Saline tracer visualized with three‐dimensional electrical resistivity tomography: Field‐scale spatial moment analysis

A joint research project between Statoil and Schlumberger is focusing on permanent cross-well geophysical methods for reservoir monitoring during steam assisted gravity drainage (SAGD). A feasibility study indicated detectable differences in seismic and electrical reservoir properties based on expected changes in temperature and fluid saturation during oil production. Based on these results, a permanent cross-well system was installed at Statoil’s Leismer Demonstration Area (LDA) in Alberta, Canada. Baseline datasets, including cross-well seismic, 3D vertical seismic profiling (VSP) and cross-well electrical resistivity tomography (ERT), have been acquired. Comparisons between conventional surface seismic and downhole seismic data show an increase in resolution and frequency content as expected. The ERT baseline shows clear separation between zones of high and low electrical resistivity. Several time-lapse studies have been carried out over the last year, and production-induced time-lapse effects are observed in the reservoir section, both for the time-lapse 3D VSP and ERT datasets. Continuous remote ERT monitoring and annual 3D VSP acquisitions will give the potential of each data type as monitoring tools for oil sand reservoir, as well as possibilities for integrating acoustic and electric data.

We conducted a field experiment at the agricultural field site Voulund within the Danish hydrological observatory, HOBE, with the purpose of estimating recharge using geophysical methods. In September 2011, a saline tracer was added across a 142‐m2 area at the surface at an application rate mimicking natural infiltration. The movement of the saline tracer front was monitored using cross‐borehole electrical resistivity tomography (ERT); data were collected on a daily to weekly basis and continued for 1 yr after tracer application. The ERT data were inverted and corrected for temperature changes in the subsurface, and spatial moment analysis was used to calculate the tracer mass, position of the center of mass, and thereby the downwardly recharging flux. The recovered mass was underestimated by the ERT data by up to 50%. Mass balance errors are widely recognized and are a result of variable resolution of the tomographic models and smoothing applied in the inversion routine. The results were nonetheless in very good agreement with pore water samples collected and analyzed from five cores extracted within the tracer application area during the same period. Recharge during the 7.5 mo from September 2011 to the end of April 2012 was estimated to be about 500 mm using the ERT data. This value is in good accord with recharge estimates made based on drainage data from buried lysimeters located only meters away from the cross‐borehole ERT array. This suggests that long‐term automated ERT monitoring of a surface‐applied tracer is a promising technique for estimating groundwater recharge.

Electrical resistivity tomography (ERT) is a non-destructive geophysical method that provides images of the soil electrical resistivity. In particular, this study focuses on small-scale soil subsurface areas (up to 1 m deep). Soil resistivity depends on several factors, particularly soil water content. Consequently, ERT has proven to be effective in detecting water flow pathways during infiltration tests. ERT has already been used for hydroecological and hydrogeological applications, but its use still requires further investigation and new developments, particularly for specific processes such as preferential flows. Indeed, water infiltration into the soil is all but homogeneous with the occurrence of preferential flows due to several factors (lithological heterogeneity, macropores, cracks, macrofauna galleries, and root channels). The presence of these preferential flows promotes the downward movement of water and solutes to deeper soil horizons. Thus, the detection of these flows using geophysical methods, such as ERT, should provide a better understanding of the dynamics of preferential flows. However, conventional ERT inversion methods have proven unable to provide insight into the processes at adequate scales (e.g., the macropore scale) and have shown difficulties in detecting sharp variations because of smoothing constraints. Information from apparent resistivity may be lost because of these constraints.To overcome these limitations, an inversion method based on Convolutional Neural Networks (CNNs) is proposed to detect small-scale resistivity heterogeneities. For the training step, we designed a specific generator to produce synthetic resistivity data for various 2D random electrical resistivity distributions mimicking different types of soil heterogeneity (earthworm and root induced macropore, layering, etc.) and for typical protocols for ERT acquisition. For each case, our database associates the true resistivity field with the apparent resistivity. This database contains a large number of training pairs that allow machine learning.The preliminary results show that while some predictions were able to predict properly the soil heterogeneities (shape and size), the values of the estimated true resistivity were far from the target values. To avoid unrealistic estimates, we added physical constraints during the training process. An additional forward calculation was performed based on the true resistivities predicted by the neural network, then the apparent resistivities corresponding to the predicted resistivities were compared to the apparent resistivities corresponding to the targeted true resistivities and a supplementary loss function was added to the initial loss function. At this stage, the neural networks were trained on heterogeneous resistivity distributions in the soil with neither time evolution nor link to hydrological processes. In the future, the resistivity generator will be coupled with hydrological models to simulate water infiltration dynamics and changes in soil water content over time. This perspective is essential for applying the proposed method to detect flow pathways during infiltration tests.

