An Evaluation of the Application of Low Field NMR in the Characterization of Carbonate Reservoirs
Bibliographic record
Abstract
Abstract Conventional reservoir analysis has always been an extensive process. In order to properly characterize a reservoir, cores and/or logs have to be obtained. Both core and log analysis is expensive and time consuming. NMR is an attractive alternative to these tools due to the fact that in theory, only one measurement is required. However, the conventional methods of interpreting NMR data only seem to work for simple sandstones. A new method of interpreting NMR data is required for complex porous structure such as carbonates. It was found that NMR can predict porosity that is similar to the values obtained by gas expansion. By using the NMR data at fully saturated and irreducible water saturation (Swi), a T2cutoff value was obtained for each sample that separates the bound and movable fluid signals. It was found that T2cutoff for carbonates is not 100 ms as is widely believed by many people who have analyze NMR in carbonates. A correlation for T2cutoff was found as a function of the size of the last peak and its geometric mean. A correlation was also found for Swi, which was a function of the size of the first and last peak. The Free Fluid and the mean T2 permeability models were evaluated. It was seen that the predictions from these models were not adequate. Another permeability model was developed, which is expressed in terms of the size of the first and last peak of the NMR spectrum obtained from the fully saturated sample. It was found that the correlation did a better job of predicting the permeability values. The new model has its own limitations, a method is currently being investigated to overcome these limitations. Despite these limitations, however, the new NMR permeability model provides better estimates of carbonate permeability than any other established methods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".