Detection of Oil Reservoir Bright Spot Using Back-propagation
Shih Wei Chan
Ren Jye Dzeng
|油氣儲集;圖型辨識;倒傳遞網路;亮點偵測;Oil Reservoir;Pattern Recognition;Back-propagation;Bright Spot Detection
本研究主要探討如何使用類神經網路於震測剖面圖的亮點圖形辨識，經由實地訪談油公司取得震測剖面圖資，並提出了五項常用特徵，如下:震波原始訊號(Seismic Signal)、震幅強度(Envelope)、瞬間頻率(Instantaneous Frequency)、瞬間相位(Instantaneous Phase)、逆推阻抗(Inversion Impedance)，再將特徵做前處理(Pre-processing)，如:除去異常極大值、除去極端異常值、正規化，最後輸入網路，反覆測試出使辨識率最佳的可能特徵組合、訓練函數(倒傳遞網路之變形Levenberg-Marquardt、Conjugate Gradient)、網路層數與神經元數，共12480組，最後提出最佳化辨識率的儲集層亮點辨識法。
he complexity of two-dimensional seismic data often leads to mistakes in discriminating oil reservoir. However, mis-drilling caused by these mistakes brings about thirty million loss in cost each time. Seismic data interpreters do recognition by rules and experiences. If we can use neural network in place of rules and experiences, then we can get rid of some chances of seismic data interpreters’ emotional discrimination or mislook. The past oil reservoir bright spot detections still can't be applied to practice for two reasons. First, target seismic data in past research to be detected is in perfect condition. Interpreters seldom deal with these kinds of seismic data in reality. There is a big difference between seismic data in perfect condition and seismic data in practice. Second, the seismic attributes that past research used differs from that interpreters used in oil companies. The primary goal of this research is to apply back-propagation neural network to pattern recognition of oil reservoir bright spot. By interviewing with Seismic data interpreters in the oil company, we propose five seismic attributes in common use including seismic signal, evelope, instantaneous frequency, instantaneous phase and inversion impedance. After five seismic attributes of feature extraction, we do pre-processing on extracted features including transformating .segy file into .mat file, elimination of blunder, elimination of outlier, normalization and building feature set matrixes. Then, we import feature set into neural network and train matrix by matrix. By tuning any possible neural network layer, hidden layer node, training function(Levenberg-Marquardt, Conjugate Gradient), we have summed up to 12480 times of neural network training. Finally, we propose a method of optimized-recognition rated oil reservoir bright spot detection.
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