Hydrocarbon Detection By Generalized Target Inversion Under Frequency Division Multi-Attributes Combination

The well data available from Penglai Oilfield of Bohai basin is limited due to the low degree of exploration.Therefore, the effect of conventional impedance inversion used to predict the oil and gas is not ideal. The authors attempted to use the combination of frequency division multi-attribute and neural network algorithm for the prediction of oil and gas, and achieved good effect. Firstly, the seismic signals were transformed from time domain to frequency domain by the spectrum decomposition technique, and the combination of high,medium and low frequency sensitive to the hydrocarbon was determined.Then, analyzed the correlation of attributes that were associated with hydrocarbon anomalies, optimized the best combination of attributes, established the nonlinear correlation of seismic attributes and oil saturation curve by BP neural network algorithm,and verified the effectiveness of this method by well data.Finally, the nonlinear correlation was used to detect the hydrocarbon, predict the sand bodies with potentials and guide the deployment of wells. The method has good applicability in the shallow hydrocarbon-bearing strata with moderate sand ratio and good quality of seismic data.