A high-resolution nonlinear inversion method of reservoir parameters and its application to oil/gas exploration

作者：Wu Mei^*; Fu Li-Yun; Li Wei-Xin

来源：Acta Geophysica Sinica, 2008, 51(2): 546-557.

• nonlinear inversion
• petrophysical parameters
• convolution model
• Calaniello neural network
• edge detection wavelet
• separate-frequency inversion

In the prediction of complex reservoir in continental deposition environment, because of inexact data (e. g., information-overlapping, information-incomplete, and noise-contaminated) and ambiguous physical relationship, inversion results suffer from nonuniqueness, instability, and uncertainty. Thus, reservoir prediction technologies based on linear assumption are unsuited for these complex areas. By means of nonlinear rock physical models, the method presented in the paper establishes a relationship between impedance and porosity/clay-content. Through multistage decomposition and bidirectional edge wavelet detection, it can depict more complex rock physical relationship. Moreover, it uses the Caianiello neural network to implement the combination of deterministic inversion, statistical inversion and nonlinear theory. Last, it incorporates geological information I well data and seismic impedance to perform petrophysical parameters inversion by combined applications of model-based and deconvolution-based methods. The joint inversion consists of four steps: (1) multistage vertical edge detection wavelets extraction at the wells and nonlinear factor estimation, (2) initial petrophysical parameters estimation by vertical edge detection wavelets, (3) multistage transverse edge detection wavelets extraction and nonlinear factor estimation, (4) final petrophysical parameters reconstruction by transverse edge detection wavelets. The scheme adopts multi-well constraint and separate-frequency inversion mode and achieves good results in the application on some continental and near-sea exploration data.

• 出版日期2008-3
• 单位中国科学院地质与地球物理研究所兰州油气资源研究中心; 中国科学院地质与地球物理研究所