Obtaining estimates of permeability from NMR methods requires the ability to measure relaxation time parameters that are most sensitive to pore geometry. While surface-NMR (SNMR) measurements can reliably quantify the effective transverse relaxation time T2*, transverse relaxation times are sometimes dominated by the influence of magnetic geology. The longitudinal relaxation time T1 is known to provide a more robust link to pore geometry, but previous attempts to measure T1 by SNMR, have shown mixed success due to fundamental limitations the commonly applied “pseudo-saturation recovery” (PSR)experiment. Recognizing a need for improved measurements, we have developed a new paradigm of experiments for quantifying T1, which we refer to as the “crush recovery” (CR)sequence. The CR sequence utilizes two pulses: a first pulse of fixed amplitude and a second smaller pulse with an amplitude that is varied between measurements. The first pulse of the CR sequence is not intended to generate saturation with 90 degree tip angles, as in the PSR sequence; rather, the initial pulse acts to crush or decoherently scatter the magnetization orientation, leaving an effectively saturated condition of zero net longitudinal magnetization over a range of shallow to intermediate depths. A second pulse, applied after a short delay, is then used to probe the magnetization that recovers by T1 between the two pulses. The amplitude of the second pulse is varied to provide depth resolution of T1 recovery within the “crushed” zone. Fixing the amplitude of the initial pulse provides constant initial conditions, such that the data measured after the second pulse can be inverted using the standard SNMR imaging kernel. Inversion of a CR dataset for a single delay time yields depth-separated free induction decay signals where the amplitude of the signal components are linearly weighted by the observed T1 recovery at each depth. Given a complete set of CR data acquired with multiple delay times, the inversion provides estimates of both T1 and T2* as a function of depth. Further, the covariance of the observed signal with T2* and T1, provides the opportunity to estimate a two-dimensional distributions of T2* versus T1 at each depth. We illustrate the success and advantages of this new approach though a combination of synthetic and real field data examples.


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