Strong tolerance random forest algorithm in seismic reservoir prediction
Song Jianguo1,2, Yang Lu1, Gao Qiangshan3, Liu Jong4
1. School of Geosciences, China University of Petroleum(East China), Qingdao, Shandong 266580, China; 2. Laboratory of Marine Mineral Resource Evaluation and Detection, Qingdao National Laboratory for Marine Science and Technology, Qingdao, Shandong 266071, China; 3. Lunar and Planetary Science Research Center, Institute of Geochemistry, Chinese Academy, Gui-yang, Guizhou 550081, China; 4. Research Institute of Petroleum Exploration and Production, SINOPEC, Beijing 100083, China
Abstract：Since it is difficult to effectively define noise and signal on seismic and logging data,seismic re-servoir prediction needs good noise tolerance algorithms.The random forest (RF) algorithm with strong noise tolerance is proved by adding noise to training samples.However this does not mean RF has good noise tolerance in seismic reservoir prediction as well.First we extract noise samples from original seismic data with strong noise in the Survey F3,and extract denoised samples from the data processed by the dip-steered median filter.Then we establish random forest regression models between seismic attributes and the porosity parameter.After processing the original seismic data and the filtered seismic data with the noise sample model and denosied sample model,we estimate four different porosity parameter cubic data.The results reveal that the two data sets obtained with the noise model are more disturbed by noise,and the other two data sets obtained with the denoised model are much less affected by noise.On these data sets,reservoir geological characteristics can be effectively characterized which proves the random forest model has strong robustness and perfect tolerance to abnormal data differing from the sample data.The key issue in the application of the random forest algorithm to seismic reservoir prediction is that the training data does not contain noise.In other words,the input variable of sample data being denoised is much more significant,whereas whether seismic data were denoised or not has less effects on the prediction result.
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