A Petrophysical Multivariate Approach Using Geophysical Well Logs and Laboratory Measurements to Characterize an Albian Carbonate Reservoir in the Campos Basin, Southeast Brazil
Bibliographic record
Abstract
This study characterizes an Albian carbonate reservoir of oilfield B in the Campos Basin, based on geophysical well logs and laboratory petrophysical data. The use of current approaches to characterize reservoirs allowed us to estimate the porosity, permeability, and water saturation of this reservoir in a more reliable way than when this oilfield was discovered in 1976. To achieve this goal, the cluster analysis for rock typing module of the Interactive Petrophysics software was used to divide the succession into 11 electrofacies. Using log and laboratory data, an equation was derived to determine the porosity and the permeability of each electrofacies through the multiple linear regression technique. The results were compared with different models proposed by other authors, with the best results being found with multiple linear regression. Water saturation, on the other hand, was estimated by the Archie (The electrical resistivity log as an aid in determining some reservoir characteristics, Petroleum Transactions of AIME, 146:54–62, 1942) equation after identifying the cementation coefficient with the Pickett (A review of current techniques for determination of water saturation from logs, Journal of Petroleum Technology 18:1425–1433, 1966) cross plot. Finally, the porosity and permeability data were again used to identify three main flow units in the reservoir through the Winland (Oil accumulation in response to pore size changes, Weyburn field, Saskatchewan. Amoco Production Company Report F72-G-25, 20 p., 1972) graph. To verify the effectiveness of the adopted methodology, it was applied (successfully) in a test well, defining porosity, permeability, water saturation, and flow units, where laboratory data were absent.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".