Impact and mitigation of borehole related effects in permanent crosshole resistivity imaging: An example from the Ketzin CO2 storage site
Bibliographic record
Abstract
Geoelectrical methods are particularly suited for CO2 injection monitoring due to their high sensitivity to fluid displacement processes in porous rock formations. The use of borehole electrodes is favorable for deep storage horizons. Yet data acquisition based on permanently installed borehole electrodes can be challenged by the finite extent of the electrodes, unintended borehole deviation and complex borehole completion. Such conditions can lead to systematic errors in the electrical data sets, distortions of tomograms, and ultimately misinterpretations. We systematically analyze the effects of different borehole related error sources on tomographic inversion results and present respective methods for mitigation. Specifically, we incorporate the finite extent of the ring electrodes and the borehole completion into the electrical finite-element models and discuss the opportunity to infer borehole deviations solely based on geoelectrical data by means of a coupled inversion. While the finite extent of ring electrodes can be neglected if the electrode spacing is sufficiently large (> 5 m), different borehole completion materials used to fill the well annulus can cause potentially strong resistivity contrasts between the borehole completion and the rock formation, i.e., close to the electrodes. Resulting inversion artifacts are generally less severe when the borehole completion is more resistive compared to the surrounding rock. It is also shown that 2.5D inversion approaches are not adequate for imaging injection experiments in the presence of borehole completion. Unintended borehole deviation can result in geometric errors. Especially, vertical electrode shifts cause strong and localized inversion artifacts. Coupled inverse schemes potentially provide the opportunity to infer electrode shifts solely based on geoelectrical data provided the availability of high quality measurements (< 5% data error). After discussing the effects of the different borehole related error sources, the mitigation methods are validated using synthetic data sets. Subsequently, relevant methods are applied to a field data set from the Ketzin CO2 storage site, Germany, where crosshole electrical resistivity imaging is used for CO2 migration monitoring. The mitigation methods presented can improve estimates of the subsurface resistivity distribution, which, in our particular example, is an essential basis for the quantification of CO2 saturation from time-lapse geoelectrical measurements.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".