Low Resistivity Contrast Gas Bearing Formation Identification from Conventional Logs in Tight Gas Sandstones
Bibliographic record
Abstract
It’s a great challenge in identifying gas bearing formation from conventional logs in tight gas sandstones due to the low resistivity contrast caused by high irreducible water saturation. Based on the difference of the principles of three kinds of porosity logs (density, neutron and acoustic logs), three porosities difference method, three porosities ratio method, correlation of neutron and density logs and the overlap method of water-filled porosity and total porosity are introduced to identify tight gas bearing reservoirs. In gas bearing formations, the difference of three porosities is higher than 0.0, the ratio of three porosities is higher than 1.0, the correlation between density and neutron logs is negative, and the water filled porosities are lower than total porosities. On the contrary, in water saturated formations, the difference of three porosities is lower than 0.0, the ratio of three porosities is lower than 1.0, the correlation between density and neutron logs is positive, and the water filled porosities are overlapped with total porosities. Considering the complexity of in-suit formation, when the proposed identification criterion are mainly meet, the pore fluid should be determined, field examples show that the proposed techniques are applicable in tight gas formation identification. Key words : Low resistivity contrast gas bearing formation; Tight gas sandstones; Identification; Difference of three porosities; Ratio of three porosities; Correlation of neutron and density logs
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".