Computer Modeling of Porosity and Lithology for Complex Reservoirs Using Well-Log Measurements
Bibliographic record
Abstract
The high degree of heterogeneity, saturation of multiphase fluids, and presence of clays in complex reservoirs make each of the three porosity logs (sonic, density, and neutron), if used independently, generally record inaccurate porosity. For such reservoirs, combining different logs gives accurate results of porosity. The reservoirs of Terra Nova and Hibernia (Jeanne d?Arc Basin), offshore of the eastern coast of Canada, are saturated with multiphase fluids, enriched with clays, and made of compacted and heterogeneous rocks, in terms of the lithological and mineralogical composition, and the size and shape of the grains and pores. In this study, the porosity and the rock constituents were determined for both reservoirs using a computer technique in which the iteration process was applied. That was done by developing and using various computer programs and models, and utilizing numerous data from several logs analyzed at 0.2-m sampling-depth intervals. The more the number of logs and iterations used in computation, the higher the degree of accuracy of results obtained. The reservoirs are made of shalestone, sandstone, siltstone, limestone, marlstone, and conglomerate. The porosity varies widely, because of variations in the rock composition and overburden pressure. The modeled porosity was compared to the porosity measured by the compensated neutron log (CNL). The results indicate that the CNL-measured porosity is generally higher than the modeled porosity by about 50%. The CNL-measurements are greatly affected by the high amount of hydrogen that is chemically bound in the shales, hydrocarbons, and water. Therefore, CNL records higher values of porosity when porosity is actually low, and lower values of porosity when it is actually high.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".