A small-diameter NMR logging tool for groundwater investigations

Borehole Nuclear Magnetic Resonance Estimation of Specific Yield in a Fractured Granite Aquifer

In this study, we introduce a novel field-based method to estimate specific yield (Sy ) in fractured, low-porosity granite aquifers using borehole nuclear magnetic resonance (bNMR). This method requires collecting a bNMR survey immediately following a pump test, which dewaters the near-borehole fractures. The residual water content measured from bNMR is interpreted as "bound" and represents the specific retention (Sr ) while the water drained by the pump is the Sy . The transverse relaxation cutoff time (T2C ) is the length of time that partitions the total porosity measured by bNMR into Sr and Sy . When applying a calibrated T2C , Sy equals the bNMR total porosity minus Sr ; thus, a calibrated T2C is required to determine Sy directly from NMR results. Based on laboratory experiments on sandstone cores, the default T2C is 33 ms; however, its applicability to fractured granite aquifers is uncertain. The optimal T2C based on our pumping test is 110 ± 25 ms. Applying this calibrated T2C on a saturated, A-type granite at our field site, we estimate the Sy to be 0.012 ± 0.005 m3  m-3 which is significantly different from the Sy (0.021 ± 0.005 m3  m-3 ) estimate using the default T2C of 33 ms. This Sy estimate falls within a range determined using traditional hydraulic testing at the same site. Using the conventional T2C (33 ms) for fractured granite leads to an inaccurate Sy ; therefore, it is essential to calibrate the bNMR T2C for the local site conditions prior to estimating Sy .

Evaluation of Models for Estimating Hydraulic Conductivity in Glacial Aquifers with NMR Logging

Nuclear magnetic resonance (NMR) logging is a promising method for estimating hydraulic conductivity (K). During the past ∼60 years, NMR logging has been used for petroleum applications, and different models have been developed for deriving estimates of permeability. These models involve calibration parameters whose values were determined through decades of research on sandstones and carbonates. We assessed the use of five models to derive estimates of K in glacial aquifers from NMR logging data acquired in two wells at each of two field sites in central Wisconsin, USA. Measurements of K, obtained with a direct push permeameter (DPP), KDPP , were used to obtain the calibration parameters in the Schlumberger-Doll Research, Seevers, Timur-Coates, Kozeny-Godefroy, and sum-of-echoes (SOE) models so as to predict K from the NMR data; and were also used to assess the ability of the models to predict KDPP . We obtained four well-scale calibration parameter values for each model using the NMR and DPP measurements in each well; and one study-scale parameter value for each model by using all data. The SOE model achieved an agreement with KDPP that matched or exceeded that of the other models. The Timur-Coates estimates of K were found to be substantially different from KDPP . Although the well-scale parameter values for the Schlumberger-Doll, Seevers, and SOE models were found to vary by less than a factor of 2, more research is needed to confirm their general applicability so that site-specific calibration is not required to obtain accurate estimates of K from NMR logging data.

Applied Geophysics for Managed Aquifer Recharge

Increasing water stress and decreasing supplies caused by growth and climate variability have expanded demand for managed aquifer recharge (MAR) projects to provide water supply resilience. Some of the most important factors in determining the performance of a MAR project include site selection, subsurface hydrogeologic characteristics and associated properties of the storage zone. Costs for invasive subsurface investigations to address these factors have slowly increased over the past two decades, with drilling costs increasing dramatically by as much as 30% or more since COVID-19 hit, a result of supply chain issues, steel prices, and manpower challenges. This paper provides a high-level review of major geophysical methods that have become more mainstream over the past decade or two to supplement invasive subsurface investigations and are very cost effective when compared to drilling boreholes and installing wells, which provide only point data. The more commonly used surface geophysical methods include ground-based and airborne time-domain electromagnetic methods (TEM), electrical resistivity, and seismic reflection. Airborne TEM methods (AEM) collect data very quickly, avoiding ground-based access constraints, and land-based methods are especially efficient using towed arrays. Electrical resistivity measurements provide resolution comparable to TEM but require more time than towed methods. Seismic reflection surveys are more expensive than other methods but typically have a much greater depth of penetration and can provide high resolution information on aquifer geometry, geology, and faults. Borehole geophysics is one of the more common methods used in MAR, providing near hole formation data and ground truths surface geophysics.

