Evaluation of petrophysical rock typing and determination of pore size distribution in a carbonate reservoir using nuclear magnetic resonance
Bibliographic record
Abstract
Tight carbonate reservoirs exhibit more complex petrophysical parameters than conventional carbonate reservoirs, presenting unique challenges for characterization and hydrocarbon exploration. One crucial aspect of describing a tight carbonate reservoir is the accurate calculation of petrophysical properties (e.g. porosity and permeability) and rock characteristics. The proposed workflow has been implemented in the Ilam Formation, which is a tight carbonate reservoir. Applying an integrated methodology, including petrography, thin-section analysis, mercury injection capillary pressure (MICP), scanning electron microscopy (SEM) and nuclear magnetic resonance (NMR), on reservoir rocks is a prerequisite to understanding the complexity of carbonate reservoirs, petrophysical properties and pore throat size distribution. As a result, combining the aforementioned parameters will reduce the amount of uncertainty associated with exploratory projects. Core measurements and the petrophysical rock typing (PRT) method were used to determine permeability, porosity and capillary pressure curves. Based on the PRT method, four rock types were determined when considering the geological attributes. The pore size distribution curves obtained from the NMR model show that NMR could be applied as a useful technique for estimating pore size distribution and correspond with the results from the MICP method, which reinforces the importance of integrating NMR–MICP to improve carbonate pore facies estimates. Moreover, the results of this study showed that the NMR log data, when calibrated with MICP, core data analysis, thin-section petrography and SEM images, can help to characterize the tight carbonate reservoir more accurately and reduce uncertainty in the reservoir rock typing.
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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) | 0.000 | 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 | 0.000 | 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".