Optimized experimental design strategies for ERT monitoring of transient flow processes

SUMMARY Monitoring subsurface fluid transport processes using electrical resistivity tomography (ERT) gained attention due to its sensitivity to variations in fluid content and temperature. Capturing these dynamic processes, such as tracer transport or pollutant dispersion, requires not only advanced imaging techniques but also efficient data acquisition strategies to ensure high-resolution and cost-effective monitoring. This study advances Optimal Experimental Design (OED) for transient flow monitoring using ERT. We developed three different strategies based on the Compare-R algorithm, and present as well as compare their results. By optimizing the selection of measurement configurations, these strategies aim to maximize the information content of the acquired data while minimizing redundancy and acquisition costs.The data-driven approach uses data from prior time steps to generate focusing masks. The model-driven approach incorporates transport models to target critical areas, and the hybrid approach combines both methods, iteratively refining the transport model based on the acquired data. We use synthetic studies to demonstrate the model-driven strategy’s advantage in identifying transport-affected areas, especially for rapid processes or longer monitoring intervals. The data-driven approach performs well when masked and process-affected areas overlap, but struggles with spatial delays otherwise. The hybrid OED effectively combines the strengths of both methods, detecting discrepancies between modelled and observed fluid flow, and refining transport simulations for subsequent steps, thereby ensuring reliable optimization for dynamic monitoring scenarios.