Learnings from Impact and Implications of Signal-to-Noise in NMR T1-T2 Logging of Unconventional Reservoirs

Data quality and signal-to-noise ratio (SNR) in NMR well logging are dependent on various factors such as logging tool, porosity available for low-field NMR relaxation, bulk fluid wait time, logging speed, echo stacking level, rock heterogeneity, and background noise due to tool movement. As SNR decreases, inversion of NMR relaxation spectra becomes challenging due to ill-conditioning and tradeoffs between solution existence, bias/uniqueness, and stability. These tradeoffs inevitably lead to over-regularization that causes the broadening and smearing of relaxation peaks. In unconventional reservoirs, because of micropore to nanopore sizes and low porosities (5 to 15%), poor SNR, over-regularization, and smeared T1-T2 peaks are very common. These impact interpretation of fluid saturations with adverse implications for hydrocarbon volume estimates. In this paper, we introduce a novel semi-analytical technique to compensate for the over-regularized smearing of T1-T2 relaxation peaks due to poor SNR in unconventional reservoirs. A common notion in T1-T2 fluid partitioning methods is that a fluid type exhibits a continuous footprint in T1-T2 space. This can be true when SNR is adequate to give distinct fluid relaxation in T1-T2 space, e.g., in benchtop NMR of core samples, or in conventional reservoirs. However, in borehole NMR T1-T2 maps acquired in unconventionals, we observe that a continuous relaxation spectra footprint can be a combination of different fluid types because the fluid peaks smear together. Therefore, we introduce a novel NMR semi-analytical smeared-peak (NMR-SASP) prediction technique to correct for the effects of over-regularization in T1-T2 space and reconstruct original pore volumes of the different poro-fluid types. The NMR-SASP correction assumes that the smearing effect approximates a pore-volume-weighted geometric average. We validate the NMR-SASP model with relevant numerical experiments and field measurements in unconventional reservoirs. For different borehole logging tool types (single frequency vs. multifrequency tools), acquisition modes (stationary vs. moving-pass logging), logging speeds, and stacking levels that replicate varying SNRs, we observe a gradual degradation in T1-T2 map resolution as SNR worsens, i.e., as over-regularization intensifies (example in Fig. 1). The NMR-SASP technique serves as a subsurface calibration scheme that uses high-SNR stationary measurements to correct or de-smear poor-SNR moving-pass measurements. Consequently, this improves accuracy in fluid pore volume predictions by up to 60% (refer to Fig. 2). The learnings show that logging protocols that combine specific acquisition parameters and processing strategies with acceptable compromises and are designed to increase SNR are mandatory for the reliable characterization of unconventional reservoirs, e.g., slower logging speeds, subsurface stationary measurements for benchmark calibration, and appropriate stacking levels. Furthermore, with the novel NMR-SASP technique introduced in this paper, we mitigated the impact of smeared T1-T2 fluid modes and improved the reliability of fluid saturation predictions from moving-pass NMR measurements acquired in unconventional reservoirs.