Hydraulic Conductivity from Nuclear Magnetic Resonance Logs in Sediments with Elevated Magnetic Susceptibilities

This study examined the application of slim-hole nuclear magnetic resonance (NMR) tools to estimate hydraulic conductivity (K_NMR ) in an unconsolidated aquifer that contains a range of grain sizes (silt to gravel) and high and variable magnetic susceptibilities (MS) (10^-4 to 10^-2 SI). A K calibration dataset was acquired at 1-m intervals in three fully screened wells, and compared to K_NMR estimates using the Schlumberger-Doll research (SDR) equation with published empirical constants developed from previous studies in unconsolidated sediments. While K_NMR using published constants was within an order of magnitude of K, the agreement, overprediction, or underprediction of K_NMR varied with the MS distribution in each well. An examination of the effects of MS on NMR data and site-specific empirical constants indicated that the exponent on T_2ML (n-value in the SDR equation, representing the diffusion regime) was found to have the greatest influence on K_NMR estimation accuracy, while NMR porosity did not improve the prediction of K. K_NMR was further improved by integrating an MS log into the NMR analyses. A first approach detrended T_2ML for the effects of MS prior to calculating K_NMR , and a second approach introduced an MS term into the SDR equation. Both were found to produce similar refinements of K_NMR in intervals of elevated MS. This study found that low frequency NMR logging with short echo times shows promise for sites with moderate to elevated MS levels, and recommends a workflow that examines parameter relationships and integrates MS logs into the estimation of K_NMR .

Quantifying motional dynamics in nuclear magnetic resonance logging

The motional dynamics of nuclear magnetic resonance (NMR) logging tools can significantly influence the measurement performance of such tools. NMR logging is used for geophysical evaluation in geological environments, primarily quantifying formation porosity and fluid volumes, as well as providing a qualitative estimation of permeability. NMR logging tools are conveyed via two main mechanisms; wireline logging and logging while drilling (LWD). We conduct detailed simulations to quantify the impact of tool motion on NMR measurements during logging. This involves conducting electromagnetic simulations which quantify the magnetic fields generated by a logging tool, and subsequently introducing motion profiles within the relevant spin dynamic calculations. This enables tool motional dynamics to be imposed on the signal acquisition. Several movement profiles are considered: linear axial movement to replicate wireline logging tool motion, as well as axial harmonic and lateral harmonic movement to simulate the shocks and vibrations experienced during logging while drilling. Lateral motion is observed to cause a greater degree of signal attenuation relative to axial motion due to the cylindrical shape of the excited volume. The magnitude of motion (e.g. the velocity of linear motion or the amplitude of harmonic motion) is demonstrated to increase the severity of signal attenuation, as expected. However, the frequency of harmonic motion demonstrates a more complex effect on the measured signal. The harmonic interaction between the motion frequency and measurement frequency (determined by the echo spacing) can cause wave interference which results in enhanced or diminished signal attenuation. Finally, we demonstrate that reducing both the magnetic field gradient as well as the echo spacing reduce the degree of signal attenuation observed during measurement. The results presented in this work demonstrate how the optimisation of key design parameters can be used to control the sensitivity of NMR logging tools towards motion.

Surface parameters and bedrock properties covary across a mountainous watershed: Insights from machine learning and geophysics

Bedrock property quantification is critical for predicting the hydrological response of watersheds to climate disturbances. Estimating bedrock hydraulic properties over watershed scales is inherently difficult, particularly in fracture-dominated regions. Our analysis tests the covariability of above- and belowground features on a watershed scale, by linking borehole geophysical data, near-surface geophysics, and remote sensing data. We use machine learning to quantify the relationships between bedrock geophysical/hydrological properties and geomorphological/vegetation indices and show that machine learning relationships can estimate most of their covariability. Although we can predict the electrical resistivity variation across the watershed, regions of lower variability in the input parameters are shown to provide better estimates, indicating a limitation of commonly applied geomorphological models. Our results emphasize that such an integrated approach can be used to derive detailed bedrock characteristics, allowing for identification of small-scale variations across an entire watershed that may be critical to assess the impact of disturbances on hydrological systems.

Distribution Model of Fluid Components and Quantitative Calculation of Movable Oil in Inter-Salt Shale Using 2D NMR

