FORMATION EVALUATION FOR SOME OIL PRODUCING WELLS IN GEBEL EL-ZEIT AREA, GULF OF SUEZ, EGYPT
Bibliographic record
Abstract
Surface geological methods can help to identify the interesting sub-surface structures which may containfluids, but are unable to predict whether they contain hydrocarbons or not. Accordingly, there is no solution other thandrilling a well to really determine the presence of hydrocarbons below the surface. Formation evaluation is a process inwhich borehole measurements are used to evaluate the characteristics of subsurface formations. The primary objectives offormation evaluation are: the identification of reservoirs, the estimation of hydrocarbons in place and the estimation ofrecoverable hydrocarbons. Gebel El Zeit area has only one oil producing field which is Ras El Ush (REU) Oil field. TheTDT interpretation for different REU Field wells resulted in divided the Matulla Formation into different intervals withineach well, while the Malha Formation did not be covered by the TDT log in some of these wells. The SW varies, in theintervals of the Matulla Formation, from 30% to 80%. Gas zones were observed in some wells and the GOC weredetected. The Malha Formation is a clean sand formation with little kaolinite volume in most wells. The thickness of theTarmat section is about ±78 ft, above the OWC, within the Malha Formation. The Tarmat section has the same depth andalmost has same thickness in all wells. It separates the oil, above, from the water, below.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.001 | 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".