Two important mechanisms affect our ability to estimate solute concentrations quantitatively from the inversion of field-scale electrical resistivity tomography (ERT) data: (1) the spatially variable physical processes that govern the flow of current as well as the variation of physical properties in space and (2) the overparameterization of inverse models, which requires the imposition of a smoothing constraint (regularization) to facilitate convergence of the inverse solution. Based on analyses of field and synthetic data, we find that the ability of ERT to recover the 3D shape and magnitudes of a migrating conductive target is spatially variable. Additionally, the application of Archie's law to tomograms from field ERT data produced solute concentrations that are consistently less than 10% of point measurements collected in the field and estimated from transport modeling. Estimates of concentration from ERT using Archie's law only fit measured solute concentrations if the apparent formation factor is varied with space and time and allowed to take on unreasonably high values. Our analysis suggests that the inability to find a single petrophysical relation in space and time between concentration and electrical resistivity is largely an effect of two properties of ERT surveys: (1) decreased sensitivity of ERT to detect the target plume with increasing distance from the electrodes and (2) the smoothing imprint of regularization used in inversion.

Improving understanding of Arctic ecosystem climate feedback and parameterization of models that simulate freeze-thaw dynamics require advances in quantifying soil and snow properties. Due to the significant spatiotemporal variability of soil properties and the limited information provided by point-scale measurements (e.g., cores), geophysical methods hold potential for improving soil and permafrost characterization. In this study, we evaluate the use of a ground-penetrating radar (GPR) to estimate thaw layer thickness, snow depth, and ice-wedge characteristics in an ice-wedge-dominated tundra region near Barrow, AK, USA. To this end, we analyze GPR and point-scale measurements collected along several parallel transects at the end of the growing season and the end of frozen season. In addition, we compare the structural information extracted from the GPR data with electrical resistivity tomography (ERT) information about ice-wedge characteristics. Our study generally highlights the value of GPR data collected in the frozen season, when conditions lead to the improved GPR signal-to-noise ratio, facilitate data acquisition, and reduce acquisition-related ecosystem disturbance relative to growing season. We document for the first time that GPR data collected during the frozen season can provide reliable estimates of active layer thickness and geometry of ice wedges. We find that the ice-wedge geometry extracted from GPR data collected during the frozen season is consistent with ERT data, and that GPR data can be used to constrain the ERT inversion. Consistent with recent studies, we also find that GPR data collected during the frozen season can provide good estimates of snow thickness, and that GPR data collected during the growing season can provide reliable estimate thaw depth. Our quantification of the value of the GPR and ERT data collected during growing and frozen seasons paves the way for coupled inversion of the datasets to improve understanding of permafrost variability.

