On the Characterization of Carbonate Reservoirs Using Low Field NMR Tools
Bibliographic record
Abstract
On the Characterization of Carbonate Reservoirs Using Low Field NMR Tools A. Mai; A. Mai University of Calgary and TIPM Laboratory Search for other works by this author on: This Site Google Scholar A. Kantzas A. Kantzas University of Calgary and TIPM Laboratory Search for other works by this author on: This Site Google Scholar Paper presented at the SPE Gas Technology Symposium, Calgary, Alberta, Canada, April 2002. Paper Number: SPE-75687-MS https://doi.org/10.2118/75687-MS Published: April 30 2002 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Mai, A., and A. Kantzas. "On the Characterization of Carbonate Reservoirs Using Low Field NMR Tools." Paper presented at the SPE Gas Technology Symposium, Calgary, Alberta, Canada, April 2002. doi: https://doi.org/10.2118/75687-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search Dropdown Menu nav search search input Search input auto suggest search filter All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE Unconventional Resources Conference / Gas Technology Symposium Search Advanced Search AbstractAlberta 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.IntroductionReservoir rocks can be divided into two groups: clastics and carbonates. Clastics include silts, sands and gravels, while carbonates encompass limestones and dolomites. Both of these two groups have intergranular porosity, which is the pore space between sand grains or carbonate crystals originally deposited1. However, carbonates also have vugular porosity, formed by the leaching of carbonate grains or other soluble materials.Vugs are usually defined as pores that are larger than adjacent grains. The presence of vugs in carbonate reservoirs is very common. However, conventional wireline logs cannot always detect such pores due to the limited vertical resolution of the tools2. Unfortunately, vugs have a significant effect on productivity, porosity and permeability. Along with vugs, another problem with carbonate rocks is that their porosity is very difficult to determine. This is due to the fact that the rock matrix or specifically the amount of limestone and dolomite varies, making it difficult to analyze using conventional logs3,4. In complex carbonates estimations from logs are deemed unreliable and it is normally required to calibrate logs against core analysis. However, core analysis is a time consuming and costly process, therefore it is not always practical.Nuclear Magnetic Resonance (NMR) is a new and promising technology that is fast, nondestructive and able to yield a vast amount of information about the reservoir formation5. In theory, a single NMR measurement can be used to determine porosity, permeability and irreducible water saturation. However, most of the earlier work on NMR usually assumes a simple formation lithology (such as sandstone) or simply ignores the effect of rocks on NMR interpretation and data analysis. Thus the methods originally developed might not be able to correctly predict the properties of complex systems such as carbonates 6,7.This paper investigates porosity and irreducible water saturation of carbonate samples from many fields in Western Canada through the analysis of T2cutoff values. Keywords: nmr, well logging, amplitude, carbonate reservoir, pore, permeability, group 1, nmr response, upstream oil & gas, characterization Subjects: Formation Evaluation & Management, Open hole/cased hole log analysis This content is only available via PDF. 2002. Society of Petroleum Engineers You can access this article if you purchase or spend a download.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".