Synthetic modelling study of marine controlled-source \nelectromagnetic data for hydrocarbon exploration
Bibliographic record
Abstract
The marine controlled-source electromagnetic method (CSEM) is a geophysical technique \nfor mapping subsurface electrical resistivity structure in the offshore environment. It has \ngained ground in recent years as a tool for remote detection and mapping of hydrocarbon \nreservoirs as it serves as an independent yet complementary method to seismic acquisition. \nWhile CSEM data contains useful information about the subsurface, modelling and \ninversion are required to convert data into interpretable resistivity images. Improvement \nof modelling tools will assist in closing the gap between acquisition and interpretation of \nCSEM data. The primary focus of this study was to explore the limits of our present modelling \ncapabilities in the context of marine electromagnetic scenarios. Software based on \nthe three-dimensional CSEM finite-element forward code CSEM3DFWD (Ansari and Farquharson, \n2014; Ansari et al., 2015) was employed in this study. While testing of this \nsoftware had been expanded to models of relevance to mineral exploration, its performance \nfor models which are representative of marine geologic environments, in particular those \nwhich are encountered in offshore oil and gas exploration, had not yet been investigated. \nIn this study, marine models of increasing complexity were built and tested, with the ultimate \ngoal of synthesizing marine CSEM data for three-dimensional earth models which \nwere complete in their description of the subsurface. Computed responses were compared \nto results existing in the literature, when available. To investigate the capability of the code \nin modelling realistic scenarios, forward solutions were computed for a marine reservoir \nmodel based on the real-life North Amethyst oil field, located in the Jeanne d’Arc Basin, \noffshore Newfoundland. When the capability of modelling realistic earth models is fully \nrealized, forward modelling may be used to assess the utility of the marine CSEM method \nas a tool for hydrocarbon detection and delineation in specific offshore scenarios.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".