2D Optimized Electrode Arrays for Borehole Resistivity Tomography and CO2 Sequestration Modelling

Abstract

Modern optimization approaches for electrode configurations can significantly improve the resolution of 2.5D resistivity imaging surveys. This study presents a brief review of the 2.5D optimization approach, particularly for borehole–borehole surveys with applications for mapping virtual CO2 plumes sequestrated in deep saline reservoir formations. The applied algorithm searches for arrays that maximize the spatial resolution of the survey among the comprehensive dataset of best possible spatial resolution (i.e. least temporal resolution). A main goal of this study is to increase the temporal resolution of ERT borehole–borehole surveys by selecting optimized electrode configurations in order to minimise the required data acquisition time while sustaining a high spatial resolution. The optimized dataset starts with a base set and is iteratively increased based on the model resolution matrix (R ) until the required number of data points is achieved. Among four different optimization methods, the compare R (CR) method of the best resolution is applied to directly calculate R for each new array added to the optimized dataset. Small optimized datasets generated by this technique are only <5% of their comprehensive sets but of an average resolution ratio (R r) of >0.95 (i.e. almost the same resolution). With increasing the size of the optimized dataset (during its generation), the algorithm progressively enhances R r values in the central interwell region (of low sensitivities and low resolution) far higher than in the near borehole region (of high sensitivities). Also the inverted tomogram reliability increases by increasing the optimized data size. Briefly, the optimized arrays improve the resolution in the interwell region which is commonly low in borehole–borehole ERT studies. The inverted output model is evaluated quantitatively using the model difference relative to the input model. The results reflect the common smearing effects and artefacts of varying degrees that overpredict volumes, underpredict magnitudes and blur boundaries of the target anomalies. This input model is a synthetic resistivity model that was used to generate synthetic (forward solution) data used during the inversion. Applications on synthetic CO2 models show that the mapping resolution for optimized datasets is better than that for other highly resolving arrays of the same number of data points. Problems of smeared boundaries and thin layers are less visible in the optimized array than in the other highly resolving arrays.