Do metal infrastructure effects cancel out in time-lapse electromagnetic measurements?
Bibliographic record
Abstract
ABSTRACT Controlled-source electromagnetic methods have the potential to be used in reservoir monitoring problems due to their sensitivity to subsurface resistivity distribution. For example, time-lapse electromagnetic (EM) measurements can help to determine reservoir changes during enhanced oil recovery processes, such as water/steam injection or CO2 sequestration. Although metal infrastructure, such as pipelines and casings, can strongly influence EM data and mask the underlying geologic response, these effects have not previously been quantified for time-lapse surveys. We have analyzed the effects of well casings on time-lapse surface-to-surface EM measurements using 1D and 3D modeling. First, using a synthetic example of an onshore 1D hydrocarbon reservoir, we quantified the effect of single and multiple casings at several source and receiver locations. We found that time-lapse responses are significantly distorted when a source or receiver is located near a casing. Next, we approximated a hydrocarbon reservoir as a thin bounded resistive sheet. We developed a method of moments algorithm to calculate the respective secondary currents and charges on a well casing and resistive sheet combination and validated the electric fields these secondary sources generate against finite-element modeling. Finally, we calculated and explicitly demonstrated time-lapse amplitude changes in the well casing-thin-sheet interaction matrix, secondary currents, charges, and surface electric fields. Our 3D modeling results indicated that the conductive casing reduces the ability of the resistive sheet to impede current flow and distorts time-lapse responses. Therefore, one cannot fully eliminate casing effects by subtraction of time-lapse data and must fully incorporate such infrastructure into forward models for time-lapse EM inversion.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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; both teacher heads agree on what is shown here.
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".