Advances in electromagnetic imaging in the presence of well casings: algorithms and field experiments
Bibliographic record
Abstract
Controlled-Source Electromagnetic (CSEM) methods have the potential to be powerful geophysical tools for imaging and monitoring the distribution of electrically resistive fluids, such as freshwater aquifers, CO2 injected into the subsurface or hydrocarbons during oil and gas production. However, the presence of metallic infrastructure (steel well casings, pipelines etc.) presents an enormous challenge, because the highly conductive metal masks the electromagnetic response of subsurface geology and distorts any associated time-lapse changes. Therefore, numerical techniques to predict and mitigate the contamination caused by pipelines and casings on CSEM surveys are critical for real world imaging and 4D applications near any such metal objects. In a collaborative project between the Colorado School of Mines and Shell, we have developed CSEM modeling and inversion tools that can handle realistic scenarios with multiple vertical as well as deviated casings and complex pipeline networks, as will be encountered in mature oil field environments. First, we implemented a forward modeling code based on the Method of Moments technique, which effectively turns the casings into extra sources, such that we do not need to discretize them into excessive numbers of very small model cells. We used this modeling tool to demonstrate quantitatively how steel casings impact synthetic and real time-lapse EM data. The forward modeling code was then combined with a newly developed Gauss-Newton inversion engine, which by itself has been demonstrated to provide images of superior resolution, depth penetration and data fit with less dependency on initial conditions compared to previous quasi-Newton inversion engines. In this contribution, we first demonstrate on synthetic data that the combination of these two algorithms provides high-quality electrical resistivity images in the immediate vicinity of well casings. Then, we show encouraging results of applying the new tools to field trial data acquired over known casings under semi-controlled conditions. The images obtained are nearly free of casing imprint and subsurface geology could be recovered. These results suggest that this technology may enable us to explain severely distorted field data that were previously uninterpretable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".