Some inter-salt shale reservoirs have high oil saturations but the soluble salts in their complex lithology pose considerable challenges to their production. Low-field nuclear magnetic resonance (NMR) has been widely used in evaluating physical properties, fluid characteristics, and fluid saturation of conventional oil and gas reservoirs as well as common shale reservoirs. However, the fluid distribution analysis and fluid saturation calculations in inter-salt shale based on NMR results have not been investigated because of existing technical difficulties. Herein, to explore the fluid distribution patterns and movable oil saturation of the inter-salt shale, a specific experimental scheme was designed which is based on the joint adaptation of multi-state saturation, multi-temperature heating, and NMR measurements. This novel approach was applied to the inter-salt shale core samples from the Qianjiang Sag of the Jianghan Basin in China. The experiments were conducted using two sets of inter-salt shale samples, namely cylindrical and powder samples. Additionally, by comparing the one-dimensional (1D) and two-dimensional (2D) NMR results of these samples in oil-saturated and octamethylcyclotetrasiloxane-saturated states, the distributions of free movable oil and water were obtained. Meanwhile, the distributions of the free residual oil, adsorbed oil, and kerogen in the samples were obtained by comparing the 2D NMR T1-T2 maps of the original samples with the sample heated to five different temperatures of 80, 200, 350, 450, and 600 °C. This research puts forward a 2D NMR identification graph for fluid components in the inter-salt shale reservoirs. Our experimental scheme effectively solves the problems of fluid composition distribution and movable oil saturation calculation in the study area, which is of notable importance for subsequent exploration and production practices.

Innovative Hydrogeophysical Approaches as Aids to Assess Hungarian Groundwater Bodies

The Hungarian water management plan has lately identified 185 groundwater bodies based on the concepts given by the European Water Framework Directive. Achieving and maintaining the good quantitative and chemical status of these groundwater bodies is of primary importance. It is demonstrated how innovative hydrogeophysical methods can be applied successfully to assess the Hungarian or other international groundwater bodies. By applying geoelectric methods, horizontal layering or large uniform rock units can be well characterized by Wenner–Schlumberger array, also enabling accurate depth determination of the shallow groundwater table. Horizontal variations in the rock type or its state can be well described by dipole–dipole array or, even better, by the newly developed quasi-null arrays. Their joint application may be very straightforward to investigate different aquifer types by giving high-resolution resistivity images as input for hydrogeological modeling. In the identification of porous formations, multivariate statistical interpretation of wireline logs using cluster analysis allows reliable lithological separation of potential aquifers. Their clay content is estimated by robust factor analysis, while their hydraulic properties are directly derived from the resistivity log. For a more effective interpretation, a combination of surface and borehole geophysical methods can be recommended for meeting challenges in hydrogeology and groundwater management.

Assessment of NMR Logging for Estimating Hydraulic Conductivity in Glacial Aquifers

Glacial aquifers are an important source of groundwater in the United States and require accurate characterization to make informed management decisions. One parameter that is crucial for understanding the movement of groundwater is hydraulic conductivity, K. Nuclear magnetic resonance (NMR) logging measures the NMR response associated with the water in geological materials. By utilizing an external magnetic field to manipulate the nuclear spins associated with ¹ H, the time-varying decay of the nuclear magnetization is measured. This logging method could provide an effective way to estimate K at submeter vertical resolution, but the models that relate NMR measurements to K require calibration. At two field sites in a glacial aquifer in central Wisconsin, we collected a total of four NMR logs and obtained measurements of K in their immediate vicinity with a direct-push permeameter (DPP). Using a bootstrap algorithm to calibrate the Schlumberger-Doll Research (SDR) NMR-K model, we estimated K to within a factor of 5 of the DPP measurements. The lowest levels of accuracy occurred in the lower-K (K < 10^-4  m/s) intervals. We also evaluated the applicability of prior SDR model calibrations. We found the NMR calibration parameters varied with K, suggesting the SDR model does not incorporate all the properties of the pore space that control K. Thus, the expected range of K in an aquifer may need to be considered during calibration of NMR-K models. This study is the first step toward establishing NMR logging as an effective method for estimating K in glacial aquifers.

Groundwater flow velocities in a fractured carbonate aquifer-type: Implications for contaminant transport

