A High‐Resolution Nonlinear Inversion Method of Reservoir Parameters and Its Application to Oil/Gas Exploration

Abstract

Abstract 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/claycontent. Through multistage decomposition and bidirectional edge wavelet detection, it can depict more complex petrophysical 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, well data and seismic impedance to perform petrophysical parameters inversion by combined applications of model‐based and deconvolution‐based methods. 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.