Petrophysically-Regularized NMR Inversion for Organic Shale
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Solution Overview
Problem
Nuclear magnetic resonance (NMR) logging faces challenges in characterizing organic shale reservoirs due to low sensitivity to short relaxation times, particularly in the range of T2 below 0.2-0.3 ms, leading to deficits in log T2 distributions and total porosity measurements, which affects the applicability of NMR for reservoir characterization.
Innovation Solution
Incorporating total porosity information into the CIPHER℠ time domain using stochastic inversion, allowing for the estimation of porobodon feature distributions that match observed pulse decay curves and petrophysical restrictions, thereby overcoming the short T2 challenge without relying on the first echo, and improving porosity characterization and matrix permeability evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If NMR logging is used to characterize organic shale reservoirs, then reservoir parameters can be measured, but low sensitivity to short relaxation times (T2 below 0.2-0.3 ms) causes deficits in log T2 distributions and total porosity measurements
Solution Approach 1:
The patent introduces an intermediary approach by using a neural network model that takes NMR pulse decay data as input and outputs corrected porosity and T2 distribution values. This neural network acts as a mediator that processes the limited NMR information and generates accurate reservoir parameters without requiring direct measurement of short T2 signals, thereby resolving the sensitivity deficiency while maintaining measurement accuracy.
Solution Approach 2:
The patent changes the parameter space from direct T2 domain analysis to a transformed parameter space using neural network predictions. By converting the NMR pulse decay curve into porosity and T2 distribution parameters through the neural network model, the method avoids the direct measurement problem of short T2 signals while achieving accurate reservoir characterization through parameter transformation.
2Reliability
If CIPHERSM time domain NMR inversion is used, then petrophysical restrictions can be applied, but total porosity information is not included as a factor guiding inversion
Solution Approach 1:
The patent merges the CIPHERSM inversion framework with total porosity information by integrating porosity constraints into the neural network training process. The neural network model combines petrophysical restrictions from CIPHERSM with total porosity data from NMR logs, creating a unified inversion approach that simultaneously honors both petrophysical constraints and porosity measurements, thereby eliminating information loss while maintaining reliability.
Solution Approach 2:
The patent makes the neural network model universal by enabling it to handle multiple input types (NMR pulse decay data, total porosity information, petrophysical restrictions) and produce multiple output parameters (porosity, T2 distribution, permeability). This multi-functional approach allows the same model to incorporate both CIPHERSM restrictions and porosity guidance, eliminating the limitation of excluding total porosity information while maintaining inversion reliability.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables the generation of high-quality total porosity logs and enhances the characterization of porosity partition occupied by short relaxation times, maintaining accuracy even in the short T2 range, thus improving the characterization of organic shale reservoirs.
Implementation Method 1
Nuclear magnetic resonance ('NMR') logging is the primary method in the industry to characterize these reservoir parameters
Data Source
AI summary
A method for generating a porosity log for a reservoir in an organic shale. The method includes receiving data representing one or more parameters in a reservoir in an organic shale. At least one of the parameters includes porosity. By stochastically inverting the data, a distribution of porobodon features is estimated that matches an observed pulse decay curve. The porosity data relates to petrophysical restrictions on at least one of the porobodon features.