Gas well productivity prediction based on fractional Fourier transform

Production capacity forecasting is an important task in the oil and gas industry, but the common forecasting methods have high cost, many parameters and complex processes. In order to solve this problem, this paper uses relatively low cost logging data to investigate the relationship between Stoneley wave, longitudinal and transverse wave sign and reservoir characteristics. Fractional Fourier transform is introduced to improve signal processing methods and extract key parameters, including Stoneley wave frequency offset rate and energy attenuation coefficient of longitudinal and transverse wave. Finally, a classification system of high, middle and low production reservoir profiles is established based on oil test data. The following three conclusions are obtained: (1) Stoneley wave frequency deviation is closely related to reservoir physical properties (porosity and permeability), and the energy attenuation coefficient of longitudinal and transverse wave can better reflect the properties of pore fluid. (2) For Stoneley waves, the larger the order, the more concentrated the energy, the weaker the frequency shift, the smaller the order, and the more dispersed the energy. The 0.5 order Stoneley spectrum features prominently, which is convenient for parameter extraction and more sensitive to formation physical properties. For longitudinal and transverse waves, the order has little effect on attenuation. (3) In Yongle area, when the attenuation coefficient of longitudinal and transverse waves energy is greater than 4.5, and the frequency change rate is greater than 6%, the reservoir segment is high yield. On the contrary, when the attenuation coefficient of longitudinal and transverse waves energy is less than 2, and the frequency change rate is less than 1.5%, it is a low-producing well segment, and the value between the two corresponds to the medium-producing well segment. In summary, the study reduces the cost of reservoir evaluation and can meet the demand of gas well productivity prediction.