Contaminants that are highly soluble in groundwater are rapidly transported via fractures in mechanically resistant sedimentary rock aquifers. Hence, a rigorous methodology is needed to estimate groundwater flow velocities in such fractured aquifers. Here, we propose an approach using borehole hydraulic testing to compute flow velocities in an un-faulted area of a fractured carbonate aquifer by applying the cubic law to a parallel plate model. The Cadeby Formation (Yorkshire, NE England) - a Permian dolostone aquifer present beneath the University of Leeds Farm - is the fractured aquifer selected for this hydraulic experiment. The bedding plane fractures of this dolostone aquifer, which are sub-horizontal, sub-parallel and laterally persistent, largely dominate the flow at shallow (<~40 mBGL) depths. These flowing bedding plane discontinuities are separated by a rock matrix which is relatively impermeable (K_well-test/K_core-plug~10⁴) as is common in fractured carbonate aquifers. In the workflow reported here, the number of flowing fractures - mainly bedding plane fractures - intersecting three open monitoring wells are found from temperature/fluid conductivity and acoustic/optical televiewer logging. Following well installation, average fracture hydraulic apertures for screened intervals are found from analysis of slug tests. For the case study aquifer, this workflow predicts hydraulic apertures ranging from 0.10 up to 0.54 mm. However, groundwater flow velocities range within two order of magnitude from 13 up to 242 m/day. Notably, fracture apertures and flow velocities rapidly reduce with increasing depth below the water table; the upper ~10 m shows relatively high values of hydraulic conductivity (0.30-2.85 m/day) and corresponding flow velocity (33-242 m/day). Permeability development around the water table in carbonate aquifer-types is common, and arises where high pCO₂ recharge water from the soil zone causes calcite/dolomite dissolution. Hence, agricultural contaminants entering the aquifer with recharge water are laterally transported rapidly within this upper part. Computation of groundwater flow velocities allows determination of the Reynolds number. Values of up ~1, indicating the lower limit of the transition from laminar to turbulent flow, are found at the studied site, which is situated away from major fault traces. Hence, turbulent flow is likely to arise in proximity to tectonic structures, such as normal faults, which localize flow and enhance karstification. The occurrence of turbulent flow in correspondence of such tectonic structures should be represented in regional groundwater flow simulations.

Hydraulic Conductivity Calibration of Logging NMR in a Granite Aquifer, Laramie Range, Wyoming

In granite aquifers, fractures can provide both storage volume and conduits for groundwater. Characterization of fracture hydraulic conductivity (K) in such aquifers is important for predicting flow rate and calibrating models. Nuclear magnetic resonance (NMR) well logging is a method to quickly obtain near-borehole hydraulic conductivity (i.e., K_NMR ) at high-vertical resolution. On the other hand, FLUTe flexible liner technology can produce a K profile at comparable resolution but requires a fluid driving force between borehole and formation. For three boreholes completed in a fractured granite, we jointly interpreted logging NMR data and FLUTe K estimates to calibrate an empirical equation for translating borehole NMR data to K estimates. For over 90% of the depth intervals investigated from these boreholes, the estimated K_NMR are within one order of magnitude of K_FLUTe . The empirical parameters obtained from calibrating the NMR data suggest that "intermediate diffusion" and/or "slow diffusion" during the NMR relaxation time may occur in the flowing fractures when hydraulic aperture are sufficiently large. For each borehole, "intermediate diffusion" dominates the relaxation time, therefore assuming "fast diffusion" in the interpretation of NMR data from fractured rock may lead to inaccurate K_NMR estimates. We also compare calibrations using inexpensive slug tests that suggest reliable K_NMR estimates for fractured rock may be achieved using limited calibration against borehole hydraulic measurements.

Detecting Microbially Induced Calcite Precipitation in a Model Well-Bore Using Downhole Low-Field NMR

Microbially induced calcite precipitation (MICP) has been widely researched recently due to its relevance for subsurface engineering applications including sealing leakage pathways and permeability modification. These applications of MICP are inherently difficult to monitor nondestructively in time and space. Nuclear magnetic resonance (NMR) can characterize the pore size distributions, porosity, and permeability of subsurface formations. This investigation used a low-field NMR well-logging probe to monitor MICP in a sand-filled bioreactor, measuring NMR signal amplitude and T₂ relaxation over an 8 day experimental period. Following inoculation with the ureolytic bacteria, Sporosarcina pasteurii, and pulsed injections of urea and calcium substrate, the NMR measured water content in the reactor decreased to 76% of its initial value. T₂ relaxation distributions bifurcated from a single mode centered about approximately 650 ms into a fast decaying population (T₂ less than 10 ms) and a larger population with T₂ greater than 1000 ms. The combination of changes in pore volume and surface minerology accounts for the changes in the T₂ distributions. Destructive sampling confirmed final porosity was approximately 88% of the original value. These results indicate the low-field NMR well-logging probe is sensitive to the physical and chemical changes caused by MICP in a laboratory bioreactor.

Evaluation of sandstone surface relaxivity using laser-induced breakdown spectroscopy

Nuclear magnetic resonance (NMR) relaxometry is a common technique used to assess the pore size of fluid-filled porous materials in a wide variety of fields. However, the NMR signal itself only provides a relative distribution of pore size. To calculate an absolute pore size distribution from the NMR data, the material's surface relaxivity needs to be known. Here, a method is presented using laser-induced breakdown spectroscopy (LIBS) to evaluate surface relaxivity in sandstones. NMR transverse and longitudinal relaxation was measured on a set of sandstone samples and the surface relaxivity was calculated from the pore size distribution determined with MICP measurements. Using multivariate analysis, it was determined that the LIBS data can predict with good accuracy the longitudinal (R²∼0.84) and transverse (R²∼0.79) surface relaxivity. Analysis of the regression coefficients shows significant influence from several elements. Some of these are elements previously established to have an effect on surface relaxivity, such as iron and manganese, while others are not commonly associated with surface relaxivity, such as cobalt and titanium. Furthermore, LIBS provides advantages compared to current methods to calibrate surface relaxivity in terms of speed, portability, and sample size requirements. While this paper focuses on geological samples, the method could potentially be expanded to other types of porous materials.

