Differential Spectrum-Based Adaptive Regularization for NMR T ₂ Inversion in Noisy Data

Area of Science:

• Geophysics
• Petroleum Engineering
• Geoscience

Background:

• Nuclear magnetic resonance (NMR) logging is crucial for reservoir characterization, evaluating formation porosity, fluid properties, and pore structure.
• Inversion of NMR echo signals is an ill-posed problem, susceptible to noise, which can lead to inaccurate porosity overestimation and misrepresentation of reservoir characteristics.
• Analysis of sandstone reservoirs in the Ordos Basin shows noise amplification causes divergence in differential spectrum tails and higher porosity estimates than core measurements.

Purpose of the Study:

• To develop and validate a locally adaptive regularization inversion method for improving NMR logging interpretation.
• To address the issue of noise amplification in NMR signal inversion, particularly in complex reservoirs.
• To enhance the accuracy of porosity estimation derived from NMR logging data.

Main Methods:

• A locally adaptive regularization inversion method was proposed, leveraging the divergence characteristics of differential spectrum tails.
• The method quantifies tail divergence to dynamically adjust regularization parameters, locally constraining abnormal spectral segments.
• The approach was tested on NMR logging data from wells in the Ordos Basin, including Well H*49 and Well H*141.

Main Results:

• The proposed method effectively suppresses nonphysical pseudopeaks in T2 spectra.
• Porosity accuracy was significantly improved, with a 57% average reduction in mean squared error (MSE) compared to core porosity in Well H*49.
• Validation in Well H*141 confirmed the method's adaptability and generalization capabilities in similar reservoir conditions.

Conclusions:

• The locally adaptive regularization inversion method offers a reliable solution for NMR logging interpretation in high-noise environments.
• The technique enhances the accuracy of porosity evaluation, crucial for understanding complex reservoir characteristics.
• This method holds practical value for the oil and gas industry, improving reservoir assessment and decision-making.