Three-dimensional inversion of controlled source electromagnetic data with steel casings
Bibliographic record
Abstract
ABSTRACT Steel casings in oil and gas development have the potential to enhance controlled source electromagnetic (CSEM) responses, particularly in detecting subtle changes in reservoir properties. To map the 3D distribution of injected fluid during hydraulic fracturing, a method was proposed that incorporates the effects of such casings. We introduced an edge conductivity parameter, which combines intrinsic conductivity and the cross-sectional area of the casing, into the conductivity model. By aligning these edge conductivities with mesh edges within a finite volume framework, casing effects were captured without complex mesh refinements. The algorithm was validated through cylindrical mesh simulations and forward modeling using a line current transmitter. For inversion, an objective function combining data misfit and model smoothness constraints was optimized with the Gauss-Newton method. Synthetic scenarios involving horizontally drilled wells and top-casing sources demonstrated the method's ability to recover fluid trajectories and detect asymmetric fluid distributions. Additionally, the method proved effective in assessing casing integrity by estimating edge conductivities. This work shows that integrating steel casing data into CSEM modeling enhances reservoir characterization and supports decision-making in oil and gas exploration and CO₂ sequestration.
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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) | 0.000 | 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 teacher head, 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".