A New Method for Determination of Rock Fabric Number from Well Logs in Unconventional Tight Oil Carbonates
Bibliographic record
Abstract
Abstract This paper develops a new method for estimation of rock fabric number (RFN) from well logs in unconventional tight oil carbonates with less than 0.1 md. The objective is to investigate the oil potential of a Middle Cretaceous tight carbonate in Mexico. Development of a method for these conditions is challenging as the current approach developed by Lucia (1983) has been explained for carbonates with more than 0.1md. The method is calibrated with data from cores and cuttings and allows estimating the presence of grainstone, packstone and wackstone rocks in unconventional tight carbonates from well logs. A crossplot of RFN vs rp35 (pore throat radius at 35% cumulative pore volume) permits delimiting intervals with good production potential that is supported by well testing data. Information for analysis of the Mexican carbonate comes from well logs of 9 wells and 2 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 percent 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. The novelty of this paper is the use of rock fabric (RFN) in unconventional tight carbonates with permeabilities smaller than 0.1 md for estimating the presence of grainstone, packstone and wackstone rocks from well logs. In addition, a crossplot of RFN vs rp35 provides a good indication of intervals with oil production potential.
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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 | 0.001 |
| 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".