A Method for Determination of Rock Fabric Number from Well Logs in Unconventional Tight Oil Carbonates
Bibliographic record
Abstract
Summary This paper develops a method for estimation of rock fabric number (RFN) from well logs in unconventional tight oil carbonates with permeability less than 0.1 md. The objective is to investigate the oil potential of a Middle Cretaceous tight carbonate in Mexico. The development of a method for these conditions is challenging as the current approach developed by Lucia (1983) has been explained for carbonates with permeability more than 0.1 md. Core data and drill cuttings available for this study are limited but provide important insights for the log interpretation and for identifying the presence of grainstone, packstone, and wackstone rocks in the unconventional tight carbonate under consideration. A crossplot of RFN vs. rp35 (pore throat radius at 35% cumulative pore volume) permits delimiting intervals with good production potential that are supported by well testing data. Information for the analysis of the Mexican carbonate comes from well logs of nine wells and two re-entry wells, four buildup tests, and a limited amount of core and drill cuttings information. All data were provided by a petroleum company and have been used, for transparency, without any modifications. An unconventional tight carbonate as defined in this paper has a permeability smaller than 0.1 md. The unconventional tight oil carbonate reservoir considered in this study includes 95% of data with permeabilities smaller than 0.1 md and only 5% with permeabilities larger than 0.1 md. The method introduced by Lucia (1983) and Jennings and Lucia (2003) for determining RFN is powerful, but they explained it only for permeabilities larger than 0.1 md, thus the need for a methodology that allows estimating from well logs the presence of grainstone, packstone, and/or wackstone in unconventional tight carbonate reservoirs with permeabilities smaller than 0.1 md. Results indicate that the RFN provides a useful approach for distinguishing grainstone, packstone, and wackstone rocks in unconventional tight carbonate reservoirs. Furthermore, rock fabric can be linked with Pickett plots to provide an integrated quantitative evaluation of RFN, porosity, water saturation, permeability, pore throat radius, and capillary pressure. This integration indicates that there is good oil potential in the Middle Cretaceous unconventional tight carbonate in Mexico.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".