Lithology Classification and Porosity Estimation of Tight Gas Reservoirs With Well Logs Based on an Equivalent Multi-Component Model
Bibliographic record
Abstract
Tight gas makes up a significant portion of the natural gas resources. There are tight gas reservoirs with great reserve and economic potential in the west Sichuan Basin, China. Due to the complex mineral component and heterogeneity of the thick tight sand formations, the reservoir parameters are challenging to evaluate from well logs using conventional methods, even the fundamental porosity. The mineral components must be considered. In this study, based on the analysis of different logging responses of varying lithologies, we introduced the complex reservoir analysis (CRA) method. CRA is always used in the carbonate reservoirs to calculate the different rock component volume fractions and can be used to classify the lithology and calculate the porosity simultaneously. By analyzing the component, a new equivalent component method (CRAE) is proposed based on the CRA method in this paper. In this method, the AC-CNL equation-calculated porosity is calibrated according to the core porosity data to set the rock components’ physical parameters. After calibration, the rock component fractions and porosity can be calculated accurately. Also, according to the relationship between the grain size and natural gamma-ray, a granularity median model was established. Six lithology types, including coarse-grained quartz sandstone and coarse-grained lithic sandstone, are distinguished, and the porosity is estimated in the study area. The identification results are compared with the mud logging data and other methods. It shows that this method is very well adequate in the tight sandstone gas reservoirs in the study area.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".