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Record W4241173766 · doi:10.2523/78970-ms

Advances in Heavy Oil and Water Property Measurements Using Low Field Nuclear Magnetic Resonance

2002· article· en· W4241173766 on OpenAlexafffundabout
J. W. Bryan, P. F., Y. Wen, A. Kantzas

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNMR spectroscopy and applications
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsPorous Media Laboratory
KeywordsCitationLibrary scienceDownloadComputer scienceWorld Wide Web

Abstract

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Advances in Heavy Oil and Water Property Measurements Using Low Field Nuclear Magnetic Resonance J.L. Bryan; J.L. Bryan University of Calgary, TIPM Laboratory Search for other works by this author on: This Site Google Scholar F.P. Manalo; F.P. Manalo University of Calgary, TIPM Laboratory Search for other works by this author on: This Site Google Scholar Y. Wen; Y. Wen University of Calgary, TIPM Laboratory Search for other works by this author on: This Site Google Scholar A. Kantzas A. Kantzas University of Calgary, TIPM Laboratory Search for other works by this author on: This Site Google Scholar Paper presented at the SPE International Thermal Operations and Heavy Oil Symposium and International Horizontal Well Technology Conference, Calgary, Alberta, Canada, November 2002. Paper Number: SPE-78970-MS https://doi.org/10.2118/78970-MS Published: November 04 2002 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Bryan, J.L., Manalo, F.P., Wen, Y., and A. Kantzas. "Advances in Heavy Oil and Water Property Measurements Using Low Field Nuclear Magnetic Resonance." Paper presented at the SPE International Thermal Operations and Heavy Oil Symposium and International Horizontal Well Technology Conference, Calgary, Alberta, Canada, November 2002. doi: https://doi.org/10.2118/78970-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search nav search search input Search input auto suggest search filter All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE International Thermal Operations and Heavy Oil Symposium Search Advanced Search AbstractLow field NMR of fluids can be used to measure physical properties of water and oil such as viscosity and diffusion coefficients. Remarkably accurate measurements can be obtained from simple and fast measurements in a beaker. Algorithms for the determination of heavy oil and bitumen viscosity have been previously developed that can provide first order estimates over a variety of viscosity ranges (10°-107 mPas) covering variable temperatures, water/oil ratios and oil compositions. When the algorithms are tuned for single oils, then the accuracy increases dramatically and the predictions are as accurate as direct viscosity measurements.In this paper, the aforementioned algorithms are extended to predict NMR response and viscosity predictions for live vs. dead heavy oil samples, and virgin vs. solvent-diluted heavy oil samples. The viscosity predictions of oils in beakers are compared to the predictions of the same oils while in reservoir conditions (i.e. in-situ). The proposed algorithms can be used in reservoir characterization and on-line viscosity measurements in heavy oil reservoirs.A NMR based water cut meter was recently introduced for accurate measurement of oil and water cut values. The instrument appears to be superior to conventional measurement devices since it does not seem to be affected by salinity, emulsion characteristics or temperature to date. Extensive field measurements have proved the above claims. The principles of this water cut device are further extended to the measurement of water cut oil cut and gas cut under laboratory conditions. Mixtures of heavy oil and bitumen with water and air were prepared in the laboratory and their NMR characteristics were identified under a broad range of saturations. The results were compared against mass balance measurements. It is demonstrated that the two-phase measurement algorithms can be extended to three phase systems. Thus the first step towards accurate multi-phase measurements can be achieved.IntroductionLow field nuclear magnetic resonance (NMR) has great potential as a tool for measuring properties of reservoir fluids and produced liquid streams. From a single NMR measurement of a fluid stream containing oil and water, the relative fractions of both liquids can be determined1,2. Since oil signals can be differentiated from water, the viscosity of the oil phase can also be determined3,4. Low field NMR can therefore be a useful tool for laboratory measurements, or can be implemented as an online tool at the wellhead to monitor the produced liquids. This paper extends the applications of low field NMR to in-situ viscosity estimation and determination of three phase fluid fractions. NMR is also used to investigate oil property changes in the presence of dissolved gas or solvent.The end goal of using low field NMR to predict viscosity is to make these predictions in-situ, on a logging tool. With this technology, reservoir fluids could be characterized much faster and cheaper than they can be through laboratory analysis, and viscosity changes with depth and location in the reservoir can be easily measured. The results of using low field NMR for bulk heavy oil and bitumen viscosity prediction have been very encouraging4.In a produced fluid stream, three phases can be present: oil, water and gas. Field trials have been done1,2, which used low field as an online water cut meter. This paper extends this application to three phase mixtures, made in the lab. In a fluid stream containing oil, water and gas, accurate fluid fraction estimates are necessary for metering and production records, and for estimating the emulsion viscosity4. Keywords: amplitude, prediction, viscosity, nmr, oil viscosity model, bitumen, spectra, oil viscosity, well logging, upstream oil & gas Subjects: Improved and Enhanced Recovery, Formation Evaluation & Management, Open hole/cased hole log analysis This content is only available via PDF. 2002. SPE/PS-CIM/CHOA International Thermal Operations and Heavy Oil Symposium and International Horizontal Well Technology Conference You can access this article if you purchase or spend a download.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.897
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.021
GPT teacher head0.276
Teacher spread0.255 · 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.

Study designOther design
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".

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Citations3
Published2002
Admission routes3
Has abstractyes

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