NMR Logging to Estimate Hydraulic Conductivity in Unconsolidated Aquifers

Nuclear magnetic resonance (NMR) logging provides a new means of estimating the hydraulic conductivity (K) of unconsolidated aquifers. The estimation of K from the measured NMR parameters can be performed using the Schlumberger-Doll Research (SDR) equation, which is based on the Kozeny-Carman equation and initially developed for obtaining permeability from NMR logging in petroleum reservoirs. The SDR equation includes empirically determined constants. Decades of research for petroleum applications have resulted in standard values for these constants that can provide accurate estimates of permeability in consolidated formations. The question we asked: Can standard values for the constants be defined for hydrogeologic applications that would yield accurate estimates of K in unconsolidated aquifers? Working at 10 locations at three field sites in Kansas and Washington, USA, we acquired NMR and K data using direct-push methods over a 10- to 20-m depth interval in the shallow subsurface. Analysis of pairs of NMR and K data revealed that we could dramatically improve K estimates by replacing the standard petroleum constants with new constants, optimal for estimating K in the unconsolidated materials at the field sites. Most significant was the finding that there was little change in the SDR constants between sites. This suggests that we can define a new set of constants that can be used to obtain high resolution, cost-effective estimates of K from NMR logging in unconsolidated aquifers. This significant result has the potential to change dramatically the approach to determining K for hydrogeologic applications.

In Situ Detection of Subsurface Biofilm Using Low-Field NMR: A Field Study

Subsurface biofilms are central to bioremediation of chemical contaminants in soil and groundwater whereby micro-organisms degrade or sequester environmental pollutants like nitrate, hydrocarbons, chlorinated solvents and heavy metals. Current methods to monitor subsurface biofilm growth in situ are indirect. Previous laboratory research conducted at MSU has indicated that low-field nuclear magnetic resonance (NMR) is sensitive to biofilm growth in porous media, where biofilm contributes a polymer gel-like phase and enhances T2 relaxation. Here we show that a small diameter NMR well logging tool can detect biofilm accumulation in the subsurface using the change in T2 relaxation behavior over time. T2 relaxation distributions were measured over an 18 day experimental period by two NMR probes, operating at approximately 275 kHz and 400 kHz, installed in 10.2 cm wells in an engineered field testing site. The mean log T2 relaxation times were reduced by 62% and 43%, respectively, while biofilm was cultivated in the soil surrounding each well. Biofilm growth was confirmed by bleaching and flushing the wells and observing the NMR signal's return to baseline. This result provides a direct and noninvasive method to spatiotemporally monitor biofilm accumulation in the subsurface.

Bootstrap calibration and uncertainty estimation of downhole NMR hydraulic conductivity estimates in an unconsolidated aquifer

Characterization of hydraulic conductivity (K) in aquifers is critical for evaluation, management, and remediation of groundwater resources. While estimates of K have been traditionally obtained using hydraulic tests over discrete intervals in wells, geophysical measurements are emerging as an alternative way to estimate this parameter. Nuclear magnetic resonance (NMR) logging, a technology once largely applied to characterization of deep consolidated rock petroleum reservoirs, is beginning to see use in near-surface unconsolidated aquifers. Using a well-known rock physics relationship-the Schlumberger Doll Research (SDR) equation--K and porosity can be estimated from NMR water content and relaxation time. Calibration of SDR parameters is necessary for this transformation because NMR relaxation properties are, in part, a function of magnetic mineralization and pore space geometry, which are locally variable quantities. Here, we present a statistically based method for calibrating SDR parameters that establishes a range for the estimated parameters and simultaneously estimates the uncertainty of the resulting K values. We used co-located logging NMR and direct K measurements in an unconsolidated fluvial aquifer in Lawrence, Kansas, USA to demonstrate that K can be estimated using logging NMR to a similar level of uncertainty as with traditional direct hydraulic measurements in unconsolidated sediments under field conditions. Results of this study provide a benchmark for future calibrations of NMR to obtain K in unconsolidated sediments and suggest a method for evaluating uncertainty in both K and SDR parameter values.