Unsupervised Learning Analysis of a Multi-Parameter Geophysical Database for Abu Dhabi Hydrocarbon Reservoirs
Bibliographic record
Abstract
Abstract Unsupervised learning technique was implemented to delineate locations of potential hydrocarbon reservoirs at offshore Abu Dhabi, U.A.E. Integrating several geophysical observations that are sensitive to different physical parameters in a single scheme results in more constrained and high-resolution geophysical models. We created a database comprising gravity and magnetic field data as primary attributes. Then, by implementing nonlinear inversion modelling of gravity and magnetic field data, depth-to-basement and depth-to-salt structures were derived as complementary attributes. Applying k-means clustering technique on the preprocessed data around the Ghasha oil field, the areas with higher probability of hydrocarbon reservoirs were distinguished. The regions that encompass known oil fields cover shallow Infracambrian salt interface which is associated with lower gravity signals due to lower density. Nonetheless, these areas are located over higher gravity and magnetic values resulted from the shallower basement which obscures the negative gravity anomaly expected from the salt structure. The clustering results also indicates that a different process is needed for creation of smaller hydrocarbon reservoirs. These cases are mostly located over the edges of the clusters where we have a transition from positive to negative anomalies. This is justified as trapping mechanism over the domes is different than the slopes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".