On the Characterization of Carbonate Reservoirs Using Low Field NMR Tools
Bibliographic record
Abstract
Abstract Alberta contains significant deposits of oil and gas in carbonate formations. Carbonates tend to have fairly tight matrix structures, resulting in low primary porosity and permeability. As a result, laboratory characterization of carbonate properties is a slow and tedious process. Low field NMR is an emerging technology shows great promise in rock characterization. In a single NMR experiment, rock properties like porosity, permeability and Swi can in theory be measured. Experiments were performed on approximately 80 core plugs from six carbonate formations. Porosity measurements were performed through gas expansion and brine saturation (Archimedes’ principle). Air permeabilities were also measured. Cores were also saturated with brine and spun to irreducible water saturation. NMR measurements were taken at both saturation stages (Sw = 1.0 and Sw = Swi). NMR data were interpreted using the conventional core analysis results as guides. Observations were made regarding NMR trends and corresponding rock properties. Preliminary analysis of the data shows that NMR can successfully predict the content and distribution of the fluids in the porous media. Also, the NMR spectra of carbonate samples seem to suggest that NMR can be used as a tool for classifying cores into different pore systems. Using T2cutoff values as a tool, the cores can be divided into groups. Group 1 has T2cutoff < 80 ms, while group 2 has T2cutoff in the range of 80 to 200 ms and group 3 has T2cutoff values > 200 ms. Group 1 cores generally have higher porosity values than groups 2 and 3. Group 1 also has low values of T2gm, compared to the other two groups. Air permeability was compared to the geometric means and T2gm_cutoff values of the three groups and some general trends were observed, but more analysis is required before these trends can be quantified.
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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".