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Record W2051359735 · doi:10.2118/07-07-02

Porosity Distributions in Carbonate Reservoirs Using Low-Field NMR

2007· article· en· W2051359735 on OpenAlexafffundabout
A. Mai, Apostolos Kantzas

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2007
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNMR spectroscopy and applications
Canadian institutionsUniversity of Calgary
FundersSuncor Energy Incorporated
KeywordsPorosityCarbonateNMR spectra databaseCharacterization (materials science)Permeability (electromagnetism)Proton NMRFraction (chemistry)Effective porositySpectral lineMineralogyMaterials scienceAnalytical Chemistry (journal)GeologyChemistryComposite materialNanotechnologyOrganic chemistryPhysics

Abstract

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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. Laboratory characterization of carbonate properties is a slow and tedious process, however, core data is often collected in order to augment and tune logging tool predictions. In this application, having a good understanding of carbonate pore systems at the core analysis level is key to proper reservoir characterization. Low-field NMR is an emerging technology that shows great promise for rock characterization measurements. In this paper, low-field NMR technology is investigated for determining primary and secondary porosity through the interpretation of NMR spectra. This data was also used to establish the bound and mobile fluid distributions existing in the porous medium. The data set for this experimental work consists of a large collection of core samples from various fields in Alberta and Saskatchewan. CT data were analyzed to obtain the primary and secondary porosity fractions, which 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 the irreducible water saturation, Swi. The fraction of NMR amplitude in the last peak of the NMR spectra can also be correlated to CT secondary porosity. Another important relationship observed is that the geometric mean relaxation time of the last NMR peak correlates well with the cutoff between primary and secondary porosity. The bound and mobile fluid distributions are generally distinguished through the identification of T2cutoff values. A correlation was found to predict T2cutoff for this wide range of samples. This study shows that information from the fully saturated NMR spectrum can be used to estimate primary and secondary porosity fractions in carbonates, as well as bound and mobile fluid fractions. Introduction Porosity of carbonates is a complex problem that has had only limited attention in the literature(1). In general, carbonate porosity is divided into primary and secondary porosity. These different types of porosity are not easily distinguishable unless the primary pores and the diagenesis processes that occurred are studied(1). Despite these difficulties, it is very important to recognize and attempt to quantify the different porosity types and mobile/immobile fluid fractions in carbonates in order to help in developing carbonate reservoirs and to estimate the pore connections and recovery efficiency in these reservoirs. As various researchers have found, Nuclear Magnetic Resonance (NMR) can capture pore size information of the porous media(2–4). Thus, in theory, it describes both the primary and secondary porosity. However, separating the signal into different porosity 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.(3) have previously tried to separate the signal of vugs in NMR response. In carbonates, however, even the definition of vugs can be quite different. Chang et al.(3) 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.467
Threshold uncertainty score0.958

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.292
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations36
Published2007
Admission routes3
Has abstractyes

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