Application of 3D marine controlled-source electromagnetic finite-element forward modeling to hydrocarbon exploration in the Flemish Pass Basin offshore Newfoundland, Canada
Bibliographic record
Abstract
In recent years, marine controlled-source electromagnetic (CSEM) surveying has become an effective supplemental interpretation tool to the seismic reflection method to help mitigate risk in an offshore exploration setting. Interpretation of marine CSEM data is commonly achieved via finite-difference inversions on rectilinear meshes, which has its merits, but the results are typically of very low resolution. The alternative is forward modeling, which requires a model to be known a priori, but the detail of the model can be created to reflect realistic geologic conditions. What is typically seen in the literature are applications of EM forward modeling codes to synthetic, and sometimes complex synthetic, models. However, what the literature is missing is an application that overcomes the challenges of applying a 3D forward modeling method to real models constructed from real information. We have developed an application of a 3D marine CSEM finite-element forward modeling method to the Bay du Nord prospect in the Flemish Pass Basin offshore Newfoundland. The 3D resistivity model, composed of four topographical layers and the Bay du Nord reservoir body, was built using 2D seismic data, one well log, and a marine CSEM inversion. Although other mesh representations have their merits, we chose to discretize our 3D model into an unstructured tetrahedral mesh because its flexibility enabled the accurate representation of complex structures while minimizing the number of unknowns. The availability of measured marine CSEM data allowed for the resistivities of each layer in the 3D model to be refined, and it also allowed for the simulated data to be assessed in the context of the real noise levels. A subsequent sensitivity analysis of the forward modeling results provided insights regarding the detectability of the Bay du Nord reservoir.
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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 | 0.000 |
| 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".