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Record W4240018922 · doi:10.2118/2000-095

Determination of Oil and Water Compositions of Oil/Water Emulsions Using Low Field NMR Relaxometry

2000· article· en· W4240018922 on OpenAlexaff
K. Mirotchnik, K. Allsopp, A. Kantzas

Bibliographic record

VenueCanadian International Petroleum Conference · 2000
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNMR spectroscopy and applications
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsRelaxometryOil fieldField (mathematics)Petroleum engineeringMaterials scienceChromatographyChemistryGeologyMathematicsMagnetic resonance imaging

Abstract

fetched live from OpenAlex

Abstract During production operations in heavy oil and bitumen formations where thermal recovery methods are applied, the fluids produced are often in the form of emulsions. This is also true in non-thermal recovery methods whenever oil and water are co-produced, but to a lower degree of severity. Conventional flow measuring devices are capable of measuring oil and water streams when they are segregated but they fail when oil-in-water or water-in-oil emulsions form. Conventional methods are also not reliable when there are solids flowing in the stream. Low field NMR relaxometry was successfully tested as a tool for accurately measuring the oil and water content of such streams with and without emulsions present in the samples. The method was proved to be at least as good as conventional extraction methods (i.e., Dean-Stark). The technology was tested with both artificially and naturally occurring emulsified streams with accuracy better than 96 percent. This extremely encouraging result lead to the design of an on-line NMR relaxometer for oil/water stream measurements under the conditions encountered in the production of heavy oil and bitumen. Introduction Low Field Nuclear Magnetic Resonance (NMR) relaxometry techniques were developed in the laboratory to enhance and support comparable NMR logging tools that are currently used downhole. Low field NMR relaxometry has shown that discrimination of water and oil saturation in core and ore can be easily determined. In such cases the NMR can detect the total water weight fraction and the total oil weight fraction, the viscosity of the oil, the amount of bound or mobile water and the amount of mobile or bound oil1–6. Since these measurements are done in-situ and the accuracy of the tools is at least as good as that of conventional extraction methods (i.e., Dean-Stark) NMR has emerged as a leading tool for heavy oil and bitumen characterization tool. Our research group proved that many techniques that are routinely used in conventional oil could now be extended in heavy oil and bitumen characterization1–6. This technology is now extended in the determination of oil and water content of specific hydrocarbon streams. Of particular interest are the streams that contain emulsified fluids (water-in-oil or oilin- water emulsions) which are currently very common in thermal production operations and are very difficult to handle. Test separators are currently used as the standard way of measuring the flow of thermally produced wells (CSS, SAGD, steam flooding). The test separators are inherently incapable of measuring emulsified flow. Other probe-type devices suffer from inaccuracies related to the presence of solids or gas, salinity, temperature, velocity, emulsion type, and range of cut. Low field NMR emerges as the only currently proven alternative to discriminate accurately and precisely the amount of oil and water in such streams. METHODOLOGY The NMR spectra of a mixture of heavy oil or bitumen with water consist of two sets of T2 relaxation peaks. The water peaks are at a range of 10-3000 ms, while the oil/bitumen peaks are in the range of 0.2–10 ms at the specific temperature of 30 °C.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.195
Threshold uncertainty score0.995

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)0.0060.000

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 head0.010
GPT teacher head0.275
Teacher spread0.264 · 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 designBench or experimental
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

Citations6
Published2000
Admission routes1
Has abstractyes

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