Visualizing hydrocarbon migration pathways associated with the Ringhorne oil field, Norway: An integrated approach
Bibliographic record
Abstract
Abstract Norway’s Ringhorne Field is a faulted anticline, which produces oil from Triassic (Statfjord) and Paleocene (Hermod) sands. It is located on the Utsira High. Geochemical studies of the produced oil indicate that the oil is generated from mature Upper Jurassic marine shales in the adjacent Viking Graben. However, it is not clear how oil migrated into the Triassic reservoirs and charged the overlying Paleocene reservoirs. Lateral hydrocarbon migration is not detectable on seismic data. However, vertical hydrocarbon migration can be observed as a vertically aligned, low-amplitude, chaotic signature on normally processed seismic data. Gas-chimney detection is a proven neural network technique to detect these vertical hydrocarbon migration pathways. The processing results are then validated using a set of criteria to determine if they represented true hydrocarbon migration rather than seismic artifacts. The chimney processing results using this traditional (shallow) neural network are compared with convolutional neural network (deep learning) results and geomechanical modeling on key lines. Key reservoirs are delineated using a deterministic simultaneous seismic inversion approach. Reliable chimneys are then visualized in the vicinity of the producing reservoirs. The results indicate pathways by which the Triassic fluvial sands received the charge and how these reservoirs have flank leakage to provide the charge to shallower Paleocene reservoirs. This approach is currently being used over hundreds of fields and dry holes in the Norwegian North Sea and worldwide as analogs to assess the hydrocarbon charge and top seal risk predrill.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".