Real Time Data Inversion Uncertainty Estimation of NMR Logs

NMR log relaxation time distributions (T2 or T1) are essential for deriving petrophysical properties such as porosity and pore structure. However, their accuracy is often constrained by factors such as data quality, activation sequences, and inversion techniques. Traditional uncertainty estimates derived from regularized inversion methods fail to fully capture spectral uncertainties that are critical for accurate pore typing. This paper introduces a fast, Monte Carlo-based approach for estimating 'true' NMR spectral uncertainty in real time—independent of regularization effects. We also propose a computationally efficient workflow that enables comprehensive uncertainty analysis of NMR log data inversion in real-time settings. Additionally, our findings demonstrate that the choice of activation sequence has a more significant impact on interpretation accuracy than the selection of the pore typing method itself. A study using the proposed method highlights that selecting an appropriate NMR activation sequence is the most effective strategy to minimize interpretation errors in advanced pore typing, particularly in complex formations.