Cross-Borehole ERT Monitoring System for CO₂ Geological Storage: Laboratory Development and Validation

Cross-borehole electrical resistivity tomography (CHERT) technology has been implemented in field-scale CCS/CCUS (carbon capture and storage/carbon capture, utilization and storage) projects. It is highly desirable to investigate how to optimize the design of the ERT electrode arrays and corresponding working schemes for both laboratory experiments and field applications. A CHERT system was developed for laboratory experiments of CO2 geological storage applications. An optimization method was established for optimizing the structure of electrode arrays and corresponding working schemes. The developed CHERT system was calibrated systematically to determine the measurement range and accuracy of electrical impedance. Laboratory experiments were designed and implemented to validate the performance of the developed CHERT system. It has been illustrated that: (1) It is an essential step to optimize the structure of electrode arrays and corresponding working schemes of CHERT according to the real application background. The optimization method based on finite-element modelling provides an effective means for designing a field-scale CHERT system. (2) The quality of the images inverted from the CHERT data is highly dependent on the working schemes and specific modes, which is closely related to the size of the data sets used for the inversion. The AM-BN scheme is recommended due to the better uniformity of the resultant sensitivity field and application to larger borehole spacing. (3) Based on the calibration, the measurement range of the developed CHERT system can be determined as 100 Ω to 4.5 kΩ with an error limit of 1.5%. The maximum relative errors of the impedance magnitude and phase angle are 5.0% and 7.0%, respectively. Based on the test results the location of the CO2-bearing objects can be identified accurately. The shapes of the tested objects present distortion to some extent, but this can be alleviated by selecting working modes with a larger size of data set.