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Applications of seismic inversions strongly depend on inversion methods, data quality, and reservoir complexity. An advanced inversion scheme to integrate seismic data, well data, and geological knowledge is employed by combining statistical Caianiello convolutional networks with a hierarchical seismic convolutional model for impedance estimation and with nonlinear petrophysical models for porosity and clay-content inversions. The method used to measure the reliability of seismic inversions for different geological complexities is important for reservoir characterisation. The widely used cross-validation may not be the best for the evaluation of the reliability of seismic inversions because of different geological conditions away from wells. As a supplementary means and also to understand failed cross-validations, we propose a systematic methodology to measure the reliability of seismic inversions through prior seismic-to-well correlation analyses for the fidelity of seismic data. The resulting correlation coefficients at the main frequencies of seismic data may express what degree the seismic data reflect the subsurface reliably in both amplitude and phase. First, the low-cut filtered borehole impedance logs are correlated with the seismic relative impedance traces computed by trace integration of seismic traces at wells. The resulting correlation coefficients within the seismic frequency band could be an index with which to evaluate the reliability of seismic inversions for impedance estimation. Second, the correlation between borehole impedance and porosity/clay-content is analysed by measuring the overall trend across the cloud of data points in the logging-databased cross-plot. The resulting correlation coefficients could be used to evaluate the reliability of mapping seismic impedance to porosity/clay content. Case studies from several oilfields across China show that the prior seismic-to-well correlation analyses are an excellent way to test the reliability of seismic inversions before the implementation of inversions.