improved GA is optimized and applied in predicting the actual oil wells’ increment after fracturing .According to the tes-ting result ,the oilfield measure effect prediction method based on the radial basis process neural network has higher accuracy and can be a practical oilfield measure effect prediction method .
Keywords：radial basis process neural networkgenetic algorithmoilfield measure effectfracturingoil increment prediction
Publication Date：2016-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：4( 1443-1445,1492 )