Characterizing the Self‐Potential Response to Concentration Gradients in Heterogeneous Subsurface Environments

Abstract

Self‐potential (SP) measurements can be used to characterize and monitor, in real‐time, fluid movement and behavior in the subsurface. The electrochemical exclusion‐diffusion (EED) potential, one component of SP, arises when concentration gradients exist in porous media. Such concentration gradients are of concern in coastal and contaminated aquifers and oil and gas reservoirs. It is essential that estimates of EED potential are made prior to conducting SP investigations in complex environments with heterogeneous geology and salinity contrasts, such as the UK Chalk coastal aquifer. Here we report repeatable laboratory estimates of the EED potential of chalk and marls using natural groundwater (GW), seawater (SW), deionized (DI) water, and 5 M NaCl. In all cases, the EED potential of chalk was positive (using a GW/SW concentration gradient the EED potential was ca. 14 to 22 mV), with an increased deviation from the diffusion limit at the higher salinity contrast. Despite the relatively small pore size of chalk (ca. 1 μm), it is dominated by the diffusion potential and has a low exclusion efficiency, even at large salinity contrasts. Marl samples have a higher exclusion efficiency which is of sufficient magnitude to reverse the polarity of the EED potential (using a GW/SW concentration gradient the EED potential was ca. −7 to −12 mV) with respect to the chalk samples. Despite the complexity of the natural samples used, the method produced repeatable results. We also show that first order estimates of the exclusion efficiency can be made using SP logs, supporting the parameterization of the model reported in Graham et al. (2018, https://doi.org/10.1029/2018WR022972), and that derived values for marls are consistent with the laboratory experiments, while values derived for hardgrounds based on field data indicate a similarly high exclusion efficiency. While this method shows promise in the absence of laboratory measurements, more rigorous estimates should be made where possible and can be conducted following the experimental methodology reported here.

Laboratory Measurements of Zeta Potential in Fractured Lewisian Gneiss: Implications for the Characterization of Flow in Fractured Crystalline Bedrock

“…However, the inversion of the SP field measurements (as explained above) and gain of both qualitative and quantitative interpretation of the groundwater flow (i.e., preferential flow paths, anisotropy of flow and hydrodynamic properties, pressure gradients, local flow velocities) requires values of a key petrophysical property, termed the zeta potential, which characterizes electrochemical interactions that take place at the mineral-water interface. The zeta potential depends on the aquifer lithology, mineralogy, pore space topology (e.g., [29,30]) as well as on groundwater properties, specifically ionic composition, pH and concentration of aqueous solutions [31].…”

3D-printed Ag-AgCl Electrodes for Laboratory Measurements of Self-Potential

“…The unknown exclusion-diffusion potential across the saturated porous sample can then be determined (Leinov and Jackson, 2014). A more detailed description of the experimental method and apparatus can be found in Macallister et al, 2019). Here we consider only the column experiments that are used to establish the electrode behaviour.…”

Interpreting Self-Potential Signal during Reactive Transport: Application to Calcite Dissolution and Precipitation

“…It is an electrostatic field that compensates the charge separation due to differential mobility between ions (e.g., β Na + < β Cl − ) along the concentration gradient to maintain the electroneutrality of the system. Many laboratory works have observed and successfully modeled this phenomenon for simple systems, e.g., [20,62,63]. For example, laboratory experiments of sodium chloride (NaCl) diffusion in a sand matrix were successfully modeled using the Henderson formula [19,64,65].…”