Bibliographic record
Abstract
Abstract Western Canada contains significant deposits of oil and gas in carbonate formations. Carbonates have fairly complicated pore structures with various types of porosity, thus the characterization of carbonates still remains a daunting task. Conventional log analysis of carbonates often leads to incorrect descriptions of the reservoir properties. Low field nuclear magnetic resonance (NMR) is an emerging technology that shows great promise in rock characterization. Previous results in the literature give disparaging accounts of the applicability of NMR in carbonate rock characterization, but this work demonstrates that low field NMR can be a valuable tool even in these reservoirs. The data set for this experimental work consists of a large collection of core samples from many different fields in Canada. NMR spectra interpretations have been compared to other core analysis methods. Definite correlations have been observed between the NMR spectra properties and the results from conventional core analysis, which verifies that NMR spectra can be used to characterize even complex pore structures. Unfortunately, there is too much scatter in these correlations for them to be accurate to within less than an order of magnitude. The trends observed were developed using all the data from different formations. In this work, the data were divided into their respective formations. Within a formation, the properties of the NMR spectra are compared to the conventional data to develop correlations to predict T2cutoff, irreducible water saturation (Swi), and permeability. These results show that if the general NMR correlations developed can be tuned to specific formations, NMR can become a very useful tool for characterizing carbonate reservoirs. Introduction Traditionally reservoir characteristics are studied through core and/or log analysis. The important reservoir parameters being investigated are porosity, irreducible water saturation, and permeability. These parameters give insight into the amount of existing hydrocarbon reserves, and the ease with which these reserves can be produced. These parameters can be found through core analysis, but this is a costly and time consuming process. Log analysis as an alternative has many inherent problems as well. Nuclear magnetic resonance in reservoir characterization shows promise in predicting porosity, irreducible water saturation and permeability of sandstone reservoirs. For carbonates reservoirs, however, NMR performance in the literature has not very encouraging. This is due to the direct application of the interpretation models, which were developed for sandstone reservoirs, in carbonate reservoirs. In order to predict the properties of the carbonate reservoirs through NMR data, it is important to develop a different interpretation method. Attempts have been made by many researchers to extract important reservoir information from NMR data collected for carbonate samples. Correlations were found to estimate T2cutoff, Swi and permeability. Mai and Kantzas1–4, have presented a series of experimental procedures aiming at the development of NMR-based carbonate characterization methods. Plugs from several formations were used in an attempt to provide predictive correlations for porosity, movable fluids, Swi and permeability. While porosity, movable fluids and Swi showed promise, the permeability predictions were poor. In this paper, a subset of the data investigated previously was further analyzed on a formation basis.
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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) | 0.002 | 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".