Porosity Distribution of Carbonate Reservoirs Using Low Field NMR
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 that shows great promise in rock characterization. In a single NMR experiment, information on porosity and pore size distribution of a carbonate sample can be determined. In this paper, low field NMR technology is investigated for determining primary and secondary porosity through the interpretation of NMR spectra. The data set for this experimental work consists of a large collection of core samples from various fields in Alberta and Saskatchewan. The CT data of fully saturated cores were converted to porosity, and this was found to agree well with gas expansion porosity. The primary and secondary porosity fractions were also obtained from the CT data, and were used to find corresponding NMR cutoff values that separate the NMR spectra into primary and secondary porosity. A distinct relationship was observed between the primary porosity fraction and Swi. The fraction of NMR amplitude in the last peak can also be correlated to CT secondary porosity. Another important relationship observed is that the T2gm of the last NMR peak correlates well with the cutoff between primary and secondary porosity. This implies that information from the fully saturated NMR spectrum can be used to estimate primary and secondary porosity fractions. Introduction Porosity of carbonates is a complex problem that is studied by only a few1. Secondary porosity and primary porosity are not easily distinguishable unless the primary pores and the diagenesis processes that occurred are studied1. Despite all these difficulties, it is very important to recognize the different porosity types in carbonates to help in developing carbonate reservoirs and to estimate the recovery efficiency in these reservoirs. As various researchers have stated, Nuclear Magnetic Resonance (NMR) can capture pore size information of the porous media2,3,4. Thus, in theory, it describes both the primary and secondary porosity. However, separating the signal into primary and secondary components remains a daunting task. Part of this difficulty arises from the fact that there is no clear distinction between primary and secondary pore size distributions as they overlap with each other. Chang et al. have previously tried to separate the signal of vugs in NMR response3. In carbonates, the definition of vugs can be quite important. In this case Chang et al. used the term "vugs" to describe cavities that are formed in the matrix by diagenesis, with sizes ranging from about 100 µm to cavern size. They reported that the vugs manifest themselves as a peak at the far end of the T2 distribution, with pores larger than 100 µm having T2 > 1s. They also noted that in vuggy carbonates, the vugs weakly contribute to flow3. Straley et al. later found that to minimize the errors in estimating permeability, the T2c value which separates the primary pores from the vugs was found to be 750 ms5.
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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) | 0.003 | 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".