The complex ecohydrological processes of rangelands can be studied through the framework of ecological sites (ESs) or hillslope‐scale soil–vegetation complexes. High‐quality hydrologic field investigations are needed to quantitatively link ES characteristics to hydrologic function. Geophysical tools are useful in this context because they provide valuable information about the subsurface at appropriate spatial scales. We conducted 20 field experiments in which we deployed time‐lapse electrical resistivity tomography (ERT), variable intensity rainfall simulation, ground‐penetrating radar (GPR), and seismic refraction, on hillslope plots at five different ESs within the Upper Crow Creek Watershed in south‐east Wyoming. Surface runoff was measured using a precalibrated flume. Infiltration data from the rainfall simulations, coupled with site‐specific resistivity–water content relationships and ERT datasets, were used to spatially and temporally track the progression of the wetting front. First‐order constraints on subsurface structure were made at each ES using the geophysical methods. Sites ranged from infiltrating 100% of applied rainfall to infiltrating less than 60%. Analysis of covariance results indicated significant differences in the rate of wetting front progression, ranging from 0.346 m min−1/2 for sites with a subsurface dominated by saprolitic material to 0.156 m min−1/2 for sites with a well‐developed soil profile. There was broad agreement in subsurface structure between the geophysical methods with GPR typically providing the most detail. Joint interpretation of the geophysics showed that subsurface features such as soil layer thickness and the location of subsurface obstructions such as granite corestones and material boundaries had a large effect on the rate of infiltration and subsurface flow processes. These features identified through the geophysics varied significantly by ES. By linking surface hydrologic information from the rainfall simulations with subsurface information provided by the geophysics, we can characterize the ES‐specific hydrologic response. Both surface and subsurface flow processes differed among sites and are directly linked to measured characteristics.

In recent years geophysical methods have become increasingly popular for hydrological applications. Time‐lapse electrical resistivity tomography (ERT) represents a potentially powerful tool for subsurface solute transport characterization since a full picture of the spatiotemporal evolution of the process can be obtained. However, the quantitative interpretation of tracer tests is difficult because of the uncertainty related to the geoelectrical inversion, the constitutive models linking geophysical and hydrological quantities, and the a priori unknown heterogeneous properties of natural formations. Here an approach based on the Lagrangian formulation of transport and the ensemble Kalman filter (EnKF) data assimilation technique is applied to assess the spatial distribution of hydraulic conductivity K by incorporating time‐lapse cross‐hole ERT data. Electrical data consist of three‐dimensional cross‐hole ERT images generated for a synthetic tracer test in a heterogeneous aquifer. Under the assumption that the solute spreads as a passive tracer, for high Peclet numbers the spatial moments of the evolving plume are dominated by the spatial distribution of the hydraulic conductivity. The assimilation of the electrical conductivity 4D images allows updating of the hydrological state as well as the spatial distribution of K. Thus, delineation of the tracer plume and estimation of the local aquifer heterogeneity can be achieved at the same time by means of this interpretation of time‐lapse electrical images from tracer tests. We assess the impact on the performance of the hydrological inversion of (i) the uncertainty inherently affecting ERT inversions in terms of tracer concentration and (ii) the choice of the prior statistics of K. Our findings show that realistic ERT images can be integrated into a hydrological model even within an uncoupled inverse modeling framework. The reconstruction of the hydraulic conductivity spatial distribution is satisfactory in the portion of the domain directly covered by the passage of the tracer. Aside from the issues commonly affecting inverse models, the proposed approach is subject to the problem of the filter inbreeding and the retrieval performance is sensitive to the choice of K prior geostatistical parameters.

Impact of small-scale saline tracer heterogeneity on electrical resistivity monitoring in fully and partially saturated porous media: Insights from geoelectrical milli-fluidic experiments

The aim of this work was study the effects of climate and vegetation on the moisture content present in soils that support a complex vegetation system which comprises a pasture and micro-forest area, at the Center for Innovation and Technology Transfer of the Technological University of Panama located in Aguadulce, Coclé. For such purposes, a geophysical monitoring was carried out through four electrical resistivity tomographies on a 23.5 m long profile developed in the rainy season from July 9 to October 29, 2019 with time lapses of 38, 34 and 39 days between them. The datasets were inverted using a time-lapse ERT code based on the Boundless Electrical Resistivity Tomography (BERT). With the results obtained in this work (electric resistivity tomographies), was possible to delimit the electrical anomalies related to the physical characteristics of the subsoil in both: the pasture and the micro-forest area. Geophysical monitoring and volumetric moisture content calculated via Archie's law, allowed quantifying the dynamics of moisture content in both areas with significant percentage differences between -50% and 100% over the micro-forest area. This leads us to evaluate the levels of moisture absorption in both areas and consider the problem of land use and the impact it has on the groundwater recharge processes in this important region of the country.

Scientific Investigations Report 2012–5084: Comparison of No-Purge and Pumped Sampling Methods for Monitoring Concentrations of Ordnance-Related Compounds in Groundwater, Camp Edwards, Massachusetts Military Reservation, Cape Cod, Massachusetts, 2009–2010

The electrical resistivity tomography (ERT) technique can estimate groundwater velocity to within 5% of the pre-set groundwater velocity. The apparent conductivity obtained by the ERT technique is linearly related to the groundwater conductivity, as described by Archie's law. Gaussian-like profiles of the tracer concentration were demonstrated with the ERT technique, and the estimated dispersion coefficient was between 0.0015 and 0.0051 cm2/sec. In terms of monitoring changes in groundwater conductivity, the ERT technique has two major advantages over monitoring wells: (1) it measures a larger area and provides more representative results; and, (2) it does not withdraw groundwater samples, and therefore does not affect the groundwater flow. The objective of this research is to measure groundwater velocity with the ERT technique using only one well. The experiments in this research were divided into two parts. The first part evaluated the accuracy and repeatability of the ERT technique using a dipole-dipole array, and the second part estimated the groundwater velocity in a sandbox using the ERT technique. The length, width, and height of the sandbox, which was made of acrylic, were 1.5, 5, and 1.0 m, respectively. The ERT sandbox was sequentially filled with 5-cm layers of the silica sand to a total height of 70 cm. A total of 32 electrodes spaced every 5-cm were installed in the center of the sandbox. Three monitoring wells were installed along the line of the electrodes. Both no-flow and constant flow (NaCl solution with electrical conductivity and concentration of 5,000 μs/cm and 2.456 g/L, respectively) tracer experiments were conducted.

Comparison of smoothness-constrained and geostatistically based cross-borehole electrical resistivity tomography for characterization of solute tracer plumes

With time‐lapse electrical resistivity tomography (ERT), transport processes in the subsurface can be imaged and monitored. The information content of obtained spatiotemporal data sets opens new ways to characterize subsurface spatial variability. Difficulties regarding a quantitative interpretation of the imaged transport may arise from the fact that data inversion used in ERT is generally underdetermined and that ERT data acquisition is often limited to two‐dimensional (2‐D) image planes. To address this problem, we conducted a numerical tracer experiment in a synthetic heterogeneous aquifer with preset variability and spatial correlation of the hydraulic conductivity and monitored the tracer breakthrough in a 2‐D image plane perpendicular to the mean flow direction using time‐lapse ERT. The tracer breakthrough patterns in the image plane were interpreted using equivalent transport models: an equivalent convection dispersion equation to characterize the spatially averaged breakthrough and a stream tube model to characterize local breakthrough curves. Equivalent parameters derived from simulated and from ERT inverted concentrations showed a good agreement, which demonstrates the potential of ERT to characterize subsurface transport. Using first‐order approximate solutions of stochastic flow and transport equations, equivalent model parameters and their spatial variability were interpreted in terms of the local‐scale dispersion and the spatial variability of the hydraulic conductivity. The spatial correlations of the stream tube velocity and of the hydraulic conductivity were found to be closely related. Consequently, the hydraulic conductivity spatial correlation may be inferred from stream tube velocity distributions, which can be observed with sufficiently high spatial resolution using ERT.

CO2 Migration Monitoring by Means of Electrical Resistivity Tomography (ERT) – Review on Five Years of Operation of a Permanent ERT System at the Ketzin Pilot Site

Unveiling subsurface heterogeneity in porous aquifers: Insights from hydrogeophysics and derivative analysis